Most of the bioinformatics tools developed for predicting mutant protein stability appear as a black box and the relationship between amino acid sequence/structure and stability is hidden to the users. We have addressed this problem and developed a human-readable rule generator for integrating the knowledge of amino acid sequence and experimental stability change upon single mutation. Using information about the original residue, substituted residue, and three neighboring residues, classification rules have been generated to discriminate the stabilizing and destabilizing mutants and explore the basis for experimental data. These rules are human readable, and hence, the method enhances the synergy between expert knowledge and computational system. Furthermore, the performance of the rules has been assessed on a nonredundant data set of 1,859 mutants and we obtained an accuracy of 80 percent using cross validation. The results showed that the method could be effectively used as a tool for both knowledge discovery and predicting mutant protein stability. We have developed a Web for classification rule generator and it is freely available at http://bioinformatics.myweb.hinet.net/irobot.htm.
|Number of pages||7|
|Journal||IEEE/ACM Transactions on Computational Biology and Bioinformatics|
|Publication status||Published - 2010 Jul 2|
All Science Journal Classification (ASJC) codes
- Applied Mathematics