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Average assignment method for predicting the stability of protein mutants
Author(s) -
Saraboji K.,
Gromiha M. Michael,
Ponnuswamy M. N.
Publication year - 2006
Publication title -
biopolymers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 125
eISSN - 1097-0282
pISSN - 0006-3525
DOI - 10.1002/bip.20462
Subject(s) - mutant , chemistry , stability (learning theory) , protein secondary structure , thermal stability , mutation , correlation , biological system , biochemistry , gene , computer science , biology , mathematics , machine learning , organic chemistry , geometry
Prediction of protein stability upon amino acid substitutions is an important problem in molecular biology and it will be helpful for designing stable mutants. In this work, we have analyzed the stability of protein mutants using three different data sets of 1791, 1396, and 2204 mutants, respectively, for thermal stability (Δ T m ), free energy change due to thermal (ΔΔ G ), and denaturant denaturations (ΔΔ G H 2 O ), obtained from the ProTherm database. We have classified the mutants into 380 possible substitutions and assigned the stability of each mutant using the information obtained with similar type of mutations. We observed that this assignment could distinguish the stabilizing and destabilizing mutants to an accuracy of 70–80% at different measures of stability. Further, we have classified the mutants based on secondary structure and solvent accessibility (ASA) and observed that the classification significantly improved the accuracy of prediction. The classification of mutants based on helix, strand, and coil distinguished the stabilizing/destabilizing mutants at an average accuracy of 82% and the correlation is 0.56; information about the location of residues at the interior, partially buried, and surface regions of a protein correctly identified the stabilizing/destabilizing residues at an average accuracy of 81% and the correlation is 0.59. The nine subclassifications based on three secondary structures and solvent accessibilities improved the accuracy of assigning stabilizing/destabilizing mutants to an accuracy of 84–89% for the three data sets. Further, the present method is able to predict the free energy change (ΔΔ G ) upon mutations within a deviation of 0.64 kcal/mol. We suggest that this method could be used for predicting the stability of protein mutants. © 2006 Wiley Periodicals, Inc. Biopolymers 82: 80–92, 2006 This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com