Supporting Information

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1 Supporting Information Copyright Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, 2013 Expansion of Access Tunnels and Active-Site Cavities Influence Activity of Haloalkane Dehalogenases in Organic Cosolvents Veronika Stepankova, [a, b] Morteza Khabiri, [c, d] Jan Brezovsky, [a] Antonin Pavelka, [a] Jan Sykora, [e] Mariana Amaro, [e] Babak Minofar, [c] Zbynek Prokop, [a] Martin Hof, [e] Rudiger Ettrich, [c, d] Radka Chaloupkova,* [a] [a, b] and Jiri Damborsky* cbic_ _sm_miscellaneous_information.pdf

2 (A) (B) (C) Figure S1. Steady-state kinetics of (A) DbjA, (B) DhaA and (C) LinB measured in buffer, formamide 5% v/v, acetone 20% v/v and isopropanol 10% v/v. Thin line shows the nonlinear regression fit of a particular kinetic model.. 1

3 Figure S2. Solvation of the main tunnel and active site of (A) DbjA (green), (B) DhaA (blue) and (C) LinB (yellow) by water molecules. Relative solvation describes a ratio between volume of the main tunnel or the active site occupied by water molecules and the total volume of the main tunnel and the active site. 2

4 Mean residue ellipticity (10 3 deg cm 2 mol -1 ) Intensity (10 3 CPU) Mean residue ellipticity (10 3 deg cm 2 mol -1 ) Intensity (10 3 CPU) Mean residue ellipticity (10 3 deg cm 2 mol -1 ) Intensity (10 3 CPU) (A) 16 buffer (D) 800 buffer 11 6 isopropanol 10% acetone 20% 600 formamide 5% (B) Wavelength buffer (nm) 11 isopropanol 10% acetone 20% 6 (E) Wavelength buffer (nm) formamide 5% (C) buffer (F) buffer 11 6 isopropanol 10% acetone 20% 600 formamide 5% Wavelength (nm) Wavelength (nm) Figure S3. CD spectra of (A) DbjA, (B) DhaA and (C) LinB measured at 25 C in buffer, isopropanol 10% (v/v) and acetone 20% (v/v). Fluorescence spectra of (D) DbjA, (E) DhaA and (F) LinB were measured at 25 C in buffer and formamide 5% (v/v). 3

5 Figure S4. Correlation functions for the kinetics of the time dependent fluorescence shift obtained for Coumarin labelled DhaA in buffer (black) and in acetone 20% v/v (red). Apparently, a significantly higher amount of the time dependent fluorescence shift occurs on the time scale shorter than 30 picoseconds in acetone compared to the pure buffer, while nanosecond kinetics appears to be almost identical for both systems. 4

6 Table S1. Physicochemical properties of selected organic solvents. Solvent M w, Da M v, Å 3 logp η, cp µ, D ε * α, Å 3 DC Formamide Acetone Isopropanol M w molecular weight, M v molar volume, logp partition coefficient, η - viscosity, µ dipole moment, ε dielectric constant, α polarizability, DC denaturation capacity; * determined at ambient temperature (15-30 C); determined at temperature 25 C. Table S2. Overall characteristics of HLDs structures obtained from MD simulations. Enzyme DbjA DhaA LinB Solvent RMSD, Å Radius of gyration, Å Total B-factors, Å 2 Solvent accessible surface area, Å 2 Water 1.30 ± ± ± 13 13,700 ± 200 Formamide 1.20 ± ± ± 10 13,800 ± 200 Acetone 1.50 ± ± ± 10 14,000 ± 300 Isopropanol 1.70 ± ± ± 10 14,000 ± 200 Water 1.10 ± ± ± 27 13,700 ± 100 Formamide 1.10 ± ± ± 19 13,900 ± 200 Acetone 1.20 ± ± ± 36 13,700 ± 200 Isopropanol 1.20 ± ± ± 17 13,700 ± 400 Water 1.40 ± ± ± 10 13,800 ± 200 Formamide 1.40 ± ± ± 10 13,900 ± 250 Acetone 1.40 ± ± ± 16 14,400 ± 300 Isopropanol 1.10 ± ± ± 10 14,000 ± 200 The errors represent standard errors. Table S3. Average volumes of the active sites and the main tunnels of HLDs. Enzyme DbjA DhaA Region water, Å 3 formamide, Å 3 acetone, Å 3 isopropanol, Å 3 Main tunnel ± ± ± ± 1.2 Active site ± ± ± ± 1.2 Main tunnel 68.0 ± ± ± ± 1.3 Active site ± ± ± ± 1.1 Main tunnel 99.9 ± ± ± ± 1.1 LinB Active site ± ± ± ± 1.1 The errors represent standard errors. 5

7 Table S4. Average volumes of the active site and main tunnel of DbjA, DhaA and LinB occupied by water molecules. Enzyme DbjA DhaA Region water, Å 3 formamide, Å 3 acetone, Å 3 isopropanol, Å 3 Main tunnel 77.2 ± ± ± ± 0.3 Active site ± ± ± ± 0.6 Main tunnel 23.8 ± ± ± ± 0.5 Active site 86.0 ± ± ± ± 0.4 Main tunnel 41.5 ± ± ± ± 0.6 LinB Active site ± ± ± ± 1.0 The errors represent standard errors. Table S5. Average volumes of the active sites and the main tunnels of HLDs occupied by molecules of organic co-solvents. Enzyme DbjA DhaA Region water, Å 3 formamide, Å 3 acetone, Å 3 isopropanol, Å 3 Main tunnel -* 69.4 ± ± ± 0.9 Active site -* ± ± ± 0.3 Main tunnel -* 23.5 ± ± ± 0.6 Active site -* 26.1 ± ± ± 0.7 Main tunnel -* 36.0 ± ± ± 0.7 LinB Active site -* 32.8 ± ± ± 1.1 The errors represent standard errors. * not applicable. Table S6. Parameters gained from TDFS of DhaA labelled with Coumarin specifically located in the tunnel mouth leading to the active site in buffer and acetone 20% (v/v). DhaA ν, cm - 1 ν 0, cm -1 ν, τ r, cm -1 ns % obs τ 1, ns (A 1,cm -1 ) τ 2, ns (A 2,cm -1 ) τ 3, ns (A 3,cm -1 ) τ 4, ns (A 4,cm -1 ) Buffer < 0.1 (90) 0.1 (340) 1.7 (170) 15.0 (340) Acetone < 0.1 (240) 0.1 (180) 1.8 (230) 15.1 (350) ν - maximum of the hypothetical time resolved emission spectrum (TRES) extrapolated to time infinity, ν 0 - emission maximum of time 0 estimation, ν - overall Stokes shift, τ r - relaxation time, % obs - fraction of the TDFS which occurred on a time scale slower than 30 ps, τ i and A i were obtained by fitting the time course of the maxima of the time resolved emission spectra ν(t) including the value gained by time 0 estimation to the formula: v(t) = v + ia iexp(-t/ i), where v = ia i. 6

8 Algorithms for pocket identification and pocket volume estimation The atoms of a protein structure are represented by spheres of their van der Waals radii. The algorithms are based on testing if a sphere is empty, i.e. disjoint with the spheres of a protein. Pockets are represented by a collection of empty spheres of radius r_probe placed on cubical grid. In the next two sections, algorithms for pocket identification and pocket volume estimation will be described. The third section contains their detailed technical specification. Algorithm S1. Pocket identification The algorithm uses a depth-first search of the cubic grid for two purposes. First is to identify a starting point and second to estimate space, which is accessible from the starting point and at the same time lies in the user-specified spherical constraint. The constraining sphere center is specified by a radius r_c and by the center S computed as an average position of centers of atoms A. First, an attempt to find the empty sphere of radius m times greater than r_probe, and center closer than dist_start from S is made by a grid search. If no such sphere is found, m is decreased by i and the search is repeated. This continues until desired empty sphere was found or m is smaller or equal to one. In the second case, a pocket of zero volume is reported. Otherwise, the center of the empty sphere is reported as starting point for pocket body identification. The pocket body is approximated by a set of empty spheres of probe radius r_probe on a grid of a resolution d. We say two grid points are adjacent if their distance is equal to the grid resolution, and both lies on a line parallel with an axis of coordinate system. Two spheres are adjacent if their centers lie on adjacent grid points. First, only a sphere placed at the starting point makes the pocket. Repeatedly, new spheres adjacent to spheres already classified as pocket spheres are inspected, and if they are empty and inside the constraining sphere, they are also classified as pocket spheres. Algorithm S2. Pocket volume estimation Volumes of a space enclosed at the same time by spherical constraint and (i) solvent accessible surface (SA) or (ii) molecular surface (MS) are estimated. SA volume is computed as a product of volume of a cell of a grid of resolution d and the number of spheres representing the pocket. To compute the MS volume, a new grid with resolution d_v is defined inside a rectangular box. The volume is then computed as a product of the volume of a cell of the grid, and the number of grid points contained in at least one sphere representing the pocket body. Furthermore, the MS volume is split into volumes occupied by specific type of solvent molecules and free space. This division is achieved by testing if a grid point lies under van der Waals surface of a solvent molecule. 7

9 Algorithm 1. Pocket identification. Input r_probe radius of the sphere used to probe pocket space A set of atoms defining the pocket P set of spheres representing protein atoms r_c radius of sphere limiting search space d - distance between neighboring points in grid for pocket identification dist_start maximal distance of averaged location of A and center of starting probe, i.e. how far the grid is searched to find pocket space m multiplier of probe radius that is used in the attempt to find space of radius m-times as big as solvent mimicking probe i - value by which the value of m is decreased if starting probe of radius m * r_probe was not found Output V centers of spheres whose union creates the body of a pocket function neighbours(p): return 6 points created by moving the point p along each axis of coordinates to both directions by distance d; function collides(probe): if probe intersects sphere from P: return true; else: return false; end collides. function poll(q): return first element from first in, first out queue, the element is removed from the queue; end poll. function compute_starting_probe(r): // r starting probe radius S := average position of atoms from A; C := sphere of radius r_c and center S; Q := empty first in, first out queue; // grid points to expand V := empty hash table; // stores already visited points o := averaged position of centers of atoms from A; add o to Q; add o to V; while Q is not empty: p := poll(q); dist := distance between p and o if dist < dist_search: N := neighbours(p) for each q from N: if (q is not in V) and (q is inside C): probe := sphere with center q and radius r; if collides(probe): add q to Q; add q to V; else: return q; end for; end while; return null; // nothing was found end compute_starting_probe. 8

10 starting_probe := null; while (starting_probe is null) and (1.0 <= m) starting_probe := compute_starting_probe(r_probe * m); m := m - i; end while; if starting_probe is not null: // starting probe was found V := empty hash table; // stores already visited points Q := empty first in, first out queue; // grid points to expand add starting_probe to Q; add starting_probe to V; while Q is not empty: p := poll(q); N := neighbours(p) for each q from N: if q is not in V: probe := sphere with center q and radius r_probe if (probe is inside S) and (not collides(probe)): add q to Q; add q to V; end for; end while; return V; // centers of all probes representing volume of pocket else: return empty set; // pocket not found 9

11 Algorithm 2. Pocket volume estimation. Input r radius of the spheres representing the pocket body P centers of the spheres, pocket body is union of spheres of center from P and equal radius r d_v - distance between neighboring points in the grid for volume estimation Output V = {v_0, v_1, v_2,, v_s} // volumes of the space in a pocket occupied by // free space and solvents 1, 2,, s N = {n_0, n_1, n_2,, n_s} // number of grid points in each space // initialized to zeros function evaluate_volume(): B := minimal box encapsulating pocket body; (x, y, z) := the corner of B with the minimal coordinates; for each point p := (x + i * d_v, y + j * d_v, z + k * d_v) within B: if p lies in any sphere of pocket: if p lies in solvent molecule named m: n_m := n_m + 1; else: n_0 := n_0 + 1; end for; for i := 0 to s: v_i := n_i * d_v * d_v * d_v; return V; end evaluate_volume. Implementation The algorithms were implemented in Java using a standard Java HashSet class for hash table and KD tree library written by Simon D. Levy downloaded from for fast detection of sphere collisions, and to accelerate decision whether a point lies within a sphere. 10

CD Basis Set of Spectra that is used is that derived from comparing the spectra of globular proteins whose secondary structures are known from X-ray

CD Basis Set of Spectra that is used is that derived from comparing the spectra of globular proteins whose secondary structures are known from X-ray crystallography An example of the use of CD Modeling