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Structural analysis and prediction of protein mutant stability using distance and torsion potentials: Role of secondary structure and solvent accessibility
Author(s) -
Parthiban Vijaya,
Gromiha M. Michael,
Hoppe Christian,
Schomburg Dietmar
Publication year - 2007
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21115
Subject(s) - correlation , jackknife resampling , torsion (gastropod) , protein stability , protein secondary structure , mathematics , denaturation (fissile materials) , biological system , chemistry , statistics , biology , geometry , biochemistry , zoology , estimator , nuclear chemistry
Analyzing the factors behind protein stability is a key research topic in molecular biology, and has direct implications on protein structure prediction and protein–protein interactions. We have analyzed protein stability upon point mutations using a distance‐dependant pair potential representing mainly through‐space interactions, and torsion angle potential representing mainly neighboring effects as a basic statistical mechanical setup for the analysis. The synergetic effect of accessible surface area and secondary structure preferences was used as a classifier for the potentials. In addition, short‐, medium‐, and long‐range interactions of the protein environment were also analyzed. Two datasets of point mutations were taken for the comparison of theoretically predicted stabilizing energy values with experimental ΔΔ G and ΔΔ G H 2 O from thermal and chemical denaturation experiments. These include 1538 and 1603 mutations, respectively, and contain 101 proteins that share a wide range of sequence identity. The resulting force fields were carefully evaluated with different statistical tests. Results show a maximum correlation of 0.87 with a standard error of 0.71 kcal/mol between predicted and measured ΔΔ G values and a prediction accuracy of 85.3% (stabilizing or destabilizing) for all mutations together. A correlation of 0.77 (more than 80% prediction accuracy with a standard error of 0.95 kcal/mol) each for the test dataset of split‐sample validation and fivefold crossvalidation was obtained and a correlation of 0.70 (77.4% prediction accuracy with a standard error of 1.17 kcal/mol) was shown by the jackknife test. The same model was implemented, and the results were analyzed for mutations with ΔΔ G H 2 O. A correlation of 0.78 (standard error 0.96 kcal/mol) was observed with a prediction efficiency of 84.65%. This model can be used for the future prediction of protein structural stability together with various experimental techniques. Proteins 2007. © 2006 Wiley‐Liss, Inc.