Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations.

TitlePredicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations.
Publication TypeJournal Article
Year of Publication2002
AuthorsGuerois R, Nielsen J E, Serrano L
JournalJ Mol Biol
Date Published2002 Jul 5
KeywordsAlgorithms, Databases as Topic, Hydrogen Bonding, Models, Genetic, Models, Statistical, Mutation, Point Mutation, Protein Conformation, Protein Folding, Protein Structure, Secondary, Proteins, Software, Thermodynamics, Water

We have developed a computer algorithm, FOLDEF (for FOLD-X energy function), to provide a fast and quantitative estimation of the importance of the interactions contributing to the stability of proteins and protein complexes. The predictive power of FOLDEF was tested on a very large set of point mutants (1088 mutants) spanning most of the structural environments found in proteins. FOLDEF uses a full atomic description of the structure of the proteins. The different energy terms taken into account in FOLDEF have been weighted using empirical data obtained from protein engineering experiments. First, we considered a training database of 339 mutants in nine different proteins and optimised the set of parameters and weighting factors that best accounted for the changes in stability of the mutants. The predictive power of the method was then tested using a blind test mutant database of 667 mutants, as well as a database of 82 protein-protein complex mutants. The global correlation obtained for 95 % of the entire mutant database (1030 mutants) is 0.83 with a standard deviation of 0.81 kcal mol(-1) and a slope of 0.76. The present energy function uses a minimum of computational resources and can therefore easily be used in protein design algorithms, and in the field of protein structure and folding pathways prediction where one requires a fast and accurate energy function. FOLDEF is available via a web-interface at

Alternate JournalJ. Mol. Biol.
PubMed ID12079393