Human-Readable Rule Generator for Integrating Amino Acid Sequence Information and Stability of Mutant Proteins

Authors:
Liang-Tsung Huang;Lien-Fu Lai;M. Michael Gromiha
Affiliations:
Mingdao University, Changhua;National Changhua University of Education, Changhua;National Institute of Advanced Industrial Science and Technology (AIST), Tokyo
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2010

Citing 6
Cited 1

C4.5: programs for machine learning

C4.5: programs for machine learning
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning

Machine Learning
Induction of Decision Trees

Machine Learning
A neural-network-based method for predicting protein stability changes upon single point mutations

Bioinformatics
iPTREE-STAB

Bioinformatics

Development of knowledge-based system for predicting the stability of proteins upon point mutations

Neurocomputing

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Abstract

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.