Spectroscopy on Poly-L-Lysine & Spectromicroscopy Study on Biomineralized Proteins in Jaws of Glycera Dibranchiata

Transcription

1 Spectroscopy on Poly-L-Lysine & Spectromicroscopy Study on Biomineralized Proteins in Jaws of Glycera Dibranchiata A Thesis Presented by Marc Häming to The Graduate School in Partial Fulfillment of the Requirements for the Degree of Master of Arts in Physics Stony Brook University August 2005

2 Copyright c by Marc Häming 2005

3 Stony Brook University The Graduate School Marc Häming We, the Thesis committee for the above candidate for the Master of Arts degree, hereby recommend acceptance of the Thesis. C. Jacobsen, Thesis Advisor Professor, Department of Physics and Astronomy, Stony Brook University A. Abanov Professor, Department of Physics and Astronomy, Stony Brook University T. Weinacht Professor, Department of Physics and Astronomy, Stony Brook University This Thesis is accepted by the Graduate School. Dean of the Graduate School ii

4 Abstract of the Thesis Spectroscopy on Poly-L-Lysine & Spectromicroscopy Study on Biomineralized Proteins in Jaws of Glycera Dibranchiata by Marc Häming Master of Arts in Physics Stony Brook University 2005 This work describes two independent investigations on proteins using soft x-ray spectroscopy or spectromicroscopy respectively. The first one deals with the question if there are any features in the carbon K-shell spectra of proteins resulting from their secondary structure. Therefore NEXAFS spectroscopy was applied on wet cells containing different poly-l-lysine solutions varying in the secondary structure of poly-l-lysine, its concentration in the solution and its molecular weight. The results show that with the current method for preparing wet cells it is likely to get discrepancies in cell thickness of up to 3.5 µm between different wet cells. Consequently the obtained spectra are heavily biased by this effect. The second investigation is on biomineralized proteins in the jaw of the worm Glycera Dibranchiata. NEXAFS spectromicroscopy revealed that the composition of the protein matrix is correlated with the local concentration of the mineral atacamite. iii

5 Contents List of Figures xiii List of Tables xiv Acknowledgements xv 1 Introduction Why X-rays for these Applications? Principle of X-ray-matter Interaction Refractive Index and Optical Density Optical Oscillator Strength Description of XANES and NEXAFS K-Shell Excitation and NEXAFS features Some Simple Molecules and the Building Block Principle Amino Acids, Proteins and their Secondary Structure Amino Acids v

6 1.4.2 The Structure of Polypeptides Poly-L-Lysine The Experimental Setup - The Beamline X1A in Brookhaven National Laboratory Scanning X-ray Transmission Microscope (STXM) - the basics Fresnel Zone Plate zone plate and spatial resolution A brief description of the synchrotron ring at NSLS The undulator X What the setup looks like Energy Resolution X-ray Spectroscopy on Poly-L-Lysine, What are we up to? Samples Circular Dichroism Measurements X-ray Data Conclusion X-Ray Spectromicroscopy on Jaws of Glycera Dibranchiata Goals and Principle Ideas vi

7 4.2 The Samples Data Processing Tools Principal Component Analyzes and Cluster Analyzes Curve Fitting On the Hunt for Histidine and Atacamite Processing of Carbon K-shell Data Histidine Fraction in the Protein Matrix Atacamite Concentration in the Protein Matrix Conclusion General Conclusion Bibliography A X-ray Spectroscopy on Poly-L-Lysine B X-ray Spectromicroscopy on Glycera Dibranchiata vii

8 List of Figures 1.1 Penetration depth for electron and soft x-ray radiation Photo cross section of carbon Schematic potentials and K-shell spectra of single atoms and diatomic molecules s np Rydberg series in Helium and Argon gas ISEELS K-shell spectra of Ne, NH 3, N 2 H 4 and N First order and second order orbital interaction Chemical structure of single amino acid Chemical structure of glycine List of the twenty standard amino acids Carbon K-shell spectra of the twenty standard amino acids Peptide bond Three common types of secondary structure in polypeptides Carbon K-shell spectrum of lysine viii

9 2.1 The principle of a Scanning Transmission X-Ray Microscope (STXM) Schematic of a Fresnel zone plate Schematic of an undulator Horizontal or vertical distribution of flux respectively coming from the undulator Beamline map The beamline X1A Beamline optics Reflectivity of the order sorting mirrors The order sorting aperture Energy spread for the X1A outboard branch Open wet cell Closed wet cell CD measurement for the MW α-helical poly-l-lysine solution CD measurement for the MW α-helical poly-l-lysine solution Sketch of a wet cell ix

10 3.6 Optical density of β-sheet poly-l-ysine (3.8 % solution of MW molecules) Optical density of β-sheet poly-l-lysine (1.9 % solution of 9200 MW molecules) Simulated optical density at the carbon absorption step Simulated optical density at the carbon absorption step Simulated optical density at the carbon absorption step Simulated optical density at the carbon absorption step including noise Poly-l-lysine spectrum ( MW, β-sheet) Carbon K-shell spectra of thin films of pure histidine Sketch of a longitudinal section of a typical Glycera Dibranchiata worm jaw nm thick TEM sections of different Glycera Dibranchiata jaw tips A zoomed in picture of the 200 nm thick TEM section (sample A4, #3) in the lower right of Fig A 200 nm thick TEM sections of the Glycera Dibranchiata jaw base x

11 4.6 Cluster analysis - the learning vector quantization cluster algorithm The use of angle distance measure in cluster analysis Asymmetric line shape Cluster map of the worm jaw sample A4, #3 (close to the tip) Cluster spectra (sample A4, 3, close to the tip) C K-edge spectra of the amino acids phenylalanine, tyrosine, tryptophan and histidine Fit of the yellow cluster spectrum (sample A4, #3, close to the tip) Fit of the red cluster spectrum (sample A4, #3, close to the tip) Fit of the green cluster spectrum (sample A4, #3, close to the tip) Fit of a histidine spectrum Fit of a histidine-phenylalanine spectrum A STXM image showing the optical density of the worm jaw sample A4, #3 (close to the tip) Cluster spectra (sample A4, 3, close to the tip) A.1 Wiener filtered transmission signal of a wet cell A.2 Radiation damage test xi

12 A.3 Optical density of α-helical poly-l-lysine (3.8 % solution of MW molecules) A.4 Optical density of α-helical poly-l-lysine (1.9 % solution of 9200 MW molecules) A.5 Optical density of α-helical poly-l-lysine (0.8 % solution of MW molecules) A.6 Optical density of random coil poly-l-lysine (3.2 % solution of MW molecules) B.1 STXM image of the sample A4, # B.2 Carbon K-shell spectrum from the protein matrix in the center of the sample A B.3 Cluster spectra (sample A4, #3, to the base) B.4 Fitted spectrum of the blue cluster from Fig. B.1 (sample A4, #3, to the base) B.5 Fitted spectrum of the yellow cluster from Fig. B.1 (sample A4, #3, to the base) B.6 Fitted spectrum of the green cluster from Fig. B.1 (sample A4, #3, to the base) B.7 Cluster map of the region referred to as to the base in sample A4, #3 (Fig. B.1) xii

13 B.8 Cluster map (sample A1) B.9 Fit of the blue cluster spectrum (sample A1) B.10 Fit of the red cluster spectrum (sample A1) B.11 Fit of the green cluster spectrum (sample A1) xiii

14 List of Tables 3.1 Poly-l-lysine solutions Relative difference in histidine and phenylalanine Relative difference in histidine and phenylalanine Relative difference in histidine and phenylalanine Carbon map for sample A4, #3 to the tip Atacamite concentration in the cluster presented in Fig. 4.9 (sample A4, #3 to the tip) B.1 Relative histidine and phenylalanine content in the protein matrix (sample A4, #3 to the base) B.2 Density of protein matrix and atacamite mineral (sample A4, #3, close to the base, Fig. B.7) B.3 Relative histidine and phenylalanine content in the protein matrix (sample A1) xiv

15 Acknowledgements The past year was not only interesting because of the work I was introduced to, but also because of the people I met. I really liked being part of the x-ray group in Stony Brook. Especially I like to thank Chris for all his advice and support. It s amazing how he manages traveling around the globe and taking care of the whole group at the same time. He always had time for me when I wanted to talk to him. During the last weeks he outstandingly put effort into guiding me to the end of this work. He was not only a good adviser to me but also someone who helped me quickly getting integrated into the group. I guess that s the reason why I felt pretty well at the university. THANK YOU VERY MUCH!!! I also like to thank Sue. Apart from teaching me how to use the equipment in the lab she also provided us with coffee, cookies and lunch. There are not many people one can call after midnight when all the computers in the lab are crashed. Holger is one of these extra ordinary peoples who really try to help you out of everything. He and Ben gave me some insight into computer systems and programming. Thank you! Without Jeffry Gillow offering me a lab place for preparing the poly-l-lysine samples and John Sutherland giving me a last minute chance for the circular dichroism measurements this investigation would not have been possible. Thank you to Manfred Böhm for organizing the financial support from the Deutsche Akademische Auslandsdienst DAAD. Finally I like to thank my house mates for an interesting year and coffee in the afternoon. Thank you for this year in Stony Brook!

16 Chapter 1 Introduction 1

17 This work comprises two more or less independent parts. First I deal with the question if we can gain insight into the secondary structure of proteins by K-shell x-ray spectroscopy and secondly I demonstrate on worm jaws of the species Glycera Dibranchiata how the combination of microscopy and spectroscopy can be used for biochemical analyses. All the measurements were obtained at National Synchrotron Light Source (NSLS) in Brookhaven National Laboratory (BNL). In this chapter I first come up with some basic principles concerning spectroscopy and properties of proteins, which I consider necessary for this investigation. This is followed by chapter 2, describing briefly the experimental setup of the beamline X1A in NSLS as far as it is necessary for understanding how measurements are done. Then, chapter 3 deals with our investigation in finding spectral features of secondary structure of proteins with poly-l-lysine as model system. Finally the analyses of the protein matrix of the worm jaws is presented in chapter Why X-rays for these Applications? The use of x-ray microscopy for imaging of biological samples originates from several issues. One reason is that this method closes the gap between visible light microscopy and electron microscopy. With a spatial resolution between 30 nm and 60 nm it is possible to obtain detailed images of biological structures like cells and organelles [1, 2, 3, 4] that can t be resolved by visible light microscope. Additionally it s possible to perform microscopy and spectroscopy at the same time, including the use of cluster analysis [5, 6] to group areas in these images by their chemical properties. I will discuss this analysis method in section and demonstrate its usage by analyzing the worm jaw samples. Comparing electron microscopy with scanning transmission x-ray microscopy (STXM) it depends on the sample which application suits better. For example it is possible to perform measurements on frozen hydrated samples with both electron microscopes and x-ray microscopes. However, for frozen hydrated samples thicker than 500 nm (such as one might expect for whole cells) x- ray microscopy offers higher resolution at reduced radiation dose [7]. Soft x-rays interact with biological samples such that photoelectric absorption is 10 4 times higher than coherent scattering (Fig. 1.2). That means that there are no blurring effects due to plural scattering compared to electron microscopy. For electron spectroscopy and microscopy the effects of inelastic and elastic scattering are comparable (Fig. 1.1). As inelastic scattering bears the information of interest for spectroscopy, the relevant signal lies on top of 2

18 Penetration distance (µm) X-ray energy (ev) Carbon Oxygen 1/µ (protein) 1/µ (water) X rays λ elastic (water) (water) Electrons (protein) λ inelastic (protein) Electron energy (kev) Figure 1.1 Penetration depth for electron and soft x-ray radiation in carbon and oxygen [8]. a large background due to plural inelastic scattering. 1.2 Principle of X-ray-matter Interaction Refractive Index and Optical Density By considering Fig. 1.2 we can see that the dominant interaction of soft x-rays with matter in the range of 100 ev 1000 ev is photoelectric absorption. All other effects like elastic scattering are small enough to be neglected. Thus we can apply the picture of an electromagnetic wave forcing electrons with mass m e and charge e to oscillate around their equilibrium position. Therefore we describe the propagation of x-rays as driven, damped harmonic oscillator. A(x) = A 0 e i n k x (1.1) 3

19 100 λ (nm) Cross section (barns=10-24 cm 2 ) 10 7 Carbon σ coh (coherent) σ ab (absorption) σ incoh (incoherent) Energy (ev) Figure 1.2 Photo cross section of carbon: Contribution of photoelectric cross section σ ab, coherent scattering σ coh and incoherent scattering σ incoh. Data from [9] and [10], reprinted by [8]. 4

20 How this leads to the complex refractive index n = 1 δ iβ = 1 r e 2 π λ2 i n i f i (0) (1.2) with refractive coefficient δ and absorption coefficient β can be found in [11]. The part on the right hand side of this equation expresses the refractive index on the atomic level, which depends on the classical electron radius r e, the wave length λ, the number of oscillators n i of type i per unit volume and the complex atomic scattering factor for forward scattering f(0) = f 1 + if 2. We see below that by measuring the absorption of materials we can determine β or f 2 respectively. This factor is linked to the atomic photoabsorption cross section by the relation f 2 = σ a 2 r e λ. (1.3) With the relation I(x) = A(x) 2 = A(x) A + (x) we obtain the Lambert-Beer law for the transmitted x-ray intensity I(x) as function of sample thickness I(x) = I 0 e µ l x, (1.4) where µ l = 4πβ is called the linear absorption coefficient. It depends on β and λ therefore on the number of atoms per unit volume according to (1.2), so that it changes with the material state (gaseous, liquid, solid). To overcome this inconvenience we define a mass absorption coefficient µ m the way that OD = µ m ρx or I(x) = I 0 e µm ρ x (1.5) with [µ m ] = cm2 g. With (1.2) in mind we know that this mass absorption coefficient is only determined by material type and wave length, but not by the density of the material. If we know the molar mass M of the material, we can then calculate its absorption cross section σ a with µ m = µ l ρ = 4 π β ρ λ = 2 r e λ N A M f 2 = N A M σ a, (1.6) where N A is Avogadro s number. Another aspect of these calculations is that if we like to compare spectra of different samples with the same optical thickness, we can also just deal with the optical density OD. 5

21 1.2.2 Optical Oscillator Strength For discussion of intensities of resonances we often refer to the optical oscillator strength f, which is related to the x-ray absorption cross section according to σ a (E) = C df de, (1.7) where C = 2 π 2 e 2 /mc. Since f is the energy integral of the cross section, the oscillator strength is a measure of the intensity of a resonance. That means for a cross section versus energy plot the area of a bound state resonance is equal to the oscillator strength. The discrete and continuum oscillator strength satisfy the Thomas-Reiche- Kuhn sum rule. It states that for a given electron in an atom or molecule the sum of the oscillator strength of all transitions to all states, discrete and continuous, is unity. Then the total oscillator strength for an excitation of an atom or molecule must be equal to the number of electrons in the atom or molecule: f n + n IP df(e) de de = N (1.8) 1.3 Description of XANES and NEXAFS Soft x-ray photons provide enough energy to transfer K-shell electrons from the ground state to unoccupied states or even ionize these atoms. That means that there is not just one step function in the respective spectra, but they contain also several peaks due to transition to various unoccupied states near the vacuum level. The width of these features is mainly determined by the lifetime of the core hole τ c. In this section I discuss briefly the principle features of single atoms and simple diatomic molecules and how one can apply this knowledge on spectra from larger molecules K-Shell Excitation and NEXAFS features One can gain some insight into the absorption processes of complex molecules and their spectra respectively by solving the Schrödinger equation for simple unperturbed atoms and comparing the results to that for a simple unperturbed 6

22 diatomic molecule. Let us start with the eigenvalue equation for single atoms: ( 2 2 m i 2 i + V ) Ψ = EΨ, (1.9) For single multi-electron atoms in a spherical coordinate system with the atom in the center, the potential V is expressed by V = i Z e 2 4πε 0 r i + i, j>i e 2 4πε 0 r i r j (1.10) and for diatomic molecules in the center-of-mass coordinate system one obtains V = i, j Z j e 2 4πε 0 r i R j + i, j>i e 2 4πε 0 r i r j + Z 1 Z 2 e 2 4πε 0 R 1 R 2. (1.11) In each case the total potential V reflects the Coulomb interactions between electron and core in the first term and the electron-electron interactions in the second term. For the calculation of electron states only these terms are of primary interest, because the third term in this molecule potential only reflects the Coulomb interaction between the different cores. This however takes place on much larger time scale than the electron interaction and could therefore be neglected in first place, in what is often referred to as Born-Oppenheimer approximation. To get a solution for (1.9), the wave function Ψ is usually separated in a radial and an angular part, thus leading to Ψ(r, θ, φ) = R(r) Y (θ, φ). (1.12) Inserting (1.12) into (1.9) for the hydrogen atom for example leads to an differential equation of: g (r) + 2m 2 [ ] E V (r) 2 l(l + 1) g(r) = 0 (1.13) 2 m r 2 This equation shows us, that the effective potential is a sum of a Coulomb and a centrifugal term depending on the quantum number for the angular momentum l. For multi-electron systems equation (1.13) would also contain an electron-electron exchange potential. For K-shell excitations of single atoms the centrifugal term plays little role, as the dipole selection rules allow only transitions with l = ±1 and for K-shell electrons it applies l = 0. For diatomic molecules, in a center-of-mass coordinate system, large l-values may 7

23 dominate the wave function because of the large distance between electrons and the molecule center. As a result electrons with high angular momentum are strongly influenced by the potential arising from the centrifugal barrier. This difference between single atoms and diatomic molecules in the potential and therefore in the energy of the respective electron states is demonstrated in Fig On the left hand side we can clearly see the 1 -like potential with r an indication of various atomic states called Rydberg states below the vacuum level and continuum states above this barrier. Consequently soft x-ray photons give rise to transitions to either continuum states or unoccupied atomic states, which are situated closer to each other the more their energy comes to the vacuum level. According to this explanation the respective x-ray absorption spectrum for single atoms should look like the graphic given in Fig One should see a step function near the energy of the 1s-shell and several absorption peaks at lower photon energies due to transitions to Rydberg states getting closer in the direction to higher energies. On the right hand side in Fig. 1.3 we find the respective plots for N 2, an example of a diatomic molecule. The centrifugal term gives rise to the indicated barrier, which separates an inner shell well from an shallower outer well. The inner one contains all core or valence orbitals, which can be either atomic Rydberg orbitals or molecular orbitals (MO). While low energy Rydberg orbitals are mainly confined by the inner well potential, higher ones with energies close to the vacuum level lie almost entirely in the outer well. The molecular orbitals are characterized by their symmetry and therefore called σ-orbitals and π-orbitals. They are usually marked with an asterisk if the respective orbital is anti bonding and therefore unoccupied in the molecular ground state. Their shape can be found in books like [12]. Resonances in K- shell spectra arise from 1s initial state to Rydberg or unfilled molecular orbital final states. For diatomic molecules the lowest unoccupied molecular orbital (LUMO) is usually a π -orbital with a σ -orbital at an energy above the vacuum level. As a transition to the last one (1s σ ) has a short lifetime, the respective peak above the continuum step is very broad compared to peaks below this threshold. According to the Heisenberg uncertainty principle the full width of the resonance is Γ = /τ. This leads to characteristic features in the molecular K-shell spectra as schematically shown in the graphic in the upper right in Fig In addition to these one-electron features the respective spectra are also influenced by effects from multi-electron transitions. Fig. 1.4 illustrates measured spectra of the 1s np Rydberg series of gaseous Helium and Argon, which agrees very well with the model described above. In Fig. 1.5 we can get an impression of how well we can compare atom spectra with respective molecule data. Analyzing the neon and the ammonia signals we 8

24 Figure 1.3 Schematic potentials (bottom) and K-shell spectra (top) of single atoms and diatomic molecules taken from [12]. The lower part indicates the potential well for single atoms including the position of Rydberg states on the left hand side and the respective pattern for diatomic molecules including Rydberg and MO states on the right hand side. The important difference between the two potentials is the fact that the centrifugal term is very important for proper description of molecule transitions. It gives rise to transitions to states above the vacuum level, resulting in spectral features above the continuum step. 9

25 can see that the spectra are principally the same, the main difference being in the intensity of the peaks and the step. This can be explained by mixing of the N-Rydberg states with the N-H σ-states on the one hand and different oscillator strength due to different numbers of electrons (see equation (1.8)) on the other hand. The same effects are reasons for some differences between the N 2 H 4 and N 2 spectra. All these spectral features around the ionization step are usually referred to as near edge x-ray absorption fine structure (NEXAFS) or x-ray absorption near-edge structure (XANES) Some Simple Molecules and the Building Block Principle The measured curves presented in Fig. 1.4 and a comparison of the He and N 2 curves in Fig. 1.5 show that the model for single atoms and diatomic molecules described in section works quite well. Therefore it is reasonable to try to apply it also on large molecules. Such a straightforward approach is the building block principle. According to this principle we consider large molecules as a sum of several small molecules which are joined by extra-molecular bonds with strength of the same order as intra-molecular bonds. This principle however only works well for molecules without conjugated bonds, which means that there should be no first order bond interaction and only weak second order bond interaction between π, π, σ and σ orbitals [12, 19]. Conjugation implies strong interaction between local bonds, resulting in energy-split and delocalized MOs [12]. A first order bond interaction means that two energetically degenerate orbitals overlap, resulting in two split levels with energies E 1 = A B and E 2 = A + B, where A is the energy of the of the degenerate levels and B is the energy shift due to the perturbation by the overlap. A second order bond interaction is referred to as the interaction of two non degenerate states with the unperturbed energies A1 > A2, with a larger difference energy A 1 A 2 >> B (Fig. 1.6). As we are interested in the C-edge spectrum of poly-l-lysine as one part of this work and our group already has measurements of the 20 most important amino acids including a possible interpretation of the data [20], I consider it reasonable to apply the building block principle in this investigation. In this sense the building blocks are the single lysine molecules which are connected to long polymer chains of amino acids. Therefore we expect to get poly-llysine spectra that are somehow similar to that of the single lysine molecule. Differences should arise from several conditions and parameters. For example the MOs around the C α must be affected by peptide bonds as it was already 10

26 Figure 1.4 Illustration of the 1s np Rydberg series in Helium (upper half) and Argon gas (lower half), merging into the ionization continuum at the 1s ionization potential (IP). The He spectrum was recorded by high resolution (FWHM 20 mev) electron energy loss spectroscopy with 25 kev electrons [13], while the Ar spectrum was obtained by x-ray absorption spectroscopy [14, 15]. The dashed curves represent a fit to the data with Lorentzians. In both spectra the line width originates rather from the final state lifetime than from instrumental broadening. Note the difference in intensity scale: logarithmic for He and linear for Ar [12, Fig. 2.1]. 11

27 Figure 1.5 ISEELS K-shell spectra of Neon (Ne) [12, 16], Ammonia (NH 3 ) [17], Hydrazine (N 2 H 4 ) [12, 17] and Nitrogen gas (N 2 ) [18], aligned to their ionization potentials. The graphic illustrates the increasing number of spectral features by introducing additional bonds. One can clearly identify the Rydberg resonance about 3 ev below the IP in the He signal. The fact that the two bound state resonances in the NH 3 signal are stronger relative to the continuum step compared to the Ne measurement can be attributed to mixing of N-H valence orbitals with Rydberg states. N 2 H 4 exhibits an additional feature close to the IP, which must originate from the 1s σ transition due to the N- N single bond. Triple bonded N 2 shows a strong sharp low energy feature and two broad and flat ones above the IP. With the explanation given in section the feature 9 ev below the IP can be identified as a π resonance and the one 9 ev above the IP as σ resonance. The broad bump 5 ev above the IP is a multi-electron resonance [12, Fig. 4.3]. 12

28 observed for other di- and trimers [21, 22, 23, 24]. Another source of perturbation in the sense of molecular orbital interaction may be the molecule geometry, which is one of the major interests in this work. The molecular geometry determines how strongly certain orbitals overlap. The energy-split of the respective spectral features should vary with changes in the geometrical structure. 1.4 Amino Acids, Proteins and their Secondary Structure Proteins are composed of amino acids, which form long chains that are somehow folded to a complex structure. Although more than hundred different amino acids have been discovered in nature, only twenty of them are encoded by the standard genetic code. Human and animal proteins are synthesized from these so called standard amino acids. The carbon K-shell spectra of these molecules were published by [20] as shown in Fig In this section I briefly discuss the chemistry of single amino acids and short proteins as far as it is necessary to understand their behavior for spectroscopy purposes Amino Acids In general, amino acids consist of one carboxyl group (-COOH), an amino group (-NH 2 ) and a more or less long side chain, labeled as R for residue (Fig. 1.7). The residue can consist of only one hydrogen atom as in the case of glycine (the simplest amino acid), but it can also be a long carbon chain including several nitrogen atoms (e.g. arginine, lysine) or oxygen atoms (e.g. aspartic, glutamic acid), or the residue can be some kind of cyclic molecule like in histidine, phenylalanine or tyrosine. Different behavior, such as hydrophilicity or hydrophobicity of the molecules, depends mainly on these residues. Lysine for example is quite soluble in water due to its amine side chain terminus. The fact that most of them are poorly soluble in water, led to investigations in preparing samples for spectroscopy with concentrated trifluouro acetic acid solution (TFA) [20, 25]. As the ionic state of amino acids depends on the ph level of solution, there was some discussion not only on the carbon background due to the TFA, but also about the perturbation of the system due to the different ionic structure compared to neutral molecules. Because in neutral solutions at ph 7, amino acids exist in zwitterionic state, where the carboxylic group is deprotonated whereas the amino group is protonated. This leaves the molecule as a whole 13

29 Figure 1.6 ([12, Fig. 3.5]) Schematic energy level diagram of two important possibilities how two orbitals can interact. On top we see how the overlap of two degenerate orbitals Φ 1 and Φ 2 with energy A leads to a lower energy bonding orbital with energy E 1 = A B and a higher energy anti bonding orbital with energy E 2 = A + B. The bottom graphic indicates the weak second order bond interaction between energetically non degenerate orbitals Φ 1 and Φ 2 with energies A 1 > A 2, including including wave functions and energies of the perturbed system. 14

30 R H H C N H C O O H Figure 1.7 Chemical structure of single amino acid with an amino group (NH 2 ), a carboxylic group (-COOH) and a residual side chain (R). being electrically neutral, but containing polar functional groups. Under conditions with high or low ph levels, amino acids are not neutral any more, but turn to anions or cations respectively. In Fig. 1.8 we can see how these changes in the geometry of the respective functional groups look like. As a consequence the respective K-shell spectrum mirrors these effects as has recently been shown [26]. Having this in mind, we expect amino acid residues also to be more or less similarly influenced by the ph level of the solution The Structure of Polypeptides As already mentioned proteins are more or less long chains of different amino acids with different levels of structure, which are determined by the amino acid sequence. Usually polypeptide chains are residues long, but for example the muscle protein titin has a length of about residues [27]. There are several levels to classify their structure, starting with the covalent structure of the molecules and ending up with macromolecular descriptions of different degrees of protein folding. If one refers to the primary structure of polypeptides, one addresses the amino acid sequence, which is their covalent structure as we usually write it down in chemical formulas. Amino acids are then connected by peptide bonds to form long molecule chains. This bond type (Fig. 1.11) is relatively rigid, because it has about 40 % double bond character due to delocalization of the lone electron pair on the nitrogen atom. This fact leads to some kind of resonance hybrid considering its spectral features [21, 23] and it also explains the dipole character of the peptide bond. In general the conformation, meaning the spatial structure of poly amino acids, indeed is strongly determined by the code in the primary structure, but 15

31 Figure 1.8 Chemical structure of glycine at room temperature: gaseous (upper left), in neutral water solution (upper right), in acidic water solution (lower left) and in basic water solution (lower right) [26]. it also depends on various other influences like hydrogen bonds, electrostatic, Van der Walls interactions, and other forces. This gives us the possibility to tune conformation by temperature, ph level of the environment, etc. As a result the same protein can appear in different shapes, which differ between twisted covalent bonds or varying molecular angles for example. Only those proteins with correct shape are biologically active however. An attempt to describe different molecule conformations on a short range is to classify them in terms of secondary structure. There are three common structures, namely α-helix, β-sheet and helix. The α-helix is a helical molecule, about 5 Å wide, with each amino acid resulting in a 100 turn and a translation of about 1.5 Å along the axis of the helix. That means one loop is about 3.5 amino acids long. The chain is tightly packed, because all amino acid residues are arranged outside the helix in a way that there is no space for other molecules in the center (such as water). As the loops are so close to each other hydrogen bonds are formed between the oxygen atom at the C α atoms and the hydrogen atom at the nitrogen atom in the adjacent loop (1.12). The formation of such helices depends on several issues like the amino acid sequence, chain length and properties of the molecule s environment. Usually only amino acids with long side chains that can protect the helix hydrogen bonds from disturbance by water molecules are good α-helix formers [29]. Glycine or proline, for example, which have very 16

32 Figure 1.9 List of the twenty standard amino acids with chemical formulas [27]. 17

33 30 valine tyrosine tryptophan 25 threonine serine proline 20 phenylalanine methionine lysine leucine 15 isoleucine histidine glycine 10 glutamine glutamic_acid cysteine 5 aspartic_acid asparagine arginine alanine ev Figure 1.10 Carbon K-shell spectra of the twenty standard amino acids recorded by [20] from TFA thin films. 18

34 Figure 1.11 Peptide bond, described as mixture of 60 % single and 40 % double bond character. Figure 1.12 Three common types of secondary structure in polypeptides: α- helix (left, [28]), parallel β-sheet (center, [27]) and antiparallel β-sheet (right, [27]). One loop in the α-helix is about 3.5 amino acids long and the helix itself is about 5 Å thick. The colors indicate carbon atoms (black), hydrogen (blue, small), nitrogen (blue, big), oxygen atoms (red) and residues (green). For β-sheets the C α atoms are about 3.5 Å apart from each other. 19

35 short residues are known as helix breakers. As forming such a structure means a loss in entropy, and there are only n 4 hydrogen bonds in an α-helix of n amino acids countering the quest for the biggest entropy, only molecules up on a certain size tend to form these helices. That suggests that if one wishes to synthesize homopolypeptide α-chains one has to use an appropriate amino acid type and long molecules. Another very stable secondary structure that occurs often in proteins is the β -sheet. In principle it consists of two or more amino acid sequences in one protein that are arranged adjacently in parallel orientation. Depending on the sequence of the single strands the structure is called a parallel or antiparallel β-sheet. The single amino acid sequences are connected to each other by hydrogen bonds between N-H groups in one strand and C=O groups in the other. Therefore the molecule gets rigid and stable. This is also a reason for the short distance between adjacent C α atoms of only 3.5 Å. The amino acid residues are arranged in such a way that many of the adjacent side chains on one side of the sheet are hydrophobic (unpolar) and many of them on the other side are hydrophilic (polar). In globular proteins the secondary structure elements are not very long. The average α-helix is 17 Å long and contains 11 residues, which corresponds to 3 turns, while β-strands usually consist of from three to ten residues. This is due to the fact that these secondary structures are interrupted by loops and turns, and so form compact assemblies called supersecondary structures, which by themselves determine the overall topology in a protein domain called tertiary structure. Considering all proteins of known structure, 89 % of residues are involved in secondary structure and about 31 % of these occur in α-helices, 28 % in β-sheets and 30 % in turns and loops [27] Poly-L-Lysine Poly-l-lysine is a homo poly amino acid that is very soluble in water. Its secondary structure in such a solution depends primarily on ph level and on temperature. For ph 5 mainly random coil form is present and for ph 11 it forms periodic secondary structures which depends on temperature. At room temperature mainly α-helical molecules should be existent, however β-sheets can then be produced by heating [30, 31, 32], because as mentioned in β- sheets are more stable than α-chains and therefore are more resistant against heat. As described in section 1.3.2, we expect that the spectrum of poly amino acids can be explained using the building block principle with single amino acids serving as building blocks. Therefore it is useful to compare poly-l-lysine 20

36 mass absorption coeff. in g/cm Energy (ev) Figure 1.13 Carbon K-shell spectrum of lysine (TFA thin film sample), recorded with 0.1 ev energy resolution [20]. The resonance at ev results from 1s π transitions in the carboxyl group, where the features above the continuum step are due transitions in TFA. spectra with the spectrum of single lysine molecules (Fig. 1.13). 21

37 Chapter 2 The Experimental Setup - The Beamline X1A in Brookhaven National Laboratory 22

38 All the x-ray experiments described in this work were performed with the Scanning Transmission X-ray Microscope (STXM) at the beamline X1A in the National Synchrotron Light Source (NSLS) in Brookhaven National Laboratory (BNL). Before we have a closer look at the experimental setup, I first would like to explain the principle of the type of STXM we used in BNL including a brief description of the synchrotron ring. 2.1 Scanning X-ray Transmission Microscope (STXM) - the basics A scanning microscope is a microscope that takes an image point-by-point. For reasons we will describe below, scanning transition x-ray microscopes are particularly useful for combining the technics of spectroscopy and microscopy into spectromicroscopy. A monochromatic, coherent x-ray beam is focused on the sample by a Fresnel zone plate, while on the other side of the sample a detector measures the transmitted flux as can be seen in Figure 2.1. Therefore the spatial resolution for thin samples depends only on the spot size of the beam on the sample. By scanning the sample pixel by pixel for a certain energy one gets an image showing different degrees of transmission or absorption respectively for this certain energy. If we perform these two-dimensional scans for different x-ray energies we get an array of two-dimensional optical density maps along the energy axis. A sequence of such images is sometimes referred to as stack. Each pixel in such a stack gives us information about the optical density as a function of the x-ray energy. That means the STXM provides us with both spectra for each pixel, and thickness maps similar to pictures we are used to from a visible light microscope Fresnel Zone Plate The refractive index for most materials is not high enough for wavelengths in the x-ray region in order to build appropriate lenses. Therefore diffractive lenses are common for these applications. In this section I first would like to introduce the working principle of a zone plate and afterwards discuss its resolution to give the reader an explanation how we determined these parameters for our setup in BNL. 23

39 STXM Scan sample Undulator and monochromat Objective Zone plate 1D detector Figure 2.1 The principle of a Scanning Transmission X-Ray Microscope (STXM). The undulator in combination with the spherical grating mirror serve as source for monochromatic and coherent x-rays. The Fresnel zone plate focuses the x-ray beam onto the sample, while on the other side a onedimensional detector measures the transmitted flux. Zone plate basics Actual Fresnel zone plates are good lenses for focusing x-rays with relatively high resolution and focusing power. These circular gratings consist of alternating opaque and transparent zones with decreasing zone widths for greater radius. Today s zone plates are usually made of Gold or Nickel plated on a Si 3 N 4 membrane with 250 up to zones, a range in thickness from 100 nm nm and outer most zone widths δ N from 35 nm to 150 nm. The diameter of condensors is about 4000 µm, while a diameter of 200 µm is usual for objectives. The rings are arranged the way that the path to the focal point differs by one wave length λ between between two adjacent transparent zones and the transmitted radiation interferes constructively at the focal point. These two constraints lead to the following equations: 2r N δ rn = mλf (2.1) r 2 n = (f sinα n ) 2 + (mn λ 2 )2 (2.2) r 2 n = mnλf + m 2 n 2 λ2 4, (2.3) 24

40 Figure 2.2 Schematic of a Fresnel zone plate showing alternating opaque and transparent rings. The radius and width of each ring is chosen such that the rings diffract incident radiation to form a focus spot. where r n is the radius of the nth zone, m is the mth diffraction order and f is the focal length as shown in Fig According to equation (2.1) the focal length of a zone plate is determined by its radius, its outer most zone width and the wavelength. The second term of equation (2.3) corrects for spherical aberration. As Fresnel zone plates are usually thin, the opaque zones can t block the x-rays completely. The efficiency of these lenses can be improved by choosing a thickness such that the phase of the radiation traveling through opaque rings is shifted by π in comparison to the radiation going through transparent zones. Then the phase shifted radiation interferes constructively at the focus and contributes to an increase in efficiency zone plate and spatial resolution According to [33] zone plates can be treated as normal thin lenses if they consist of more than 100 zones. This is always the case for today s zone plates. That means the transverse resolution can be determined by the Rayleigh criterion for a circular aperture by using the approximation that tan φ n φ n for small 25

41 angles: sin φ N = 1.22 λ 2r N (2.4) δ t f sin φ N = 1.22 δ rn (2.5) where r n is the radius of the nth zone and δ rn its width. If we apply the Rayleigh criterion again we obtain for the longitudinal resolution or depth of focus: δ l = 4.88 δ2 rn (2.6) λ We get the maximum resolution if the sample is not thicker than this value. Our zone plate we are actually using at X1a outboard has a diameter of 160 µm and an outermost zone width of 45 nm. So we calculate the transverse resolution to δ t = 55 nm and the longitudinal resolution to be δ l = 2.4 µm for a photon energy of 300 ev. 2.2 A brief description of the synchrotron ring at NSLS In 1978 the construction of a second generation synchrotron ring with a storage ring of m circumference at Brookhaven National Laboratories began. Its double bend achromat lattice provides a low emittance (product of beam size and divergence) and hence a light source with high brightness. Finally in 1984 operation started. Since then the facility went through several minor and major upgrades. It now runs at a normal operating current of around 280 ma after a refill, which takes place twice a day. There are 25 electron bunches circling around with a natural bunch length of 7.8 cm and an electron orbital period of nsec. The normal operation energy is 2.8 GeV with a normal energy spread of σ e /E = [34]. 2.3 The undulator X1 For applications like the STXM it is important to have an appropriate source of x-ray radiation. There are several constraints such sources must fulfill. The use of diffraction optics and spectroscopy applications require a collimated, coherent and monochromatic x-ray beam. According to [35, 36, 37] we therefore 26

42 S N S N S N S N S N Figure 2.3 This schematic of an undulator indicates how the electron beam is deflected by an alternating magnetic structure, leading to a narrow radiation cone of monochromatic x-ray beam. can only accept a tiny volume in phase space x x y y λ 2, (2.7) with the spatial extend x(y), the angular extent x (y ) and the wavelength λ. On the other hand the fact that we use a zone plate as focusing device means that the monochromaticity of the beam must be greater than the total number of zones N multiplied with the diffraction order m [38, 39]: λ mn (2.8) λ In section was already mentioned that typical zone plates in use at X1A have more than 500 zones. For spectroscopy purposes, however, a monochromaticity of more than 2500 is preferable. There is one parameter which gives a measure of how well both the phase space and the monochromaticity requirements are fulfilled: the source brightness which is defined as Φ brightness = A Ω 0.01%, (2.9) where Φis the photon flux with 0.1% bandwidth E/E which is emitted from the source area A into the solid angle Ω. The spectral bandwidth is defined as the ratio of the considered energy range E and the photon energy E. By measuring the flux density in phase space, brightness is an invariant quantity, so it is only determined by the radiation source and no linear optics can improve it. That s why it is important to have an appropriate x-ray source in order to be able to perform x-ray microscopy. In principal an undulator like X1 in Brookhaven is a device that is made of 27

43 Spatial distribution of the X1 undulator output Flux in arbitrary units x, y in arbitrary units Figure 2.4 Graph of the horizontal or vertical distribution of flux respectively coming from the undulator. The left area indicates the portion taken by X1B and the right two thin areas represent the portions for X1A inboard and outboard, which are each about 10 % of the total output. The width of the x-ray beam or beam emittance (depending on which is the common measure) is 3 λ in vertical and 100 λ in horizontal direction [42]. alternating magnetic poles. Electrons flying through this structure at nearly the speed of light experience an alternating magnetic field with the respective Lorentz force and are therefore follow an undulatory path as represented in Fig Radiation from different periods interferes coherently, thus producing sharp peaks at harmonics of the fundamental [40]. Because of the reason described in (Fig. 2.4) the two X1 beamlines use only a fraction of the undulator output. An exact description including dimensions and parameters for the undulator X1 in Brookhaven National Laboratory can be found in [41]. 2.4 What the setup looks like At the beginning of this chapter I described an ideal STXM. In practice the STXM at the beamline X1A in BNL is a bit more complicated. One reason is that the undulator is not fully coherent. Therefore we must filter out one mode each for the beamline branches X1A1 and X1A2, while the adjacent beamline X1B does not need coherence. The X1 undulator provides three beamlines with 28

44 X1A M0 X1A M2 (toroid) X1A M1 (toroid) X1A1 SGM X1 Undulator X1A2 SGM Figure 2.5 Schematic showing how the flux for the two beamline branches X1A outboard and inboard is split off from the from the beam that feeds the adjacent beamline X1B. Each X1A branch gets about 10 % of the total undulator output. Due to the fact that the plane scraping mirror M0 is made of Ni and the toroidal mirrors X1A1 M1 and X1A2 M1 are Au coated, photons with energies higher than 2 kev are not delivered to the experimental endstation. flux, namely X1A1, X1A2 and X1B. The setup for the two X1A branches is principally the same with the difference that the outboard branch is optimized for the carbon edge and the inboard branch can be optimized for either the oxygen or the nitrogen edge. The nomenclature inboard and outboard refers to the position of the two branches if one looks in the downstream direction. The one lying closer to the synchrotron ring is referred to as inboard while the other one is called the outboard branch. In the following paragraph I will focus on the outboard branch. Fig. 2.5 and Figure 2.6 describe the way the x-ray radiation produced by the X1 undulator travels through the setup until it hits the detector. When 29

45 the x-ray beam leaves the undulator, 35% of the central cone is intercepted by the water-cooled plane scraping mirror M0 and directed to the two X1A beamline branches, as seen in Figure 2.5. The angle of incidence on this Nicoated mirror is approximately 40 mrad, leading to nearly entire absorption of radiation above 2 kev. While the undeflected beam feeds the beamline X1B the reflected part is collimated by a water-cooled Cu mask. The X1A M1 toroidal mirror (made of gold-coated single-crystal silicon blanks) splits off 50% of the beam for the outboard branch and focuses it horizontally onto the entrance slit and vertically onto the exit slit of the outboard Spherical Grating Mirror called X1A1 SGM. The importance of the slits for the energy resolution of the beamline is discussed in section 2.5. The horizontally dispersing SGM is manufactured from single-crystal silicon blanks with gold coating and a groove density of 892 lines per mm. The instrument operates in positive first diffraction order. As I already mentioned, the scraping mirror M0 reflects x-rays with energies up to 2 kev and therefore the problem arises that the beam hitting the exit slit is composed of of the first diffraction order of the fundamental (eg. 300 ev at the C-edge) and higher diffraction orders of higher undulator harmonics. As we are only interested in the fundamental for good NEXAFS spectra with a low background radiation, Order Sorting Mirrors (OSM) are installed between the SGM and the exit slit. They are adjusted with an angle of incidence such that higher harmonics with λ n = 1 λ n 0 are absorbed (Fig. 2.8). Basically one makes use of the angle dependence and energy dependence of the specular reflectivity for these gracing incidence mirrors. Two parallel mirrors are installed for the sake of higher efficiency on the one hand and for getting a parallel displacement of the x-ray beam on the other hand. That makes it easier to align the beamline. How the OSM work in detail, however, can be found in [43, 40]. The zone plate works like a circular grating that focuses the first diffraction order of the beam on a spot on the sample. Without any other equipment the sample would be also radiated by other diffraction orders, which are not focused on the sample leading to a large radiated area with high background signal and lots of noise. For example the zero diffraction order would radiate a circular area of the size of the zone plate. In order to get rid of these side effects a pinhole called Order Sorting Aperture (OSA) is placed between the lens and the sample, and a Central Stop is installed in the center of the zone plate. So for a certain wave length the OSA is adjusted the way that all other diffraction orders from the zone plate are blocked, except of the first one, which is focused on the sample. (see Fig. 2.9). A central stop, as its name already suggests, is a disk, placed in the center of the lens in order to stop the central part of the x-ray beam, which otherwise would just travel directly through the 30

46 Fresnel zone plate and hit the sample without being focused. A detailed description and further specifications of the beamline X1A can be found in [42]. 2.5 Energy Resolution The energy resolution of the beamline X1A is determined by the position of the entrance and exit slits relatively to the SGM. For a traditional spherical grating the distances must satisfy the Rowland condition, what means that the position of the exit slit must be adjusted as the grating rotates in order to keep the exit slit at the proper focal distance. As the energy region for NEXAFS scans at the C, N and O edge is very narrow, the defocus aberration is very small. That means good resolution can be obtained even with a fixed exit slit position. Here only the illuminated area on the grating that contributes to the illumination of the zone plate is of interest for the calculation. With the current monochromator layout of the beamline X1A it is possible to determine the energy resolution primarily by adjusting the entrance slit size as long as the exit slit is smaller than the entrance slit. This effect can be explained as followed: The entrance slit restricts the size of the beam that hits the monochromator. The SGM does a Fourier transformation into energy space, meaning that the spacial coherence due to the entrance slit is equivalent to the energy resolution of the grating. The exit slit can be considered as a spot source that illuminates the zone plate and therefore effects the spatial resolution of the facility [44]. Fig shows the energy resolution for different slit settings for the X1A outboard branch with an 80 µm-diameter zone plate at 1.2 m from the exit slit with coherence parameter p = The calculation is performed with the program sgm delta ev.pro as described in [42]. 31

47 2.8 GeV electrons National Synchrotron Light Source X-ray Ring Specimen Zone plate Monochromator Soft x rays X1 undulator Detector Order sorting aperture Figure 2.6 Sketch of the beamline X1A indicating the electron storage ring, the photon source and some of the beamline optics. It shows the X1 undulator, the entrance slit to the monochromator and the exit slit. The Fresnel lens focuses the beam onto the sample. Due to the OSA diffraction orders higher than 1 are filtered out. Note that the mirrors X1A M0 and X1A1 M1 are placed between undulator and entrance slit and the oder sorting mirrors are installed between the monochromator grating and the exit slit. 32

48 vertical plain zone plate toroid undulator grating horizontal plain Figure 2.7 Schematic of the optical path of the x-rays when they travel through the setup. Note the difference between the horizontal and the vertical. The optics for both branches, inboard and outboard, are similar. % reflectivity (2 bounces) nm nm 4.0 nm 3.5 nm 20 mrad 30 mrad 40 mrad 50 mrad 60 mrad 70 mrad Order sorting mirror 3.0 nm 2.5 nm 2.0 nm ev Figure 2.8 Calculated reflectivity for the order sorting mirrors (SiO 2 ). At each singlebounce angle (in mrad), the reflectivity for two bounces is shown. 33

49 0 order ordersorting aperture (OSA) Incident beam Zone plate +3. order +1 order focal point Figure 2.9 Drawing of the order sorting aperture, which is a pinhole installed downstream from the zone plate in order to prevent the sample being radiated by 0 order or third and higher order radiation. As the zone plate is not thick enough to stop all the 0 order radiation in the center a 70 µm thick disk called central stop is fabricated on the zone plate using a lift-off process. In this sketch this feature is only indicated by the solid line in the zone plate center. 34

50 ZP dia=160 m, dist=1.20 m, p= E (ev) Energy (ev) Figure 2.10 Calculated energy spread for the X1A outboard branch SGM. The numbers in the graphics indicate the respective entrance slit position. The calculation is done for a zone plate diameter of 160 µm, a zone plate position of 1.20 m downstream from the exit slit and a coherence parameter of p =

51 Chapter 3 X-ray Spectroscopy on Poly-L-Lysine, 36

52 This chapter deals with the question if there are any features in the carbon K-shell spectrum allowing to determine the secondary structure of proteins. Up to now, we know only about analytical investigations on amino acids in the range from single amino acids [20] to short peptides on the one hand and large proteins like fibrinogen on the other hand. In section I mentioned that about 89 % of the proteins are characterized by a certain secondary structure. So if we are able to determine this feature by carbon and nitrogen NEXAFS spectroscopy we can make use of another source of information for determining types of proteins. Because yet we can only find out the major amino acid component in complex proteins, but not its complete set for reasons I pointed out in section 1.3. That means that if we don t know what kind of protein a sample contains we actually can only narrow down the selection of proteins that are considered to be on hand by comparing the main amino acid component. As certain polypeptides however appear only in certain secondary structures depending on their function, we can further isolate the choice of good candidates. 3.1 What are we up to? In general the C 1s πc=o transition of the carboxyl group in amino acids is very sensitive to the electronegativitiy of the carbon neighbors [45]. Recent investigations [26] showed that this resonance at ev is 0.15 ev red shifted if the carboxyl term is deprotonated. The carbon atom adjacent to the amine group in contrast is less sensitive to changes in the electronic state of this term. Indeed at the carbon K- edge no shift in position could be measured for the transition to mixed σ -Rydberg state between neutral and protonated amine terminus, but this peak around 290 ev becomes less broad and intenser if this functional group is protonated. However significant differences in the spectrum between neutral and protonated state could be observed at the nitrogen K-edge [26]. For the neutral amine group the nitrogen edge is 1.3 ev red shifted to 406 ev compared to the charged state and the spectrum exhibits two sharp pre-edge resonances at ev and ev due to N 1s σ transitions. Consequently these transitions at the carbon and nitrogen edge should be affected by hydrogen bond between carbonyl and amine group because of contribution of electron density to this interaction. As described in section poly amino acid α-helices and β-sheets are mainly determined by hydrogen bonds. Therefore we should be able to find differences in carbon and nitrogen K-shell spectra between distinct secondary structure and random coil structure. Another point we are interested in, is 37

53 the question if these features are expressed differently for α helices and β- sheets, what generally would allow us to distinguish between these structures by their NEXAFS spectrum. In order to get information about how these effects depend on molecule length or protein size respectively we observed poly peptides of different molecular weight. Additionally the spectra should contain as less features as possible allowing clear and explicit assignment of the respective transitions. Consequently we need homo poly amino acids consisting of simple amino acid types from the point of spectroscopy. Our search for an appropriate model system terminated in the choice for poly-l-lysine. The behavior of this molecule in aqueous solution is well known and its secondary structure can be tuned easily by adjusting the ph level. Poly lysine served as model system for generating optical rotatory and circular dichroism reference data from 190 nm to 250 nm wave length, which is generally used for determining secondary structure of proteins [30, 31]. Another reason for this choice is that [30, 31] demonstrated that this molecule is unaffected by salt in the solution. So if there are impurities in the specimen despite of cautious handling during the preparation process, they have no effect on the molecule structure. The effect on primary structure due to changes in the ph level on the carbon spectrum of this molecule should be very small, as for high ph only the final carboxyl group (COOH) at the chain end will be turned into the anionic state (COO ) and for long molecule chains the effect of this element is negligible. So poly-l-lysine is a good system to determine spectral effects due to secondary structure. 3.2 Samples The poly-l-lysine samples were produced as described in [30, 31]. We prepared different solutions of poly-l-lysine in water, varying in molecule length and secondary structure. The homo poly amino acid we used for the mixtures was obtained from Sigma Aldrich in dry form as poly-l-lysine hydrobromide, with high purity. We used powder of poly-l-lysine bromide with molecular weight MW = , 9200 and without further purification. The secondary structure was tuned by ph level and temperature of the sample. As it is necessary to get thick solutions for x-ray spectroscopy we solved about 50 mg powder in 3 ml of distilled water at 22 C room temperature and adjusted the ph level by adding NaOH or HCl. In order to determine the ph level correctly without adding any contamination by carbon containing chemicals we used an ph-meter with 0.01 accuracy. During the mixing the solution was constantly stirred to get an uniform ph level in the whole solution. 38

54 sample concentration ph-level heated α-helix, MW % α-helix, MW % α-helix, MW % β-sheet, MW % x β-sheet, MW % x β-sheet, MW % x random coil, MW % Table 3.1 This table shows the ph-level and concentration for the different poly-l-lysine solutions we prepared in weight percentage. β-structure samples were fabricated by heating some of the respective α-helix solution to 50 C in a water bath for 25 minutes. The error in concentration results mainly from the fact that the dry poly-llysine quickly soaked up water, therefore became sticky and probably not all powder fell into solution. Another reason is that weighing was done without bubbler and so some solution was taken out with the device before the final weight was determined. With respect to [30] we expected to get random coil structure for ph and α-helix for ph. β-sheet structure was obtained by heating up some of the α-helix solution in a 50 C hot water bath for 25 minutes and cooling it down to room temperature afterwards. The NEXAFS scans of these solutions were performed 12 hours after the preparation of the solution. Therefore we gave two 12 µl - drops on the flat side of a Si 3 N 4 -window and squeezed a second Si 3 N 4 -window on the first one. For getting equally distributed pressure in the windows they were glued onto circular window holders which were pressed together by three screws. So we got a thin solution layer between the flat sides of the windows with equal thickness. Fig show the used wet cell frame and windows for different stages of preparation. We performed measurements on these wet cells within less than 25 minutes, because we could see under the visible light microscope, that after about 40 minutes the solution dried out and clusters or regions with lots of Newton fringes formed. The described procedure should minimize penetration of the samples during the preparation process. We stored these solutions in a refrigerator at about 8 C until we used them for Circular Dichroism measurements, which were performed seven days after the NEXAFS spectroscopy. For these investigations we prepared the samples 39

55 Figure 3.1 The wet cell we used for x-ray spectroscopy. One Si 3 N 4 -window was glued on each, the cover and the bottom metal plate, with the flat side of the window facing up. Two 12 µl drops were given on the on the upper window before the cell plates were screwed together. 40

56 Figure 3.2 This photo gives an imagination of how the closed wet cell looks like. We used three screws to obtain equally distributed pressure on the two windows. This should minimize the variance of the thickness or the optical path length respectively in the specimen. 41

57 after the same principle as described above. Apart from the fact that we used cell types with quartz windows which absorb little for wavelength bigger than 160 nm, we went through the same procedures. Two drops of solution were given on a flat window and another window with the flat side pointing to the solution was pressed onto the first one. By doing so we again got a flat thin layer of solution. As we could not determine the thickness of these samples accurately, we also prepared a series of samples with spacers between the two windows leading to a well defined sample thickness. The spacers thickness was determined by J. Trunk, Biology Department at BNL via interference fringes to a value of µm. 3.3 Circular Dichroism Measurements For each sample circular dichroism spectra were determined at the UV synchrotron beamline U11 in Brookhaven National Laboratory. That means the difference in absorption coefficients A = A l A r for left and right circular polarized light was measured over wave lengths from 250 nm to 170 nm in 1 nm steps with a dwell time of 1 ms. At the same time the pseudo absorption A pseudo = A(λ) A(λ max ), meaning the absorption normalized to the absorption for the greates wave length, was recorded. As described in [46] this curve gives a measure for the reliability of the circular dichroism signal, because for high absorption of the sample it represents only noise. For these measurements usually some µl of the solution of interest are given in cells with a determined thickness and then the respective spectra are recorded. With knowledge of optical path length and concentration the optical activity can be calculated. It turned out however, that the solutions we prepared were to thick for performing these measurements with spacer containing cells, because important features below 200 nm wave length could not be recorded as result of too high absorption. So we prepared samples with a vacuum cell and the spacers turned outside. Consequently the optical path length in the solution was very short what led to proper signals, but also to unknown thickness of the respective cells. So it is necessary that one performs reference measurements with the 14.4 µm cell that exhibit enough spectral features allowing to normalize spectra from cells with unknown thickness to these data. Comparing Fig. 3.3 and Fig. 3.4 reveals that the concentration of the MW poly-l-lysine solution, which is about 3.8 % is near the upper limit for these measurements. For the specimen prepared with the 14.4 µm cell namely the dip at around 200 nm in the circular dichroism signal is not 42

58 Circular Dichroism Circular Dichroism Pseudo absorption Pseudo Absorption Wavelength in nm Figure 3.3 Circular Dichroism measurement for MW α-helical solution with 3.8 % concentration in a 14 µm thick cell. The circular dichroism signal gets very noisy for big slope of the pseudo absorption signal. A comparison with Fig. 3.4 reveals that for wavelengths lower than 202 nm the curve does not represent properties of the solved molecules. fully represented because of the strong rise in absoption towards shorter wave lengths. For much higher concentrations this feature will vanish completely in spectra recorded with this cell. Therefore the concentration of poly-l-lysine solutions should not be higher than 4.5% by mass. 3.4 X-ray Data For each solution, the specimen was prepared as described in section 3.2. Wet cells were mounted the way that the distance between order sorting aperture and sample was only about 0.5 mm and specimen were in the focus of the visible light microscope, which can determined by ± 50 µm. So the loss in flux due to absorption in surrounding gas is minimized. Yet, only data at the carbon edge were taken by measuring the transmitted flux over photon energies from ev to ev in steps of 0.1 ev with a dwell time of 120 msec. We did this five times for each specimen and averaged over the measured signals in order to reduce noise in the spectrum. During the measurements we 43

59 Circular Dichroism Circular Dichroism 2 1 Pseudo absorption Pseudo Absorption Wavelength in nm Figure 3.4 Circular Dichroism measurement for MW α-helical solution with 3.8 % concentration in vacuum cell. The pseudo absorption is lower than in Fig. 3.3 due to the thinner cell type and so the circular dichroism signal shows true molecule features down to 180 nm. 44

60 tested if the recorded spectra are biased by radiation damage by irradiating the specimen for one minute with photons of a certain energy and recorded the transmitted flux. This was done for ev, ev, ev and ev photon energy with a dwell time of 5.0 ms (appendix, Fig. A.2). As for all four energies there was no decrease in transmission detected over time we conclude that the radiation dose was below the critical dose of J/kg [47]. We considered this procedure as necessary because by radiating specimen with photon energies comparable to the continuum step breaking of molecular bonds may be caused, leading to broadening and decrease in intensity of the respective resonances. Additionally according to [48] we can estimate the radiation dose using the inverse absorption length µ: dose = number of absorbed photons µ radiated area density of the material In order to get the radiation dose in Gray we can write equation (3.1) as dose = E N t µ A ρ (3.1) Gr, (3.2) where N is the number of absorbed photons per msec with the photon energy E in ev and t is the exposure time in msec. A represents the size of the radiated area in µm 2 and ρ is the poly-l-lysine density in g/cm 3. The 1/e absorption length can be calculated using the atomic scattering factor f 2 from the Henke data (equations 1.3, 1.6): µ l = m A 2 r e λ N A ρ f 2 (3.3) with r e = m, atomic weight m A and Avogadro number N A. For ev we calculate for poly-l-lysine µ l = µm 1. With the data from the MW β-sheet solution (Fig. 3.6) we can calculate N as following. The heights of the ev resonance compared to the spectrum at 280 ev is about 0.9. So the optical density of carbon at ev is less than 1.1. For an incident flux of 280 khz we then calculate the number of absorbed photons per msec as N = I 0 (1 e OD ) = 280 khz (1 e 1.1 ) = khz. (3.4) With a diameter of the focus spot that is greater than 0.5 µm and 60 sec exposure time, 1.35 g/cm 3 density and ev photon energy we calculate the radiation dose to J/kg. That means the radiation dose is one 45

61 order of magnitude lower than the critical dose for biological samples. The reason for the low transmission flux noticeable in all measurements must originate from the fact that wet cells are relatively thick compared to other samples like thin films or microtomes. Their thickness ranges in the µm scale, because the two Si 3 N 4 -windows are together 0.2 µm thick and the solution layer contributes between µm to this factor, as described below. So the absorption is pretty high resulting in a low signal to noise ratio. This means averaging over five measurements might not be enough to cut down the respective fluctuations in the optical density. Therefore we first Fourier transformed each signal, applied a Wiener filter on it and then transformed it back before calculating the optical density. Comparing the filtered signal with the original one assures that only noise is removed and the signal s real qualities like the little bump at ev are conserved (Fig. A.1). Then the optical density corrected by the ring current was calculated as following. The idea is to prepare two similar wet cells, one containing the protein solution of interest and the other containing pure water as reference (Fig. 3.5). We calculate the transmitted flux for the pure water wet cell as I w = I 0 e (µ wt 1 +µ 0 t 0 ), (3.5) where I 0 represents the incident photon flux, µ w is the absorption coefficient of water and µ 0 represents the absorption coefficient of the Si 3 N 4 -windows with the overall thickness t 0. The optical density of the water wet cell is then found to be OD w = ln I w I 0 = µ w t 1 + µ 0 t 0. (3.6) The transmitted flux and the optical density for the wet cell containing protein solution can be determined analogously. I p = I 0 e (µwtw+µptp+µ 0t 0 ) (3.7) OD p = ln I p I 0 = µ w t w + µ p t p + µ 0 t 0 (3.8) Here µ w is the absorption coefficient of pure water, µ p represents the absorption coefficient of pure protein and t p is the optical thickness of the protein in the solution. Its relation to the cell thickness t 2 is t p = protein concentration t 2. For the optical thickness of water t w we then get t w = (1 protein concentration) t 2. For the following we can assume that 46

62 the thickness of the Si 3 N 4 -windows is for all wet cells the same. If the water layers in the two wet cells are of the same thickness, the difference in optical density between the water wet cell and the protein wet cell should be due to the poly-l-lysine in the solution. OD = OD p OD w = ln I w I p = µ w t w + µ p t p µ w t 1 (3.9) That means we can calculate the optical density of poly-l-lysine from the intensities of the respective transmissions. However before we do so the recorded intensities have to be corrected by the ring current, because the photon flux is proportional to the number of electrons circling around in the storage ring. As the ring current undergoes an exponential decay over time, we have to normalize the intensities to the ring current. Then the optical density can be determined by correcting equation (3.9) for this effect as ( ) Iw i p OD = ln, (3.10) I p i w where I w represents the transmitted flux through the reference wet cell containing pure water recorded at the ring current i w, and I p is the transmitted flux for a wet cell with protein solution recorded at the ring current i p (see plots in Fig. 3.6, Fig. 3.7 and in the appendix Fig. A.3 - Fig. A.6). At first glance some spectra are very noisy; this can be partially attributed to low signal with the respective poor signal to noise ratio as already discussed above. Another issue is that the spectra differ in shape and big discrepancies in scale are visible. For example in the spectrum of MW β-sheet solution there is an offset of 0.4 with values ranging up to nearly 1.3 in optical density, where the 9200 MW β-sheet specimen has a negative offset near 0.03 and a maximum of The reason for these facts can be found by considering the right part of equation (3.9): OD = µ w t w + µ p t p µ w t 1 ;, (3.11) with the optical thickness of the water layer in the reference wet cell t 1, the optical thickness of protein t p and the optical thickness t w of water in the protein solution. If the thickness of the water layer t w in the solution cell is of the same order as t 1 in the reference cell the contribution of µ w to the optical density OD is negligible compared to µ p. One thing we conclude from (3.11) is that the variation in the difference MAX(OD) MIN(OD) must result from variation in the optical thickness of ploy-l-lysine between different samples. This can be due to discrepancies in concentration between the respective solutions on the 47

63 Si 3 N 4 I w I 0 t 1 I p t 2 = t w + t p Figure 3.5 Sketch of the water wet cell (top) and protein wet cell (bottom). The variables refer to the calculations leading to equation (3.9). 48

64 Poly-L-Lysine, ß-sheet, MW OD Energy (ev) Figure 3.6 Optical density of β-sheet poly-l-ysine (3.8 % solution of MW molecules), calculated from the transmitted flux with equation (3.11). The relative large offset in optical density at 280 ev indicates an discrepancy in thickness between the pure water wet cell and the wet cell containing protein solution. Here the water wet cell is thinner than the protein wet cell. 49

65 Poly-L-Lysine, beta-sheet, 9200MW, 1.9% OD Energy (ev) Figure 3.7 Optical density of β-sheet poly-l-lysine (1.9 % solution of 9200 MW molecules), calculated from the transmitted flux with equation (3.11). The negative optical density seems to be unphysical. However it can be explained with the fact that in this case the pure water wet cell is thicker than the cell containing the protein solution. 50

66 one hand and a result of differences in thickness t 2 of the poly-l-lysine solution layer on the other hand. This explains why MAX(OD) MIN(OD) = 0.7 for the MW β-sheet specimen, where in the 9200 MW β-sheet sample this difference is only about From Tab. 3.1 we know that the protein concentration in the MW solution is twice as high as in the 9200 MW sample. This means that the water layer in the long peptide wet cell must be about half as thick as in the other one. Another implication of equation (3.11) is that the offsets in the spectra result from a variance in optical thickness of the water layers in the protein solution cells compared to the reference cell, because if these layers are equal the optical density depends only on the optical thickness of protein. Then the optical density should be near zero at 280 ev with respect to the spectrum of single lysine molecules [20], with the assumption that the protein thickness is of the order of 10 nm. This goes aside with the previous conclusion that the differences in the spectra of the MW β-sheet and 9200 MW β-sheet sample result from both, discrepancies in the protein solution and the thickness of the water layer. Fig Fig present a simulation of the optical density calculated with equation (3.11). Different measures were obtains by fixing the thickness t 1 = 2.0 µm of the reference wet cell, which contains only a plain water layer and varying the thickness of the protein solution layer. The values for the absorption coefficients µ w and µ p are approximated with the Henke data for single atoms and therefore don t exhibit any NEXAFS features, but only the absorption step. The values for the thickness t 2 of the protein solution are 0.5 µm, 1.5 µm, 2.5 µm and 3.5 µm. For all concentrations of protein the system exhibits the same tendencies: If the the thickness t of the protein containing wet cell is much lower than t 1 of the pure water cell we get negative optical densities and the hight of the absorption step is very low. In case that t 2 is much higher than t 1 we get a positive offset. The plots show, that only for the poly-l-lysine solution with 3.8 % by mass concentration one gets an absorption step of about 0.3, what is already the lower limit for a reasonable measurements. Fig is the result of a simulation of wet cell spectra with 3.5 % by mass poly-l-lysine solutions (thicknesses of 0.75 µm, 2.25 µm, 3.75 µm and 5.25 µm) with a 3.00 µm thick pure water cell for reference. Noise was created by Poisson statistics for 250 khz input flux resulting in 50 khz transmitted flux and dwell time was set to 120 msec. This simulation indicates that for a lysine cell thickness of 3.00 µm the absorption step hight is 0.4 what could be considered as lower limit with respect to the simulated noise. The discrepancy between the thickness of the water layer and the lysine solution layer should not be greater than 0.25 µm for getting acceptable spectra. The single calculation 51

67 Wet Cell Simulation OD Energy in ev Figure 3.8 Simulated optical density at the carbon absorption step without NEXAFS features using equation (3.11) and the Henke data for µ w and µ p. The calculation is done for a t 1 = 2.0 µm thick water wet cell and a 3.8 % by mass protein solution with wet cell thicknesses t 2 of 0.5 µm (solid, black), 1.5 µm (dashed, red), 2.5 µm (dashdotted, green) and 3.5 µm (dashed double dotted, blue). Program: sim wc.pro (see appendix) 52

68 Wet Cell Simulation OD Energy in ev Figure 3.9 Simulated optical density at the carbon absorption step without NEXAFS features using equation (3.11) and the Henke data for µ w and µ p. The calculation is done for a t 1 = 2.0 µm thick water wet cell and a 1.9 % by mass protein solution with wet cell thicknesses t 2 of 0.5 µm (solid, black), 1.5 µm (dashed, red), 2.5 µm (dashdotted, green) and 3.5 µm (dashed double dotted, blue). Program: sim wc.pro (see appendix) 53

69 1.0 Wet Cell Simulation 0.5 OD Energy in ev Figure 3.10 Simulated optical density at the carbon absorption step without NEXAFS features using equation (3.11) and the Henke data for µ w and µ p. The calculation is done for a t 1 = 2.0 µm thick water wet cell and a 0.8 % by mass protein solution with wet cell thicknesses t 2 of 0.5 µm (solid, black), 1.5 µm (dashed, red), 2.5 µm (dashdotted, green) and 3.5 µm (dashed double dotted, blue). Program: sim wc.pro (see appendix) 54

70 steps of the simulation can be followed in the program code (see appendix). The only spectrum that is not heavily biased by a discrepancy in wet cell thickness is the one recorded from the MW β-sheet solution. A fit of these data (Fig. 3.12) shows that we can get reasonable results. The fit parameters of the resonance at ev, which is due to 1s π transition at the carbonyl carbon is well defined, because the flank on the side to higher energies is relative high and the peak is relative sharp. That s why the oscillator strength can be fitted with an accuracy of 0.5 % and the peak position can be determined with an accuracy of ev, which is one quarter of the energy resolution of the monochromator. As pointed out in section 3.1, this transition is of major interest for studying the effect of hydrogen bonds in secondary structures of proteins. Unfortunately the transition to mixed σ -Rydberg state at carbon binding the amine group, which is expected at 290 ev cannot be determined by fitting the data, because it overlaps with various other σ and Rydberg states. It is not clear if the feature at ev reflects a transition or just noise, because at the carbon edge the lowest known resonances in amino acids are those to π states in aromatic carbon rings, which are located at around 285 ev. So in general, if there are differences in spectra due to secondary structures that are above the energy resolution of the monochromator they are reflected by the respective fit parameter. 3.5 Conclusion NEXAFS studies on poly-l-lysine at the carbine edge have to be done on solutions with concentrations between 3.5 % and 4.5 % by mass. This is a compromise between the limits given by the two types of measurements one has to perform. On the one hand the poly-l-lysine concentration should not be higher than these values in order to be able to perform circular dichroism measurements for determining the secondary structure of the protein. On the other hand however the concentration should not be lower, because other wise the optical thickness of poly lysine in a 3 µm water layer would be less than 120 nm. This corresponds to an absorption step of 0.45 in optical thickness, which is necessary for getting spectral features that are clearly distinguishable from noise. As it turned out that it is crucial for getting reasonable spectra that the thickness for the reference cell (pure water) and the thickness of the protein solution cells are the same one has to perform measurements for several wet cells for each protein solution, because it is not possible to controll their thick- 55

71 Wet Cell Simulation 2 1 OD Energy in ev Figure 3.11 Simulated optical density at the carbon absorption step without NEXAFS features using equation (3.11) and the Henke data for µ w and µ p. The calculation is done for a t 1 = 3.0 µm thick water wet cell and a 3.5 % by mass protein solution with wet cell thicknesses t 2 of 0.75 µm (solid, black), 2.25 µm (dashed, red), 3.75 µm (dashdotted, green) and 5.25 µm (dashed double dotted, blue). Noise is generated by Poisson statistics for 250 khz input flux, what leads to 250 khz output flux. Dwell time is set to 120 msec. For comparison with measured data the spectrum from the MW β- sheet sample is included, which seems to have pretty similar parameters compared to the simulation. Program: sim wc.pro (see appendix) 56

72 Poly-L-Lysine OD Energy in ev Energy in ev Figure 3.12 Poly-l-lysine spectrum ( MW, β-sheet) corrected by the optical density due to a water with an arbitrary thickness chosen so, that the minimum optical density is set to zero. The fit was applied using PAN. 57

73 ness directly. So one must get a set of data for different cell thicknesses and calculate the optical density from measured transmission signals of those with equal cell thickness, what can be determined by the offset of the spectrum. Another possibility to handle the problem of varying cell thickness might be, that we measure the incident and transmitted flux for wet cells containing a protein solution. Then we calculate the optical density from these data and subtract the theoretically calculated optical density due to the Si 3 N 4 -windows and the water layer. Of course the subtraction of optical density due a water layer of unknown thickness bears a certain inaccuracy. The results of the measurements at the carbon K-edge give some hint for investigations at the nitrogen K-edge. As the here described problems originate from sample preparation they also occur for measurements at the nitrogen edge. 58

74 Chapter 4 X-Ray Spectromicroscopy on Jaws of Glycera Dibranchiata 59

75 Using a scanning transmission x-ray microscope to aquire absorption spectra at each pixel of an image, a first look at the organic chemistry of the jaws of a worm, Glycera dibranchiata, is obtained. This worm has unusual characteristics for biomineralization of copper in its jaw. Its main component is a protein matrix, which by itself is flexible but not very hard and not very resistant. The interesting thing is that mineralized fibers are incorporated in the tip region where the jaw gets very thin. This is suggested to harden the respective region without making it porous and unflexible. This way destabilization due to the venom channel seems to be compensated (Fig. 4.2). According to fluorescence and x-ray powder diffraction measurements [49, 50] it turned out that the tip of the jaw switches over to a composition dominated by the copper containing mineral atacamite (Cu 2 (OH) 3 Cl) with some traces of Fe that might be associated with atacamite. This is a bit surprising, because copper usually acts in organisms as a poison. The major goal of this investigation is to determine how the general composition of the protein matrix in these jaws changes with varying atacemite concentration. Particular interest is turned to the ratio of histidine in the matrix. 4.1 Goals and Principle Ideas As I already explained in section 1.1, x-ray microscopy is a good tool for determining the spatial distribution of features in the protein matrix by taking microscope images and spectra at the same time. The question, however, is how can spectra provide information about the thickness of the protein layer and the histidine concentration in a sample? If one simplifies the issue a bit, this task can be handled the following way. With section in mind, one can theoretically determine the protein content in a sample by measuring the height of the ionization step carbon K-shell transitions. Since we know the atomic photo absorption cross section σ of carbon and the average density of protein and the average fraction of carbon in proteins, we can calculate the thickness of the protein layer from the step height. Different histidine concentrations in the protein matrix should be reflected in the carbon NEXAFS structure. That means we look for several pre-edge features (Fig. 4.1) which should be unique for histidine, and then determine the molecule concentration by the height of these features. Previously recorded histidine [20] and histidine-phenyl-alanine [25] spectra serve as references for this attempt. We had hoped to perform the same measurements for the copper L-edge around 900 ev in order to determine the atacamite concentration. The chance 60

76 8 mass absorption coeff. in g/cm Energy (ev) Figure 4.1 Carbon K-shell spectra (dissolved in trifluouro acetic acid or TFA) of thin films of pure histidine with 0.1 ev energy resolution [20] (black) and 0.3 ev energy resolution [25] (red, dashed). The histidine-phenyl-alanine spectrum [25] (green, dashed dotted) was also recorded at 0.3 ev energy resolution. The pre-edge features of these amino acids should allow one to distinguish their ratio in the protein matrix in the worm jaw of Glycera Dibranchiata. 61

77 that other copper containing minerals also occur in the sample is very small, as minerals containing this metal usually are poisonous for organic systems. Unfortunately after various measurements with different undulator settings it turned out that at the beamline X1A the flux at the copper L-edge is too weak to perform reasonable measurements. This led to the idea to compare carbon K-shell spectra with chlorine L-edge spectra at around 200 ev with the assumption that the chlorine content in the jaw is mainly determined by the atacamite content. We considered this approach as reasonable due to the fact that amino acids don t contain any chlorine and in [49, 50] it is suggested that atacamite is the dominating mineral in the jaw. This too was not possible, so we recorded stacks at around 270 ev in the hope to find any XANES structures in this energy region. Unfortunately our data do not show theses structures, but we can still make use of the measurements. With the assumption that only protein and atacamite mineral is present we can estimate the ratio of the two components using the Henke data for the absorption coefficients of these materials. So the major task of this investigation is to find differences in the protein matrix in the worm jaw of Glycera Dibranchiata with special focus on the correlation of histidine with the local atacamite concentration. 4.2 The Samples Samples of the jaw from Glycera Dibranchiata were obtained from the Wait Lab, Biomolecular Science and Engineering, University of California at San Diego. Several pieces of the jaw tip and jaw base were microtomed and fixed onto thin Copper or Gold grids. Fig. 4.2 shows a sketch of such a tooth with an indication of mineralized regions, pure protein matrix and a venom channel for injecting poison into a bitten victim. According to [49, 50] the mineral was identified as the Copper containing mineral atacamite (Cu 2 (OH) 3 Cl). Besides stabilizing the jaw, it is possible that this mineral is also involved in venom production. The goal of the sample preparation process was to get extremely thin slices of the samples with a thickness of 100 nm to 200 nm. Therefore the teeth were first cleaned and dried before they were embedded into either two component (EpoFix) or three component (Embed-812) epoxies, which were both purchased from Electron Microscopy Science. The method of preparing the epoxies first and then placing the samples in filled moulds avoided penetration and provided full mechanical support around the whole sample. After the glue was dried one could cut 100 nm and 200 nm thick longitudinal slices of the jaws using a diamond knife and an ultra micro-tome. These thin films 62

78 venom canal Cu mineral fibers organic jaw material 0.4 mm Figure 4.2 Sketch of a longitudinal section of a typical Glycera Dibranchiata worm jaw. Samples were either taken from the tip region, the upper third of the jaw, or from the base, the lower third of the tooth. The short, black lines indicate mineralized fibers in the protein matrix. 63

79 sample epoxy Figure nm thick TEM sections of different Glycera Dibranchiata jaw tips in Embed-812 under the visible light microscope. In this work it is referred to as sample A4 #1 in the upper left, A4 #2 in the middle and A4 #3 in the lower right region. The dark regions in the microtome sections are the actual worm jaw samples, where the light areas consist of epoxy. were put onto a water surface, where they floated until they were caught up by a Copper or Gold grid with a formvar support film. This method favors a flat thin-film on the grid and avoids too much folding and crumpling of the slices on the grids. When the samples were observed with the STXM the grids were fixed onto the microscope sample holder with some tape at the edge and mounted in the microscope chamber. Fig show how some of the respective samples we analyzed with the x-ray microscope looked like under the visible light microscope. 4.3 Data Processing Tools We recorded stacks of various tooth sections, with an energy resolution that can be calculated from the setting of the monochromator entrance and exit slits (section 2.5). We processed these raw data with principal component 64

80 sample epoxy Closer to tip Closer to base Figure 4.4 A zoomed in picture of the 200 nm thick TEM section (sample A4, #3) in the lower right of Fig. 4.3, taken by using a visible light microscope. The dark region can be identified as jaw tip and the lighter region as epoxy in which the jaw was embedded for microtoming. A detailed description of the locations where stacks are taken can be found in Fig. B.1 in the appendix. 65

81 Closer to jaw tip Closer to jaw base Figure 4.5 On the right we can see a 200 nm thick TEM sections of the Glycera Dibranchiata jaw base in Epofix under the visible light microscope along with a sketch showing which part of the tooth the slice represents. analysis and a curve fitting procedure in order to get the information we are interested in Principal Component Analyzes and Cluster Analyzes As we are interested in the spatial distribution of the protein composition, especially the fraction of histidine, it is useful to group areas in the sample by their similarity in their spectra, because the spectra reflect the composition and structure of these molecules. This can be achieved by principal component analysis [51, 52, 53]. As this approach was recently discussed in detail I explain only briefly how it works. When we record a stack, we get for each pixel in the scan area the respective spectrum. The optical density for the whole stack can be expressed as D N P = µ N S t S P, (4.1) where P is the number of pixel in the image and N is the number of photon energies per pixel for which the optical density was measured. As short and sloppy explanation one can say that in pricipal component analysis one ex- 66

82 Figure 4.6 Illustration of the learning vector quantization cluster algorithm. The data points (pixels p) are plotted on their weights R i,p and R j,p in two eigenspectra i, j. On the left hand side one can see a number of cluster centers, which are originally located at random positions (unfilled crosses). The distance to each cluster is calculated and the winning cluster, which is the closest one, is moved towards the pixel. In each iteration cycle this is done for all pixels. After 20 iterations the clusters are located in the middle of the respective pixel group and each pixel is assigned to its winning cluster [52, 53]. presses the optical density in its eigenvector basis. Then it turns out that one only needs a few eigenspectra and eigenimages (the principal components) to reconstruct the stack. The remaining eigenspctra represent only minor differences like noise. That means by choosing an appropriate set of eigenvectors one automatically filters out the noise in the spectra while the major properties of the data are conserved. In the program pca gui [52, 53] the spectrum of each pixel is represented as one point in the vector space spanned by the principal components. The initial guesses of cluster centers consist of a series of random positions in this space. Each pixel then attracts the closest cluster in such a way that the distance the cluster is moved towards the respective pixel decreases with iteration number. At the end, after a certain amount of cycles, each pixel is assigned to the closest cluster. Finally these clusters to which no pixels are assigned are erased. A cluster map then shows which pixels belong to which cluster. The spectrum of a cluster is calculated by averaging over the spectra of all pixels that are grouped together. It turned out that if one performs cluster analysis with an Euclidean distance measure, pixels are not only grouped by their chemical features but also by their integrated optical thickness. This can 67

83 Figure 4.7 Illustration of the use of angle distance measure in cluster analysis. For clustering one must exclude pixels below a radius at which different compositions will intermingle due to noise at low optical densities [54]. be understood with the following picture in mind: if there are two clouds of pixels with the same spectral features but different optical density, then the vector for each pixel in the space spanned by the principal component points in the same direction, but has different length. That means that pixels with short vectors would be erroneously assigned to a different cluster than pixels with long vectors. To overcome this problem one can employ an angle distance measure during the clustering process. The issue one has to take care of by choosing this procedure is that pixels close to the origin bias this process, because pixels from different compositions intermingle. In order to prevent this one can exclude pixels within a certain distance to the origin so that they don t attract any clusters Curve Fitting In order to obtain a good interpretation of the recorded spectra it is useful to fit these curves with their main components and compare them to each other. This way one gets more insight into the physical properties of the molecules. In [55] it is demonstrated that it is possible to fit spectra of complex molecules reasonably with one arc tangent step function and seven or eight Gaussian peak functions. This approach works quite well for several examples, but it might not be a procedure that can be applied generally on all spectra. On issue is that by applying this method one assumes that the line shape of the mea- 68

84 sured absorption peak is Gaussian-like. In other words, the profile of recorded absorption peaks is a convolution of the natural line profile of the molecule with the line shape of the beamline output. In section I mentioned that the photon absorption process can be described by damped driven harmonic oscillators. In that ease, the natural line profile should be of Lorentzian shape. The profile of the beamline output however is a convolution all the optics, like entrance slit, monochromator and exit slits. This can be approximated by a Gaussian profile. So due to the fact that the recorded absorption line profile is a convolution of Lorentzian profile with Gaussian profile the shape of the measured absorption line depends on the relative width of these two profiles. Let s assume for example, that the width of the Gaussian is much smaller than the Lorentzian width. Then the convolution of the two functions gives a profile that is pretty similar to the Lorentzian profile. So if the energy resolution of the beamline is such that its line width is much lower than the natural line width of the measured transitions one can perform better fits by using Lorentzian functions instead of Gaussians. That s the reason why we used Lorentzians to fit 1s π transitions instead of Gaussians. It is then straightforward to fit spectra with a function that is the convolution of both line profiles in the case that both, the line width of the beamline and the natural line width of the molecules are comparable to each other. This however means several integrations for each fit cycle and would therefore cost very much CPU power. A good approximation of such a function is the Voigt profile, which is a linear combination of Lorentzian and Gaussian function. I V (E) = H ( ) (Γ/2) 2 ((E P )/(Γ/c))2 η + (1 η) (E P ) 2 e 1/2 + (Γ/2) 2 (4.2) where c = 2 ln 4, η is the Lorentzian fraction and Γ represents the FWHM of the Voigt profile. So a better model than fitting all absorption peaks in a spectrum by Gauss functions is approximating them by Voigt profile. Sometimes however one can do better by using assymetric Lorentzians and Gaussians. If the spectrum was recorded with a low energy resolution of about ev one can see effects of molecule vibrations quite well. Of course these vibrational effects in the spectrum cannot be completely resolved by x-ray spectroscopy, but they cause asymmetric absorption peaks in the x-ray spectrum with increasing peak width by increasing photon energy. This can be explained with the fact that the x-ray absorption process is fast compared to the vibrational motion of the molecule and therefore the NEXAFS spectrum is sum of snapshot spectra of molecules at different stages of their vibrational 69

85 Figure 4.8 Explanation of the asymmetric line shape of σ resonances using a diatomic molecule as example. Due to vibration of the internuclear bond the length of the bond changes, which causes shifts in the position of the resonance. The lifetime for final states decreases with increasing energy of the state. So transition peaks with higher energy are broader than those for lower energy. The observed resonance is just the sum of a series of overlapping peaks with contributions from various diatomic bond length. The figure was taken from [12]. 70

86 cycle (Fig. 4.8). Considering this the asymmetry effect should be important for transitions for which every snapshot is already relative broad. That means that this issue gets especially important for broad σ resonances. It turned out that these broad features above the continuum step could be modeled best by asymmetric Gaussian functions [12] where Γ = E ev. I asym (E) = H e 1/2 ((E P )/(Γ/c))2, (4.3) In general one can fit NEXAFS spectra quite well by reproducing the absorption peaks with Voigt profiles and asymmetric Gaussians. Another issue however is the best choice of function to reproduce the continuum step. We assume that its lineshape is determined by the lifetime of the core hole. This implies the convolution of a square step function with a Lorentzian which can be well approximated by an arc tangent step function. If the the energy resolution of the equipment is greater than the natural line with we should apply a convolution of a square step function with a Gausssian, which means we would use an error function erf to reproduce the continuum step. If the energy resolution of both the beamline and the natural line width are of the same order it is reasonable to fit the continuum step in x-ray spectra by a linear combination of both I S (E) = H ( ( 1 η π arctan E P ) Γ/2 + (1 η) ( ( ))) E P 2 erf Γ/2 (4.4) Summarizing the ideas presented above, one can get proper fits of NEXAFS spectra by using models which consist of a combination of Voigts profiles for narrow peaks, asymmetric Gaussians for the broad σ resonances above the continuum step and a linear combination of arc tangent and error function for the continuum step. Unfortunately we did not have any computer program providing us with all functions described above integrated into our software for handling stacks or spectrum image data. The here presented data are processed with PAN, which is a free software package that runs under IDL. It allows to model curves by Gaussian, Lorentzian and error functions. As the measurements were performed with an energy resolution below 0.25 ev it is about one order of magnitude smaller than the width of the continuum step and therefore using a pure error function in the fit model might not be the best solution. 71

87 4.4 On the Hunt for Histidine and Atacamite Processing of Carbon K-shell Data The stack of the region closer to the tip in sample A4, #3 was recorded in 0.1 ev steps with a slit setting of 40 µm (entrance slit), 30 µm (exit slit) and 25 µm (vertical exit slit). According to Fig that corresponds an energy resolution of about 0.1 ev. In Fig. 4.9 one can see a STXM optical density image of the respective region at a photon energy of ev. The optical density is expressed by a gray scale with black corresponding to the minimum and white to the maximum optical density in the stack. From the VLM images we know that the upper dark region can be assigned to the epoxy in which the jaws are embedded for microtoming, and the bright triangles in the lower right and left corner are identified as grid mesh. That means that the region between mesh and epoxy must be protein matrix. Obviously there are three different regions varying in their optical density. At ev the protein matrix in the lower part of the image is the one with the highest optical density where the dark region in the middle has the lowest one. The results of principal component analysis in combination with cluster analysis confirm this picture. As shown in Fig. 4.9 on the right, the protein matrix can be grouped to three different regions (yellow, red, green). The cluster spectra (Fig. 4.10) are averaged over the whole cluster. Their difference to each other is in the energy interval between ev and ev, with the features of the red cluster lying in between the other features of the other two clusters. So this agrees with the STXM image and the cluster map. Because last one indeed shows three different clusters and some pixels between the yellow cluster and the green one are assigned to the red cluster. This assures that the spectral and therefore the molecular features of the red cluster are in-between the red and the green one. So the molecular properties assigned to the respective spectral feature have a distinct spatial distribution. A fit model gives more insight into the molecular properties of the protein matrix and allows a better estimate of its mixture than judging by eye. In order to minimize aliasing effects the data were first linearly interpolated. Linear interpolation was chosen in order to cancel out any bias that could result from the interpolation model, for example polynomial interpolation would smoothen sharp peaks. Then for each spectrum a fit model was chosen to provide proper fits with as few components as possible. For the fit itself the slope of the offset was fixed to zero because otherwise some fits resulted in unphysical values. The only further constraints are that for each component the height and width must be positive values. 72

88 Figure 4.9 Left: A STXM image from the recorded stack, showing the optical density of the worm jaw sample A4, #3 (close to the tip). The image is pixels big with (0.100 µm µm) pixel size. The gray scale expresses the optical density with black corresponding to the lowest and white to the highest optical density in the stack. At ev photon energy one can clearly distinguish different areas by their optical density. The rectangle indicates the region on which principal component analysis in combination with cluster analysis was applied. Right: The cluster map shows the same structure as the STXM image on the left. Pixels with similar spectra are drawn in the same color. 73

89 4 3 Cluster spectra Photon energy (ev) Figure 4.10 Cluster spectra (sample A4, 3, close to the tip): Each spectrum is an average over all pixels in the respective cluster. Colors refer to the cluster map in Fig The epoxy spectra (violet and brown) differ mainly in their optical thickness, where the three protein matrix cluster (yellow, green, red) exhibit differences in the peak height at around 287 ev and 288 ev. A closer look to the cluster spectra reveal a kink at ev. This is a result from beam instabilities. When the stack was recorded there was a beam dump with the consequence that two images at 284. ev and ev were obviously damaged and had to be removed from the stack. It seems like we didn t remove all images that are affected by beam instabilities. 74

90 Figure 4.11 Detailed C K-edge spectra of the amino acids phenylalanine, tyrosine, tryptophan and histidine from [20]. According to Fig these are the only amino acid candidates to be considered for the absorption resonances at ev, ev and ev. The fits of the protein cluster are presented in Fig Before one tries an interpretation of the result it is useful to realize that the error for the fit parameters are very small. That means that the main error of the fit is not based on the accuracy of the calculation itself, but on the choice of the fit model. The results document that the spectrum of the protein matrix can be reasonably reproduced by a model consisting of one error function step, four Lorentzian functions for the pre-edge features and one broad Gaussian function for the features with energies above the continuum step. Due to the residual plot the major error of the fit is located in the ev and ev. This can be explained with the fact that in general the quality of the fit strongly depends on the parameters of the continuum step function, because a small variation in its parameters has tremendous impact on the parameter of the ev peak and the ev Lorentzian. The parameters of the step function would probably be a bit closer to the correct values if the broad feature above the continuum energy would be represented by an asymmetric Gaussian and if the step itself would be modeled by an arc tangent function instead of an error function. Now to the interpretation of the spectrum. In the green cluster the relatively broad and well distinguishable transition at ev originates from aromatic C=C bonds [55, 45, 12]. It is followed by a close 1s π reso- 75

91 nance at ev resulting from carbon rings that contain oxygen or nitrogen atoms. For example in phenol rings the carbon atom which bonds to the oxygen of the OH group can induce such a feature [20], as in the case of phenol or aryl ether [56]. Another explanation of this feature is that if there is any histidine present, the carbon atom C 3 that connects the histidine backbone to the aromatic molecule ring (Fig. 4.11) also contributes to this transition at ev [20]. For histidine this peak is much smaller than the ev 1s π transitions at the two carbon atoms C 4 and C 5 in the aromatic ring [20]. So an explanation why in the cluster spectra the oscillator strength of this resonance is smaller than the ev transition might be phenolic carbon and carbon in the molecule ring of histidine contributing to the ev peak. This would imply that the amino acid tyrosine also plays a role by interpreting the spectrum. However, if this amino acid is present there should be a resonance at ev due to the carbon atom C 3 that bonds the phenolic ring to the amino acid back bone (Fig. 4.11). So this might be the reason why the Lorentzian that fits the feature at ev turns out to be so broad. As in the tyrosine spectrum the resonance at ev is much smaller than the one around 285 ev and in the cluster spectrum the oscillator strength at ev is three to four times lower than for ev resonance, so the contribution of tyrosine to this resonance is below 10%. Therefore this transition must result from histidine and the ev resonance from phenylalanine. But then we have to explain why in the cluster spectra the oscillator strength is weighted differently compared to the spectra of the single amino acids. The histidine spectrum recorded by [20] was for a situation in which the amino acid was mixed with TFA, a strong acid. The aromatic side chain is protonated in this environment as shown in Fig In the worm jaw, however, the ph level is probably neutral, leading to the circumstance that a large fraction of the imidazole histidine side chains is neutral. In the neutral state the nitrogen atom without hydrogen contributes the free electron pair to the carbon ring and consequently reduces the 1s π transition energy at the adjacent carbon atoms. So the different weight in the oscillator strength for the ev and ev transitions reflects the variation in charge state of the histidine side chain. The strong resonance at ev can be assigned to the 1s πc=o transition at the peptide bonds [21, 23, 25]. In general 1s π transitions are very sensitive to the electronegativity of the atoms binding to the carbon [26]. That explains that the 1s πc=o transition is 0.5 ev red shifted compared to the respective transition in single amino acids, for which it is usually at ev [21]. In protein chains the amino acids are connected by peptide bonds, which means that the NHC term binds to the carbon atom (Fig. 1.11), while in 76

92 single amino acids the carbon atom binds to the more electronegative carbonyl group that results in a greater core hole potential compared to peptide bonds. At energies above the continuum step, which is located at ev, the spectrum is determined by mixed σ -Rydberg states [12, 20, 21]. Because of the delocalized nature of the σ states and the high multiplicity of several states originating from the fact that proteins are large molecules, it is difficult to fit this region properly. Indeed a lower amplitude of the continuum step in combination with a bunch of overlapping Gaussians may fit the curve well but one should doubt that the result reflects the physical properties. The pre-edge absorption peaks in the cluster spectra result from transitions in histidine-phenylalanine complexes in the protein matrix. The respective fit parameters agree very well with those of the pure histidine-phenylalanine thin film spectra recorded by Boese and Osanna (Fig. 4.16) if one takes into account that this raw spectrum was not calibrated. It therefore has an offset of +0.3 ev. So the arguments in the previous discussion of the NEXAFS spectrum of the protein matrix are supported by the fact that with the previous interpretation the fit parameters of the histidine, histidine-phenylalanine spectrum and those of the protein matrix spectrum are in conformation with each other Histidine Fraction in the Protein Matrix With this interpretation in mind we can now try to estimate the difference in the histidine fraction in the protein matrix. As shown in the previous section the absorption peaks at ev and ev depend only on the content of histidine in the protein matrix. So there are now three possibilities to estimate the histidine fraction in the protein matrix. The first one is that we calculate the ratio of oscillator strength of the histidine and phenylalanine features to the oscillator strength of the 1s πc=o transition for each cluster spectrum and compare these values. As the integrated optical density of the ev resonance is proportional to the number of peptide bonds and therefore to the number of amino acids in the scanned volume, we get information about the variation of the percentage of histidine and phenylalanine amino acids relative to the total number of amino acids. The second option is to calculate the fraction of histidine oscillator strength of the overall oscillator strength in the scanned energy region. Comparing these values between the cluster spectra means comparing the ratio of histidine carbon to overall carbon in the protein matrix. The results might differ a bit from those we obtain with the previous approach, because if for example the protein matrix of one cluster basically consists of amino acids with short backbones while the matrix of the other cluster consists of amino acids with 77

93 Yellow Cluster (sample A4, #3, close to the tip) OD Energy in ev Energy in ev Figure 4.12 Fit of the yellow cluster spectrum (sample A4, #3, close to the tip) including all fit parameters. 78

94 Red Cluster (sample A4, #3, close to the tip) OD Energy in ev Energy in ev Figure 4.13 Fit of the red cluster spectrum (sample A4, #3, close to the tip) including all fit parameters. 79

95 Green Cluster (sample A4, #3, close to the tip) OD Energy in ev Energy in ev Figure 4.14 Fit of the green cluster spectrum (sample A4, #3, close to the tip) including all fit parameters. 80

96 Histidine ( TFA thin film) Figure 4.15 Fit of a histidine spectrum, which was recorded by Kaznacheyev. The results for the fit from ev to ev are principally the same as published and discussed in [20]. So the applied procedure is reasonable and a solid basis for the interpretation of the data. Above ev the signal is determined by TFA features, what makes this part of the spectrum uninteresting for drawing comparisons to other spectra. 81

97 Histidine-phenyl-alanine (TFA thin film) Figure 4.16 Fit of a histidine-phenylalanine spectrum, which was recorded, published and discussed by [25]. The fit was applied from ev to ev. Probably this raw spectrum is not calibrated, what results in a constant offset of the whole spectrum to higher or lower energies. The position of the C 1s πc=o transition at ev should be at ev [21]. Then the fit parameter of histidine-phenylalanine agree with the fit parameter for the cluster spectra. The features at energies above ev are due to TFA. 82

98 cluster relative histidine content relative phenylalanine content green 100 % 100 % yellow 78 % 85 % red 66 % 125 % Table 4.1 Relative difference in histidine and phenylalanine, normalized to the oscillator strength of the C 1s π C=O transition. cluster relative histidine content relative phenylalanine content green % 100 % yellow 69.8 % 89.2 % red 80.9 % % Table 4.2 Relative difference in histidine and phenylalanine, normalized to the overall oscillator strength from ev to ev. long side chains, the oscillator strength of the 1s πc=o transition is lower in the cluster with long amino acid side chains than in the one with short ones. Fig indicates that this applies for the green cluster, because its optical density at ev is a bit lower compared to the other two clusters despite the fact that the part of the spectrum above the continuum step is the same for all three clusters. The third possibility to estimate the difference in histidine ratio is to compare the absolute values of the oscillator strength of the respective resonance. This might work quite well, because we can assume that in the scan area the physical thickness and the protein concentration is constant. Considering the cluster spectra in Fig one can see that for energies above the ionization step there is nearly no difference in the spectra, which supports this approximation. As the oscillator strength is proportional to the integrated optical density and we are interested in the ratio of the respective optical densities it is sufficient for our purpose to compare the area of the respective peaks. The results in Tab. 4.2 and Tab. 4.3 are very similar. The variation may be due to the approximations we made by calculating the ratio of the respective amino acids with absolute values, namely that thickness and concentration of the protein matrix are constant in the area where the stack was recorded. Another source 83

99 cluster relative histidine content relative phenylalanine content green % 100 % yellow 72.2 % 92.2 % red 85.9 % % Table 4.3 Relative difference in histidine and phenylalanine, obtained by comparing the absolute oscillator strength for the transition peaks at ev and ev for histidine and at ev for phenylanaline. of error might be the assumption that the oscillator strength in the energy range from ev to ev is the same for the different clusters. The rather large discrepancy to the values calculated in Tab. 4.1 is a result of the lower 1s πc=o resonance in the spectrum of the green cluster as already described above. Various scans of different samples at similar areas reveal the same tendency. The histidine concentration is highest in the cluster close to the center of the jaw and decreases in the regions near the edge. The phenylalanine content fraction however is highest at the very edge and decreases in the direction to the jaw interior. This general tendency is also reflected by results from other measurements. The respective data of STXM images, cluster spectra fits and tables can be found in the appendix Atacamite Concentration in the Protein Matrix Unfortnately with the present configuration of the beamline X1A it is not possible to record any spectra at photon energies below 250 ev or above 850 ev. Test measurements for different undulator settings revealed that the photon flux at the copper L-edge is not high enough for our purposes, while 200 ev photon energy would receive an undulator gap smaller than the vacuum chamber permits. As alternative we tried to find any chlorine XANES features in the energy range from ev to ev. Unfortunately the obtained data don t exhibit any XANES features, but that does not mean that we can t make use of the measurements. In this section it is described how the atacamite concentration can be determined from the respective stacks anyway. One of the stacks (Fig. 4.17) was taken from sample A4, #3 close to the tip. It can be identified as the same area where the respective carbon stack was taken from (Fig. 4.9). After processing the data with principal component analysis and cluster analysis the pixels were grouped as presented on the right 84

100 Figure 4.17 A STXM image showing the optical density of the worm jaw sample A4, #3 (close to the tip). It was recorded in the same region as the carbon edge stack in Fig The image is pixels big with (1.0 µm 1.0 µm) pixel size. The gray scale expresses the optical density with black corresponding to the lowest and white to the highest optical density in the stack. The bright feature in the middle of the image corresponds to the brown and violet cluster region in Fig The rectangular indicates the region on which principal component analysis in combination with cluster analysis was applied. Right: The cluster map reflects the same structure as the STXM image on the left. The yellow cluster occupies the same area as the red one in Fig. 4.9, what indicates that there is a correlation between optical density in the 270 ev stack and the spectrum of the protein matrix at the carbon edge. 85

101 hand side of Fig The images look very rough compared to the stacks at the carbon edge because they were recorded with 1.0 µm 1.0 µm pixel size. The cluster spectra represented in Fig are flat, without any resonances and differ only in their overall optical density. A comparison of the data with the respective carbon edge stack reveals that the red cluster can be assigned to epoxy, the yellow cluster in Fig corresponds to the red protein cluster in Fig. 4.9, and the lower blue cluster in Fig can be associated with the yellow protein cluster in Fig That indicates that there is a correlation between the carbon K-shell spectrum of the matrix and the optical density in the range of ev to ev. We trace back the change in optical density to different atacamite concentrations. The way of thoughts is the following: We know that microtoming produced worm jaw slices of 200±20 nm thickness. That means we can assume that the sample is equally thick. So as changes in the physical thickness of the sample can t be the reason for the different optical density, it must be due to differences in the composition of the protein matrix. From the data taken at the carbon edge we can calculate a carbon map which gives us information about the protein density in the respective cluster. Using the Henke data, we can then calculate the contribution of protein to the optical density at 270 ev. As a result we can calculate the density of atacamite in the respective cluster. Let s start with the calculation of the carbon map. With equation (1.5), equation (1.6) and the assumption that there is only protein and atacamite present the optical density can be expressed as OD = (µ(e) m, p ρ p + µ(e) m, a ρ a ) t. (4.5) As µ(e) m, p is directly related to the atomic cross section (equation 1.5) it determines the shape of the spectrum at the carbon edge (section 1.2.1). The height of the continuum step at the carbon edge is proportional to the protein density. For energies far enough below and above the continuum step in the absence of any NEXAFS structure the values of µ(e) m, p can be calculated from the Henke data (equation 3.3). Then we calculate the protein density as following: µ m = m A 2 r e λ f 2 (4.6) continuum step height = OD(300 ev) OD(280 ev) (4.7) 86

102 cluster protein density ρ p in cm g yellow 1.24 ± 0.12 red 1.33 ± 0.13 green 1.33 ± 0.13 Table 4.4 Carbon map for sample A4, #3 to the tip. The protein density is given for the cluster areas shown in Fig. 4.9 and the respective cluster spectra (Fig Fig. 4.14). The results were obtained using equation (4.9) with thickness of the sample t = 200 ± 20 nm, µ m, p (280 ev) = cm2 µg and µ m, p (300 ev) = cm2 µg. The determined protein density is probably a bit too high, because the calculation had to be done using fit parameters from a simplified fit model. continuum step height = µ m, p (300 ev) ρ p t µ m, p (280 ev) ρ p t (4.8) ρ p = continuum step height (µ m, p (300 ev) µ m, p (280 ev)) t. (4.9) The continuum step heights are taken from the fit of the cluster spectra (Fig Fig. 4.14). In these fits we don t take into account the effect of the atacamite on the spectrum. However as the change in the absorption coefficient for atacamite µ m, a in the respective energy range is small compared to the change in µ m, p its contribution to the optical density can be neglected. Therefore the error in the height of the continuum step due to this effect is also negligible. The results are shown in Tab As described above the fit parameters of the step function are inaccurate due to the delocalized nature and high multiplicity of the σ -Rydberg states above the continuum step. That means we modeled the spectrum above the continuum step only with one broad Gaussian and the step function leading to fit parameters greater than the real absorption step hight. One possibility to overcome this problem is recording spectra upto energies where no NEXAFS feature occurs, but the spectrum is only determined by the continuum step heights. When the spectra were recorded, however, we did not know that we will have to determine the atacamite concentration this way. Therefore we took data only from ev to ev. With the Henke data we calculate the mass absorption coefficient 87

103 Cluster spectra Photon energy (ev) Figure 4.18 Cluster spectra (sample A4, 3, close to the tip): Each spectrum is an average over all pixels in the respective cluster. Colors refer to the cluster map in Fig The stack was taken with only 40 % flux compared to the carbon edge stacks, resulting more noise. However one can clearly distinguish three regions of different optical density. for protein to be µ m, p (270.0 ev) = cm2 µg and for atacamite to be µ m, a (270.0 ev) = cm2 µg. The density of atacamite can then be determined by solving equation (4.5) for ρ a. ρ a = OD(270.0 ev) µ m, pρ p t µ m, a t (4.10) The results can be found in Tab As explained we expect lower values for the protein density and therefore higher values for the atacamite concentration. The obtained values reflect the general tendency anyway. We got similar results for the stack region in sample A4, #3, to the tip. 88

104 cluster atacamite density ρ a in cm g yellow 0.44 ± 0.06 red 1.79 ± 0.25 green 0.21 ± 0.03 Table 4.5 Atacamite concentration in the cluster presented in Fig. 4.9 (sample A4, #3 to the tip), calculated from the cluster spectra at 270 ev (Fig. 4.18) with equation (4.10) using the values from Tab The optical density at 270 ev was for the yellow cluster and for the blue cluster Conclusion The results presented above suggest several things. Despite the fact that at the beamline X1A it is not possible to determine the atacamite content by measurements at the copper L-edge or the chlorine L-edge, we can estimate its concentration in the protein matrix by using the Henke data. We can show that on the one hand the concentration of histidine and phenylalanine in the protein matrix in the worm jaws of Glycera Dibranchiata is correlated with the presence of atacamite by the way that there is a rise in histidine with decreasing atacamite concentration. On the other hand atacamite is mainly incorporated into the protein matrix at the border of the jaw sections. The concentration of phenylalanine increases with increasing atacamite concentration. Unfortunately we cannot make any statements about other components in the protein matrix as we lack of unique spectral features concerning this issue. For example we cannot determine the concentration of proline and glycine which are the main components of the stabilizing protein collagen, which might also contribute to the stiffness of the jaws. However there is still an open question: both stacks, the one at the carbon edge and the other one at around 270 ev, show that the optical density in the 3 µm thick region where epoxy and protein matrix interfere is much higher than in the rest of the sample. This was not only the case for one specimen, but for several. So the possibility that variation in the absolute thickness of the microtomes are the reason for this effect is canceled out. An explanation might be that the epoxy glue interacts with the protein matrix in a way that the density in this region increases. Of course this implies that the outer layer of the worm jaw is penetrated during the preparation process. We are not able to determine if this has any effect on the local atacamite distribution. All the measurements presented here were performed for a specimen from 89

105 the tip regions of the jaw. So there is still the question left of how the properties might differ at the jaw base. That means that if we can perform the same measurements at samples from the base, we can make not only a statement about the local histidine-atacamite correlation, but also about the global properties. 90

106 Chapter 5 General Conclusion The investigation on poly-l-lysine in chapter 3 showed that there are problems resulting from the sample preparation. The thickness of the wet cell varies between different samples. These variations cannot be controlled. However we offered several solutions of the problem in section 3.5. That means that the initial goal, finding spectral features of secondary structures in proteins can still be achieved. In our work on biomineralized proteins of the jaws of Glycera Dibranchiata we could find correlations between histidine, phenylalanine an atacamite. With spectromicroscopy we could determine the spatial distribution and estimate the concentrations of these components. 91

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113 Appendix A X-ray Spectroscopy on Poly-L-Lysine 98

114 500 Raw and Wiener filtered Spectrum (black, red) Flux in khz Energy in ev Residuals 5 Residual Energy in ev Power Spectral Density PSD Frequency in 1/eV Figure A.1 Top: Wiener filtered transmission signal of a wet cell containing a 3.8 % by mass poly-l-lysine solution ( MW, α-helix). Center: Residual for the filtered signal. Bottom: Power spetral density with including noise floor and signal function (red) for thewiener filter function, which is defined as F (k) = signal2 +noise 2 [57]. noise 2 99

115 Energy:: ev Flux in khz Dwell time in ms Figure A.2 Radiation damage test: This curve is representative for how all radiation damage tests look like. For certain wave lengths (here ev) the transmitted flux was recorded over time. As there is no change in intensity we conclude that the recorded spectra should not be biased by radiation damage. The relative high noise in the signal compared to Fig. A.1 originates from the short dwell time of 5 ms compared to 120 ms by recording the spectra. 100

116 Poly-L-Lysine, alpha-helix, MW OD Energy (ev) Figure A.3 Optical density of α-helical poly-l-lysine (3.8 % solution of MW molecules), calculated from the transmitted flux from the wet cell containing the respective poly-l-lysine solution and the pure water wet cell with equation (3.11). 101

117 Poly-L-Lysine, alpha-helix, 9200MW, 1.9% OD Energy (ev) Figure A.4 Optical density of α-helical poly-l-lysine (1.9 % solution of 9200 MW molecules), calculated from the transmitted flux from the wet cell containing the respective poly-l-lysine solution and the pure water wet cell with equation (3.11). 102

118 Poly-L-Lysine, alpha-helix, 28200MW, 0.8% OD Energy (ev) Figure A.5 Optical density of α-helical poly-l-lysine (0.8 % solution of MW molecules), calculated from the transmitted flux from the wet cell containing the respective poly-l-lysine solution and the pure water wet cell with equation (3.11). 103

119 Poly-L-Lysine, random coil, 28200MW, 3.2% OD Energy (ev) Figure A.6 Optical density of random coil poly-l-lysine (3.2 % solution of MW molecules), calculated from the transmitted flux from the wet cell containing the respective poly-l-lysine solution and the pure water wet cell with equation (3.11). 104

120 ;; This is a simulaion of a wet cell. One can choose the thickness of the water layer in the reference wet cell and ;; in the solution layer in the protein cell. In reality each layer probably varies between 0.5 um and 4.0 um. ;; Next input is the protein concentration by mass in the protein wet cell. The flux parameter is an 2d array ;; containing the input flux in counts for the respective energy ;; M. Haeming, Stony Brook 2005 PRO sim_wc, flux=flux, t_1=t_1, t_2=t_2, concentration=concentration, add_noise=add_noise ;; If no flux is provided create your own flux (in khz) IF N_Elements(flux) EQ 0 THEN BEGIN flux = FLTARR(2,301) flux(0,*) = INDGEN(301) * 0.1 flux(1,*) = flux(1,*) ENDIF ;; dwell in ms n = 120. ;; Thickness in um of the plain water cell (t_1) in um IF N_Elements(t_1) EQ 0 THEN t_1 = 2. ;; protein concentration by mass in % IF N_Elements(concentration) EQ 0 THEN concentration = 2.5d ;; rho in g/cm^3 for water and protein rho_w = 1. rho_p = 1.35 henke_array, 'H2O', rho_w, energy_w, f1_w, f2_w, delta_w, beta_w, graze_mrad_w, reflect_w henke_array, 'PROTEIN', rho_p, energy_p, f1_p, f2_p, delta_p, beta_p, graze_mrad_p, reflect_p ;; const = hc/e * 10^-6 const = DOUBLE( ) inv_lambda_w = energy_w/const inv_lambda_p = energy_p/const ;; mass absorption coeff in 1\um if t in um: mu_m_water = 4. *!pi * inv_lambda_w * beta_w/rho_w mu_m_protein = 4. *!pi * inv_lambda_p * beta_p/rho_p dummy = MIN(ABS(energy_p ), energy_start) dummy = MIN(ABS(energy_p ), energy_stop) dummy = MIN(ABS(energy_p ), energy_stop2) energy = INDGEN(301, /float) * 0.1 energy_1 = INDGEN(60, /float) * 0.1 energy_2 = INDGEN(241, /float) *

121 coeff1 = POLY_FIT( energy_p[energy_start : energy_stop-1], mu_m_protein[energy_start : energy_stop-1], 3) coeff2 = POLY_FIT( energy_p[energy_stop : energy_stop2], mu_m_protein[energy_stop : energy_stop2], 3) mu_m_p1 = coeff1[0] + coeff1[1] * energy_1 + coeff1[2] * (energy_1)^2 + coeff1[3] * (energy_1)^3 mu_m_p2 = coeff2[0] + coeff2[1] * energy_2 + coeff2[2] * (energy_2)^2 + coeff2[3] * (energy_2)^3 mu_m_p = [mu_m_p1, mu_m_p2] mu_m_w = INTERPOL(mu_m_water, energy_w, energy) IF N_ELEMENTS(t_2) EQ 0 THEN BEGIN array = 1 OD_p = FLTARR(4,N_ELEMENTS(mu_m_w)) OD_w = FLTARR(4,N_ELEMENTS(mu_m_w)) Io = FLTARR(4,N_ELEMENTS(mu_m_w)) t_2 = FLTARR(4) FOR i=0, 3 DO BEGIN t_2[i] = t_1/4. + i * 0.5 * t_1 t_p = concentration * t_2[i] / 100. t_w = (1. - concentration/100.) * t_2[i] OD_p[i,*] = mu_m_w * rho_w * t_w + mu_m_p * rho_p * t_p OD_w[i,*] = mu_m_w * rho_w * t_1 Io[i,*] = INTERPOL(flux(1,*), flux(0,*), energy) ENDFOR ENDIF ELSE BEGIN array = 0 t_p = concentration * t_2 / 100. t_w = (1. - concentration/100.) * t_2 OD_p = mu_m_w * rho_w * t_w + mu_m_p * rho_p * t_p OD_w = mu_m_w * rho_w * t_1 Io = INTERPOL(flux(1,*), flux(0,*), energy) END I_p = Io * Exp(-1. * OD_p) I_w = Io * Exp(-1. * OD_w) I_p_n = FLTARR(4,N_ELEMENTS(mu_m_w)) I_w_n = FLTARR(4,N_ELEMENTS(mu_m_w)) IF array EQ 0 THEN BEGIN image = DBLARR(3,N_Elements(I_p)) image(0,*) = image(0,*) + I_p image(1,*) = image(1,*) + I_p image(2,*) = image(2,*) + I_p IF KEYWORD_SET(add_noise) THEN Add_photon_noise, image I_p = image(1,*) image(0,*) = I_w image(1,*) = I_w image(2,*) = I_w IF KEYWORD_SET(add_noise) THEN Add_photon_noise, image I_w = image(1,*) ENDIF ELSE BEGIN image = DBLARR(3,N_Elements(mu_m_w)) IF KEYWORD_SET(add_noise) THEN BEGIN 106

122 FOR j=0, n DO BEGIN FOR i=0, 3 DO BEGIN image[0,*] = I_p[i,*] image[1,*] = I_p[i,*] image[2,*] = I_p[i,*] Add_photon_noise, image I_p_n[i,*] = I_p_n[i,*] + image[1,*]/float(n) image[0,*] = I_w[i,*] image[1,*] = I_w[i,*] image[2,*] = I_w[i,*] Add_photon_noise, image I_w_n[i,*] = I_w_n[i,*] + image[1,*]/float(n) ENDFOR ENDFOR I_w = I_w_n I_p = I_p_n ENDIF END spectrum = Alog(Double(I_w)/Double(I_p)) IF array EQ 1 THEN BEGIN PLOT, energy, spectrum(0,*), Title='Wet Cell Simulation', xtitle='energy in ev', ytitle='od', $ yrange=[min(spectrum), MAX(spectrum)] OPLOT, energy, spectrum(1,*) OPLOT, energy, spectrum(2,*) OPLOT, energy, spectrum(3,*) ENDIF ELSE BEGIN PLOT, energy, spectrum, Title='Wet Cell Simulation', xtitle='energy in ev', ytitle='od' END PLOT, energy, I_w filename = dialog_pickfile(filter=['*.eps'], /write, /overwrite_prompt, $ title='save plot as ".eps" file') IF (strlen(filename) GT 0) THEN BEGIN just_name = file_basename(filename) IF (strpos(just_name,'.') EQ -1) THEN BEGIN just_name = just_name + '.eps' ENDIF ELSE BEGIN file_extension = $ strmid(just_name,strpos(just_name,'.',/reverse_search)) print, file_extension IF strupcase(file_extension) NE '.EPS' THEN just_name = $ strmid(just_name, 0, strpos(just_name,'.',/reverse_search))+'.eps' END 107

123 Title='Wet Cell Simulation' XTitle='Energy in ev' YTitle='OD' plot1=fltarr(2,n_elements(energy)) plot1(0,*) = energy IF array EQ 1 THEN BEGIN plot1(1,*) = spectrum(0,*) plot2 = FLTARR(2,N_ELEMENTS(energy)) plot2(0,*) = energy plot2(1,*) = spectrum(1,*) plot3 = FLTARR(2,N_ELEMENTS(energy)) plot3(0,*) = energy plot3(1,*) = spectrum(2,*) plot4 = FLTARR(2,N_ELEMENTS(energy)) plot4(0,*) = energy plot4(1,*) = spectrum(3,*) comment='thickness of the Io water layer: '+string(t_1,format='(f5.2)')+'um, of the protein solution: '$ +string(t_2[0],format='(f5.2)')+'um '+string(t_2[1],format='(f5.2)')+'um '+string(t_2[2],format='(f5.2)')+$ 'um '+string(t_2[3],format='(f5.2)')+'um. Protein concentration by mass: '+$ string(concentration, format='(f5.2)')+'%.' Plot_ps, filename, plot1, title, xtitle, ytitle,plot2, plot3, plot4, comment = comment ENDIF ELSE BEGIN plot1(1,*) = spectrum comment='thickness of the Io water layer: '+string(t_1,format='(f5.2)')+'um, of the protein solution: '$ +string(t_2,format='(f5.2)')+' um. Protein concentration by mass: '+string(concentration, format='(f5.2)')+'%.' Plot_ps, filename, plot1, title, xtitle, ytitle, comment = comment END ENDIF END 108

124 Appendix B X-ray Spectromicroscopy on Glycera Dibranchiata 109

125 cluster his (abs) phe (abs) his phe his phe blue 100 % 100 % 100 % 100 % 100 % 100 % yellow 84 % 87 % 79 % 82 % 77 % 79 % green 71 % 102 % 49 % 71 % 53 % 76 % mark 98 % 88 % 102 % 82 % 96 % 77 % Table B.1 Relative histidine and phenylalanine content in the protein matrix (sample A4, #3 to the base), calculated by comparing the oscillator strength of the respective fingerprints in the cluster spectra (Fig. B.4 to Fig. B.6). The abbreviations are abs: comparison of the absolute values of the oscillator strength; : comparison of the oscillator strength normalized to the oscillator strength of the 1s πc=o resonance; **: comparison of the oscillator strength normalized to the overall oscillator strength from ev to ev; mark: the region green region in Fig. B.1, where the spectrum in Fig. B.2 is taken from. Here the comparison of the absolute values of the oscillator strength is a bad measure for the amino acid concentration as the green spectrum as some discrepancy especially for photon energies above the continuum step compared to the other protein spectra. cluster protein density in cm g atacamite density in cm g blue µm µm yellow µm µm violet µm µm Table B.2 Density of protein matrix and atacamite mineral (sample A4, #3, close to the base, Fig. B.7). The protein matrix at the rim of the jaw (yellow) contains more atacamite than the center of the jaw. The violet cluster probably represents a mixture of protein matrix and atacamite. That would explain the rise in carbon density and decrease in atacamite concentration compared to the blue and the yellow cluster. 110

126 close to the tip to the base Figure B.1 STXM image of the sample A4, #3: The gray scale is set the way that regions with high optical density turn white. The dark grid hole in the lower left and the areas in the upper right exhibit epoxy, while the protein matrix of the jaw is is located in the light grid holes in the middle. There is no material in the black regions on the right and therefore the I 0 signal is taken from there (red pixels). The spectrum in Fig. B.2 is determined by calculating the optical density for the green pixels. The greenish rings indicate where stacks are recorded. On the left hand side is the cluster map associated with a stack with 1.0 µm 1.0 µm pixel size. The white bar at the bottom scales to 7 µm. 111