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

Authors:
Liang-Tsung Huang;Lien-Fu Lai;Chao-Chin Wu;M. Michael Gromiha
Affiliations:
Department of Information Communication, Mingdao University, Changhua 523, Taiwan;Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua 500, Taiwan;Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua 500, Taiwan;Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, 2-4-7 Aomi, Koto-ku, Tokyo 13 ...
Venue:
Neurocomputing
Year:
2010

Citing 8
Cited 0

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
Fuzzy weighted averages revisited

Fuzzy Sets and Systems - Information processing
A neural-network-based method for predicting protein stability changes upon single point mutations

Bioinformatics
iPTREE-STAB

Bioinformatics
Sequence Based Prediction of Protein Mutant Stability and Discrimination of Thermophilic Proteins

PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Human-Readable Rule Generator for Integrating Amino Acid Sequence Information and Stability of Mutant Proteins

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Competitiveness of manufacturing firms: an application of fuzzy weighted average

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Comparison of Discrete Algorithms for Fuzzy Weighted Average

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

Prediction of protein stability upon amino acid substitution is an important problem in designing stable proteins. We have developed a classification rule generator for integrating the knowledge of amino acid sequence and experimental stability change upon single mutation. These rules are human readable and hence the method enhances the synergy between expert knowledge and computational system. Utilizing the information about wild type, mutant, three neighboring residues and experimentally observed stability data, we have developed a method based on decision tree for discriminating the stabilizing and destabilizing mutants and predicting the protein stability changes upon single point mutations, which showed an accuracy of 82% and a correlation of 0.70, respectively. In addition, we have developed a fuzzy query method to predict protein stability with partial information. We have developed a web server for predicting the protein stability changes upon single mutations by using fuzzy query mechanism and it is available at http://bioinformatics.myweb.hinet.net/fqstab.htm.