Fast accurate evaluation of protein solvent exposure

Zhang, Naigong; Zeng, Chen; Wingreen, Ned S.

doi:10.1002/prot.20191

Cited by 23 publications

(31 citation statements)

References 18 publications

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“…To calculate the desolvation energy and the screened Coulombic interaction within the computational enzyme design framework, the generalized Born model specifically parameterized for proteins [38] within the CHARMM all-atom force field was modified to be pairwise decomposable based on the generic side chain proposed by Zhang et al [39]. As shown in Fig.…”

Section: Energy Function For Enzyme Designmentioning

confidence: 99%

Computational design of enzyme–ligand binding using a combined energy function and deterministic sequence optimization algorithm

2015

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Enzyme amino-acid sequences at ligand-binding interfaces are evolutionarily optimized for reactions, and the natural conformation of an enzyme-ligand complex must have a low free energy relative to alternative conformations in native-like or non-native sequences. Based on this assumption, a combined energy function was developed for enzyme design and then evaluated by recapitulating native enzyme sequences at ligand-binding interfaces for 10 enzyme-ligand complexes. In this energy function, the electrostatic interaction between polar or charged atoms at buried interfaces is described by an explicitly orientation-dependent hydrogen-bonding potential and a pairwise-decomposable generalized Born model based on the general side chain in the protein design framework. The energy function is augmented with a pairwise surface-area based hydrophobic contribution for nonpolar atom burial. Using this function, on average, 78% of the amino acids at ligand-binding sites were predicted correctly in the minimum-energy sequences, whereas 84% were predicted correctly in the most-similar sequences, which were selected from the top 20 sequences for each enzyme-ligand complex. Hydrogen bonds at the enzyme-ligand binding interfaces in the 10 complexes were usually recovered with the correct geometries. The binding energies calculated using the combined energy function helped to discriminate the active sequences from a pool of alternative sequences that were generated by repeatedly solving a series of mixed-integer linear programming problems for sequence selection with increasing integer cuts.

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Section: Energy Function For Enzyme Designmentioning

confidence: 99%

Computational design of enzyme–ligand binding using a combined energy function and deterministic sequence optimization algorithm

2015

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“…The total area of the protein-protein (P-P) interfaces for each complex was computed (Street & Mayo 1998;Zhang et al 2004) after identification of the residues in each subunit that are engaged in intermolecular contacts. On the other hand, the P-W interfacial tension of each free subunit was computed by numerical integration of equation (2.5) with charge and atomic radii assigned using the program PDB.2PQR (Dolinsky et al 2004).…”

Section: Hot Spots Of Biological Interfacial Tensionmentioning

confidence: 99%

Nanoscale thermodynamics of biological interfacial tension

Fernández

2010

Proc. R. Soc. A.

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The interfacial tension of biological water, a promoter of biomolecular interactions, is difficult to determine because of the inhomogeneous nanoscale patterns that make up the surface of biomolecules. These patterns modulate solubility in peculiar manners, enabling specific associations while preventing phase separation or precipitation. In this work, we derive de novo the nanoscale thermodynamics associated with the creation of biological interfaces and validate the results against experimentally identified complex interfaces. Interfacial tension is shown to be generated by hot spots of red-shifted dielectric relaxation. The most common spots involve hindered polar hydration. Taken collectively, these patches contribute more to the interfacial tension than the better known non-polar cavities with nanometre curvature, where our results agree with the established length-scale dependence of hydrophobicity. The thermodynamic results are validated by showing that the inferred patches of interfacial tension actually promote biomolecular associations.

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“…The described DEE algorithm has been successful in the determination of the lowest energy state for various biophysical systems. [1][2][3][4][5][6][7][8][9][10][11][12] However, for many systems a set of low energy states is functionally relevant. Based on DEE we developed a computational scheme termed X-DEE (extended DEE) that generates a gap-free list of low energy states.…”

Section: Dead-end Eliminationmentioning

confidence: 99%

“…DEE has been successfully applied in protein structure prediction, [1][2][3][4][5][6][7] protein design, [8][9][10] sequence alignment, 11 and also in the evaluation of protein solvent exposure. 12 Proteins, however, are flexible systems that may adopt several functionally important states. To understand their mechanisms it is necessary to obtain a complete picture of the states that are accessible to the protein.…”

Section: Introductionmentioning

confidence: 99%

An extended dead‐end elimination algorithm to determine gap‐free lists of low energy states

2007

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Abstract:Proteins are flexible systems and commonly populate several functionally important states. To understand protein function, these states and their energies have to be identified. We introduce an algorithm that allows the determination of a gap-free list of the low energy states. This algorithm is based on the dead-end elimination (DEE) theorem and is termed X-DEE (extended DEE). X-DEE is applicable to discrete systems whose state energy can be formulated as pairwise interaction between sites and their intrinsic energies. In this article, the computational performance of X-DEE is analyzed and discussed. X-DEE is implemented to determine the lowest energy protonation states of proteins, a problem to which DEE has not been applied so far. We use X-DEE to calculate a list of low energy protonation states for two bacteriorhodopsin structures that represent the first proton transfer step of the bacteriorhodopsin photocycle.

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Fast accurate evaluation of protein solvent exposure

Abstract: Protein solvation energies are often taken to be proportional to solvent-accessible surface areas. Computation of these areas is numerically demanding and may become a bottleneck for folding and design applications.

Cited by 23 publications

References 18 publications

Computational design of enzyme–ligand binding using a combined energy function and deterministic sequence optimization algorithm

Computational design of enzyme–ligand binding using a combined energy function and deterministic sequence optimization algorithm

Nanoscale thermodynamics of biological interfacial tension

An extended dead‐end elimination algorithm to determine gap‐free lists of low energy states

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