article

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

Authors:

Liang-Tsung Huang,

M. Michael GromihaAuthors Info & Claims

Neurocomputing, Volume 73, Issue 13-15

Pages 2293 - 2299

https://doi.org/10.1016/j.neucom.2010.02.020

Published: 01 August 2010 Publication History

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.

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Cited By

Wu CLai LGromiha MHuang L(2014)High throughput computing to improve efficiency of predicting protein stability change upon mutationInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2014.06401110:2(206-224)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1504/IJDMB.2014.064011

Development of knowledge-based system for predicting the stability of proteins upon point mutations
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Published In

cover image Neurocomputing

Neurocomputing Volume 73, Issue 13-15

August, 2010

507 pages

ISSN:0925-2312

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2010.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 August 2010

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Cited By

Wu CLai LGromiha MHuang L(2014)High throughput computing to improve efficiency of predicting protein stability change upon mutationInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2014.06401110:2(206-224)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1504/IJDMB.2014.064011

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