A new analytical method for computing solvent‐accessible surface area of macromolecules and its gradients

Hayryan, Shura; Hu, Chin–Kun; Skřivánek, J.; Hayryane, Edik; Pokorný, Imrich

doi:10.1002/jcc.20125

Cited by 47 publications

(50 citation statements)

References 45 publications

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“…We compute α k ðfr i gÞ and ∂αk ðfri gÞ ∂~r i using the procedure in ref. 62. Note that MTM does not assume whether the protein folds by a two-state or multistate mechanism.…”

mentioning

confidence: 99%

Collapse kinetics and chevron plots from simulations of denaturant-dependent folding of globular proteins

Liu

Reddy

O’Brien

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

202

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Quantitative description of how proteins fold under experimental conditions remains a challenging problem. Experiments often use urea and guanidinium chloride to study folding whereas the natural variable in simulations is temperature. To bridge the gap, we use the molecular transfer model that combines measured denaturant-dependent transfer free energies for the peptide group and amino acid residues, and a coarse-grained C α -side chain model for polypeptide chains to simulate the folding of src SH 3 domain. Stability of the native state decreases linearly as ½C (the concentration of guanidinium chloride) increases with the slope, m, that is in excellent agreement with experiments. Remarkably, the calculated folding rate at ½C ¼0 is only 16-fold larger than the measured value. Most importantly ln k obs (k obs is the sum of folding and unfolding rates) as a function of ½C has the characteristic V (chevron) shape. In every folding trajectory, the times for reaching the native state, interactions stabilizing all the substructures, and global collapse coincide. The value of m f m (m f is the slope of the folding arm of the chevron plot) is identical to the fraction of buried solvent accessible surface area in the structures of the transition state ensemble. In the dominant transition state, which does not vary significantly at low ½C , the core of the protein and certain loops are structured. Besides solving the long-standing problem of computing the chevron plot, our work lays the foundation for incorporating denaturant effects in a physically transparent manner either in all-atom or coarse-grained simulations.kinetic cooperativity | self-organized polymer model | pathway diversity | protein denaturation U nderstanding how proteins fold in quantitative molecular detail can provide insights into protein aggregation and dynamics of formation of multisubunit complexes. As a result there have been intense efforts in deciphering the folding mechanisms of proteins using a variety of experiments (1-6), theories (7-13), and simulations (14-16). Despite these advances, a number of issues such as the denaturant-dependent characteristics of the unfolded states and the link between protein collapse and folding remain unclear (17-21). Single molecule experiments have provided clear evidence for folding pathway diversity, and have shown that polypeptide chains undergo almost continuous collapse as the denaturant concentration (½C ) is decreased (1,(19)(20)(21). These and other experiments (5, 22) raise the need for computational models whose predictions can be directly compared to experiments, which often use denaturants [guanidinium chloride (GdmCl) and urea] to initiate folding and unfolding.Global thermodynamic and kinetic behavior of small proteins often exhibit two-state behavior (23), which implies that at all values of ½C only the population of native (N) and the unfolded (U) states are detectable. Experimentally two-state behavior is characterized by (i) the free energy of stability, ΔG NU ð½C Þ ð¼ G N ð½C Þ − G U ð½C ...

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“…We compute α k ðfr i gÞ and ∂αk ðfri gÞ ∂~r i using the procedure in ref. 62. Note that MTM does not assume whether the protein folds by a two-state or multistate mechanism.…”

mentioning

confidence: 99%

Collapse kinetics and chevron plots from simulations of denaturant-dependent folding of globular proteins

Liu

Reddy

O’Brien

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

202

View full text Add to dashboard Cite

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“…A small value of SASA means that the residue tends to be in the hydrophobic environment, otherwise, it tends to be in the hydrophilic environment. There are many programs for calculating SASA 30., 33., most of them are based on the atom coordinates submitted by users. In this research, a freely available program POPS ( 30 ) was used.…”

Section: Methodsmentioning

confidence: 99%

A Contact Energy Function Considering Residue Hydrophobic Environment and Its Application in Protein Fold Recognition

Duan

Zhou

2005

Genomics, Proteomics &Amp; Bioinformatics

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The three-dimensional (3D) structure prediction of proteins is an important task in bioinformatics. Finding energy functions that can better represent residue-residue and residue-solvent interactions is a crucial way to improve the prediction accuracy. The widely used contact energy functions mostly only consider the contact frequency between different types of residues; however, we find that the contact frequency also relates to the residue hydrophobic environment. Accordingly, we present an improved contact energy function to integrate the two factors, which can reflect the influence of hydrophobic interaction on the stabilization of protein 3D structure more effectively. Furthermore, a fold recognition (threading) approach based on this energy function is developed. The testing results obtained with 20 randomly selected proteins demonstrate that, compared with common contact energy functions, the proposed energy function can improve the accuracy of the fold template prediction from 20% to 50%, and can also improve the accuracy of the sequence-template alignment from 35% to 65%.

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“…Since the intrinsic volume of a protein contains two parts, i.e., the contact surface, which is the part of the Van der Waals surface contacting the solvent molecules; and the re-entrant surface, which connects the contact surfaces when one solvent molecule is contacting more than one atom of a protein (Connolly, 1983;Totrov and Abagyan, 1996;Hayryan et al, 2005).…”

Section: Interpretation Of Compressibilitymentioning

confidence: 99%

Solvent effects on the molecular structures of crude gliadins as revealed by density and ultrasound velocity measurements

Zhang

Scanlon

2011

Journal of Cereal Science

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A new analytical method for computing solvent‐accessible surface area of macromolecules and its gradients

Cited by 47 publications

References 45 publications

Collapse kinetics and chevron plots from simulations of denaturant-dependent folding of globular proteins

Collapse kinetics and chevron plots from simulations of denaturant-dependent folding of globular proteins

A Contact Energy Function Considering Residue Hydrophobic Environment and Its Application in Protein Fold Recognition

Solvent effects on the molecular structures of crude gliadins as revealed by density and ultrasound velocity measurements

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