title: Structural analysis and prediction of protein mutant stability using distance and torsion potentials: role of secondary structure and solvent accessibility.
abstract: 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 DeltaDeltaG and DeltaDeltaGH(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 DeltaDeltaG 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 DeltaDeltaGH(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.
authors.affiliation: Cologne University Bioinformatics Center, International Max Planck Research School, Cologne, Germany.
authors.forename: Vijaya
authors.initials: V
authors.lastname: Parthiban
authors.forename: M Michael
authors.initials: MM
authors.lastname: Gromiha
authors.forename: Christian
authors.initials: C
authors.lastname: Hoppe
authors.forename: Dietmar
authors.initials: D
authors.lastname: Schomburg
mesh_terms: D030562:Databases, Protein; D015233:Models, Statistical; D017354:Point Mutation; D011489:Protein Denaturation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry / Q000235:genetics*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012997:Solvents / Q000737:chemistry*; D013816:Thermodynamics
chemical_list: D011506:Proteins; D012997:Solvents