Literature DB >> 15382246

Fast accurate evaluation of protein solvent exposure.

Naigong Zhang1, Chen Zeng, Ned S Wingreen.   

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. Fast graph-based methods, such as dead-end elimination (DEE), become possible if all energies, including solvation energies, are expressed as single-residue and pair-residue terms. To this end, Street and Mayo originated a pair-residue approximation for solvent-accessible surface areas (Street AG, Mayo SL. Pairwise calculation of protein solvent accessible surface areas. Fold Des 1998;3:253-258). The dominant source of error in this method is the overlapping burial of side-chain surfaces in the protein core. Here we report a new pair-residue approximation, which greatly reduces this overlap error by the use of optimized generic side-chains. We have tested the generic-side-chain method for the ten proteins studied by Street and Mayo and for 377 single-domain proteins from the CATH database (Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM. CATH-A hierarchic classification of protein domain structures. Structure 1997;5:1093-1108). With little additional cost in computation, the new method consistently reduces error for total areas and residue-by-residue areas by more than a factor of two. For example, the residue-by-residue error (for buried area) is reduced from 7.42 A(2) to 3.70 A(2). This difference translates into a solvation energy difference of approximately 0.2 kcal/mol per residue, amounting to a reduction in root-mean-square energy error of 2 kcal/mol for a 100 residue chain, a potentially critical difference for both protein folding and design applications. (c) 2004 Wiley-Liss, Inc.

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Year:  2004        PMID: 15382246     DOI: 10.1002/prot.20191

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  9 in total

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Authors:  Mingyang Lu; Athanasios D Dousis; Jianpeng Ma
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2.  A matching algorithm for catalytic residue site selection in computational enzyme design.

Authors:  Yulin Lei; Wenjia Luo; Yushan Zhu
Journal:  Protein Sci       Date:  2011-07-29       Impact factor: 6.725

3.  A solvated ligand rotamer approach and its application in computational protein design.

Authors:  Xiaoqiang Huang; Ji Yang; Yushan Zhu
Journal:  J Mol Model       Date:  2012-11-29       Impact factor: 1.810

4.  Systematic optimization model and algorithm for binding sequence selection in computational enzyme design.

Authors:  Xiaoqiang Huang; Kehang Han; Yushan Zhu
Journal:  Protein Sci       Date:  2013-06-06       Impact factor: 6.725

5.  Expanded explorations into the optimization of an energy function for protein design.

Authors:  Yao-Ming Huang; Christopher Bystroff
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2013 Sep-Oct       Impact factor: 3.710

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

Authors:  Ye Tian; Xiaoqiang Huang; Yushan Zhu
Journal:  J Mol Model       Date:  2015-07-11       Impact factor: 1.810

7.  Solvent accessible surface area approximations for rapid and accurate protein structure prediction.

Authors:  Elizabeth Durham; Brent Dorr; Nils Woetzel; René Staritzbichler; Jens Meiler
Journal:  J Mol Model       Date:  2009-02-21       Impact factor: 1.810

8.  Explicit orientation dependence in empirical potentials and its significance to side-chain modeling.

Authors:  Jianpeng Ma
Journal:  Acc Chem Res       Date:  2009-08-18       Impact factor: 22.384

9.  Sub-nanoscale surface ruggedness provides a water-tight seal for exposed regions in soluble protein structure.

Authors:  Erica Schulz; Marisa Frechero; Gustavo Appignanesi; Ariel Fernández
Journal:  PLoS One       Date:  2010-09-17       Impact factor: 3.240

  9 in total

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