Protein Modeling. Generating, Evaluating and Refining Protein Homology Models

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1 Protein Modeling Generating, Evaluating and Refining Protein Homology Models Troy Wymore and Kristen Messinger Biomedical Initiatives Group Pittsburgh Supercomputing Center

2 Homology Modeling of Proteins Need for comparative modeling of proteins Computational techniques for generating, evaluating and refining structures. Example of interfacing computational chemistry with sequence based bioinformatics

3 The Need for Predictive Methods The amino acid sequences of more than a 1.2 million (March 2003) proteins have been provided by the various genome projects and the gap between sequence determination and structure determination continues to grow. This fact increases the need for protein structure predictive methods. Structure determination by x-ray crystallography or NMR is still relatively difficult.

4 Structural Genomics Estimated that approx. 1/3 of all sequences are recognizably related to at least one known structure Known protein sequences > 1.2 million Compared to 20,000 currently known structures Homology Modeling could then provide ~400,000 structures

5 What Is Homology Modeling? Predicts the three-dimensional structure of a given protein sequence (TARGET) based on an alignment to one or more known protein structures (TEMPLATES) If similarity between the TARGET sequence and the TEMPLATE sequence is detected, structural similarity can be assumed. In general, 30% sequence identity is required for generating useful models

6 Applications of Homology Modeling Sequence Identity % Comparable to medium resolution NMR Substrate Specificity Docking of Small Ligands, proteins 30-60% Molecular replacement in crystallography Support site-directed mutagenesis through visualization Marti-Renom et al. Annu. Rev. Biophys. Biomol. Struct., 2000, 29:

7 Procedures for Comparative Protein Modeling Start End Yes Identify templates No Model ok? Select templates Evaluate the model Align target with template Build the model

8 Programs for Model Protein Construction MODELLER 6.0» guitar.rockefeller.edu/modeller/modeller.html SWISS-MOD Server» SCWRL (SideChain placement With Rotamer Library)» *obviously not all inclusive

9 Locating Domains Sequences of more than 500 amino acids are almost certain to be divided into domains. Finding homologues may be easier if you can separate the sequence into domains. Regions of low complexity often separate domains in multidomain proteins

10 Finding templates Sequence based Threading Ab initio Consensus

11 Results Page Red letters represent helices and blue letters represent sheets If you scroll to the end of the sequence an alignment in PIR format and and initial model in PDB format are available

12

13 Factors to Consider in Selecting Templates Template environment» ph» Ligands present? Resolution of the templates Family of proteins» Phylogenetic tree construction can help find the subfamily closest to the target sequence Multiple templates?

14 Target-Template Alignment No current comparative modeling method can recover from an incorrect alignment Use multiple sequence alignments as initial guide. Consider slightly alternative alignments in areas of uncertainty, build multiple models Sequence-Structure alignment programs» MODELLER command ALIGN_2D (not tested)» Tries to put gaps in variable regions/loops Note: Sequence from database versus sequence from the actual PDB are not always identical (S2C)

15 Differences in Multiple Sequence Alignments 0 * 240 * 260 * 280 * 1ad3 : LKPSEVSGHMADLLATLIPQY-M---DQNLYLVVKGGVPETTELLK--ERFDHIMYTGSTAVGKIVMAAAAK- : 200 1cw3 : MKVAEQTPLTALYVANLIKEAGF---PPGVVNIVPGFGPTAGAAIASHEDVDKVAFTGSTEIGRVIQVAAGSS : 254 1ad3_4 : LKPSEVSGHMADLLATLIPQY-M---DQNLYLVVKGGVPETTELL--KERFDHIMYTGSTAVGKIV-MAAAAK : 200 1cw3_4 : MKVAEQTPLTALYVANLIKEAGF---PPGVVNIVPGFGPTAGAAIASHEDVDKVAFTGSTEIGRVIQVAAGSS : 254 1ad3_5 : LKPSEVSGHMADLLATLIPQY-M---DQNLYLVVKGGVPETTELLKER--FDHIMYTGSTAVGKIV-MAAAAK : 200 1cw3_5 : MKVAEQTPLTALYVANLIKEAGF---PPGVVNIVPGFGPTAGAAIASHEDVDKVAFTGSTEIGRVIQVAAGSS : 254 1ad3_6 : LKPSEVSGHMADLLATLIPQY-M---DQNLYLVVKGGVPETTELLKER--FDHIMYTGSTAVGKIV-MAAAAK : 200 1cw3_6 : MKVAEQTPLTALYVANLIKEAGF---PPGVVNIVPGFGPTAGAAIASHEDVDKVAFTGSTEIGRVIQVAAGSS : 254 1ad3_ce : LKPSEVSGHMADLLATLIPQYM----DQNLYLVVKGGV-PETTELLKE-RFDHIMYTGSTAVGKIVMAAAA-K : 200 1cw3_ce : MKVAEQT---PLTALYVANLIKEAGFPPGVVNIVPGFGPTAGAAIASHEDVDKVAFTGSTEIGRVIQVAAGSS : 254 6K E 3 a a 6i 6 6V G p 6 D 6 5TGST 6G466 AA Helix Sheet Turn Inserting gap at ends of helix versus in the middle When gaps are placed at the ends of helices, all models from these alignments resulted in RMSD versus actual of Å. In another helical region, placing them in the middle results in RMSD of ~ 2.0 Å versus less than 1.0 Å for correct alignment.

16 Alignment of variable regions * 380 * 400 * 420 * 4 1ad3 : ---EDAKQSRDYGRIINDRHFQRVKGLIDNQK------VAHGGTWDQSSRYIAPTILVDVDPQSPVMQEEIFG : 335 1cw3 : ---NPFDSKTEQGPQVDETQFKKILGYINTGKQEGAKLLCGGGIAADRGYFIQPTVFGDVQDGMTIAKEEIFG : 396 1ad3_4 : ---EDAKQSRDYGRIINDRHFQRVKGLIDNQK------VAHGGTWDQSSRYIAPTILVDVDPQSPVMQEEIFG : 335 1cw3_4 : ---NPFDSKTEQGPQVDETQFKKILGYINTGKQEGAKLLCGGGIAADRGYFIQPTVFGDVQDGMTIAKEEIFG : 396 1ad3_5 : ---EDAKQSRDYGRIINDRHFQRVKGLIDNQK------VAHGGTWDQSSRYIAPTILVDVDPQSPVMQEEIFG : 335 1cw3_5 : ---NPFDSKTEQGPQVDETQFKKILGYINTGKQEGAKLLCGGGIAADRGYFIQPTVFGDVQDGMTIAKEEIFG : 396 1ad3_6 : ---EDAKQSRDYGRIINDRHFQRVKGLI-----DNQKVAHGGTW-DQSSRYIAPTILVDVDPQSPVMQEEIFG : 335 1cw3_6 : ---NPFDSKTEQGPQVDETQFKKILGYINTGKQEGAKLLCGGGIAADRGYFIQPTVFGDVQDGMTIAKEEIFG : 396 1ad3_ce : AKQ-----SRDYGRIINDRHFQRVKGLID-----NQKVAHGGTWD-QSSRYIAPTILVDVDPQSPVMQEEIFG : 335 1cw3_ce : VVGNPFDSKTEQGPQVDETQFKKILGYINTGKQEGAKLLCGGGIAADRGYFIQPTVFGDVQDGMTIAKEEIFG : 396 G 61 F 46 G I k Gg 5I PT6 DV 6 EEIFG Placement of gaps in variable regions does not affect the quality of the models. Residues of 1cw3 produce the following RMSDs (Å). CE-4.065, clustal-4.424, pileup-4.128, SAGA 4.613, MSA The range of values for SAGA, clustal and pileup represent differences due to MODELLER procedures. The poor quality points out the need to use other methods to model loops.

17 Secondary Structure Prediction Ø Predator Ø Predict Protein Server Ø Ø SSP & NNSSP (NNSSP uses neural networks) Ø Ø predictioncenter.llnl.gov/other/interesting.html

18 Other experimental techniques Is there any other experimental data published that could aid in this model construction/alignment?» Fluorescense» Circular Dichroism» Electron microscopy» Site-directed mutagenesis Modeller command» ADD_RESTRAINT

19 Constructing Multi-domain protein models Building a multi-domain protein using templates corresponding to the individual domains proteina proteinb Target aaaaaaaaaaaaa bbbbbbbbbbbbbbb aaaaaaaaaaaaabbbbbbbbbbbbbbb

20 Effect of sequence similarity Sequence Identity w/ 1cw3 RMSCD over domains 1bi9 67% < 1.0? 1ad3 27% > 2.0? We even tried using the sequence alignment from structure RMSCD (root mean square coordinate difference) over Ca atoms.

21 Multiple model approach Reminder: Consider the effects of different substitution matrices, different gap penalties, and different algorithms. (Vogt et al. J. Mol. Biol. 1995, 249: ) Construct multiple models Use structural analysis programs to determine best model Jaroszewski, Pawlowski and Godsik, J. Molecular Modeling, 1998, 4: Venclovas, Ginalski and Fidelis. PROTEINS, 1999, 3:73-80 (Suppl)

22 Initial model and procedures Calculate coordinates for atoms that have equivalent atoms in the templates as an average over all templates CHARMM internal coordinates are used for remaining unknown coordinates Generate stereochemical and homology derived restraints

23 Spatial restraints? MODELLER minimizes the objective function, F, with respect to the Cartesian coordinates of the protein atoms F(R) = Sc i (f i,p i ) R are the cartesian coordinates of the atoms c is a restraint dependant on f,p f is a geometric feature of a molecule and include the distance, angle and dihedral values p are parameters to help describe some restraints

24 Molecular Mechanics The final stage of most Homology Modeling procedures involves an energy minimization of the protein structure by a molecular mechanics (MM) force field MM force field has a term for bond stretching, angle bending, dihedral angle torsions, and nonbonded contacts (van der Waals and electrostatics) V= bonds 1/2 κ b (b-b 0 ) 2 + angles 1/2 κ θ (θ-θ 0 ) 2 + torsion κ φ [1 + cos(nϕ - δ)] + LJ 4ε ij [(σ ij /r) 12 - (σ ij /r) 6 ] + Coulomb q i q j / εr

25 SWISS-MOD Server BLAST search to determine templates Superposition of 3D templates by SIM-structural alignment program based on diagonals of sequence similarity. Similar algorithm used for the target-template alignment Conserved areas built in similar fashion as other programs Non-conserved loops taken from PDB database search Sidechains from rotamer library Model refined with MM using GROMACS.

26 SWISS-MOD examples Modeling class 2 Aldehyde Dehydrogenase (1cw3) using class 3 ALDH as a template resulted in the following Å rmsd versus actual over the coenzyme domain Å rmsd versus actual over the catalytic domain This should be compared to the CE alignment that produced a 2.0 Å rmsd model. The CE alignment closely corresponds to multiple sequence alignment of ALDHs.

27 Clustering the ensemble Cluster analysis, based on overall fold, followed by selection of the structure closest to the centroid of the largest cluster is likely to identify a structure more representative of the ensemble than the commonly used minimized average structure NMRCLUST (

28 Errors in Homology Modeling a) Side chain packing b)distortions and shifts c) no template

29 Errors in Homology Modeling d) Misalignments e) incorrect template Marti-Renom et al., Ann. Rev. Biophys. Biomol. Struct., 2000, 29:

30 PROCHECK roman/procheck/procheck.html

31 PROSAII Z-score, Z p, of an amino acid sequence is derived from the energy E p of the aa sequence Z p = (E p E)/s E = average energy of all fragments s = standard deviation Potentials provided by applying Boltzmann s principle to structural databank Sippl, M., PROTEINS, 1993, 17:

32 ProsaII Aspartate Aminotransferase Most accurate x-ray structure (bold dotted line) has best z-score.

33 ProsaII M4 apo-lactate dehydrogenase 3ldh (dotted line) has an incorrect amino acid sequence

34 VERIFY3D Nature, 1992, 356:

35 Strategies for Molecular Mechanics Refinement Restrain the region of the model protein that is more likely correct and just minimize or do simulated annealing on the suspect areas Protein Force Fields (AMBER, CHARMM) have been parameterized to reproduce solvent phase properties. They perform best when the use of explicit solvent molecules are used to solvate the structure. Other issues include the effect of long range electrostatics, etc. Explicit solvent simulations to refine structures are expensive and perhaps prohibitive, so.

36 Generalized Born in MM Replace water molecules with a term that describes the interaction of an atom with the solvent. Can be seen as modifying the non-bonded electrostatic term. Not distance-dependant dielectric (too inaccurate), not Poisson-Boltzmann ( I think, still too expensive), but Generalized Born Dominy, B. and C. L. Brooks, J. Phys. Chem. B., 1999, 103: (CHARMM) Onufriev, A., Bashford, D. and D. A. Case, J. Phys. Chem. B, 2000, 104: (AMBER)

37 Modeling of loops Loops often determine the functional specificity of a given protein framework. Contribute to active and binding sites Conformation of loop is influenced by the core stem regions that span the loop and other surrounding residues Several methods for performing loop refinement (too numerous to mention all) Database search techniques Ab initio methods

38 Loop Database Approach Define the query as the residues that make up the loop plus the main chain atoms that are anchored to secondary structure elements. Search through the PDB using the following criteria Geometric (Does the conformation span the loop?) Sequence Similarity Applicable to loops 7 residues long or shorter

39 Modeling ß-secretase involved in Alzheimer s Disease Explains the substrate specificity of this enzyme for negatively charged residue of amyloid precursor protein Sauder, J., Arthur, J. W., and R. L. Dunbrack, J. Mol. Biol., 2000, 200:

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