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

  • M. Michael Gromiha
  • Liang-Tsung Huang
  • Lien-Fu Lai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5265)

Abstract

Prediction of protein stability upon amino acid substitution and discrimination of thermophilic proteins from mesophilic ones are important problems in designing stable proteins. We have developed a classification rule generator using the information about wild-type, mutant, three neighboring residues and experimentally observed stability data. Utilizing the rules, we have developed a method based on decision tree for discriminating the stabilizing and destabilizing mutants and predicting protein stability changes upon single point mutations, which showed an accuracy of 82% and a correlation of 0.70, respectively. In addition, we have systematically analyzed the characteristic features of amino acid residues in 3075 mesophilic and 1609 thermophilic proteins belonging to 9 and 15 families, respectively, and developed methods for discriminating them. The method based on neural network could discrimi-nate them at the 5-fold cross-validation accuracy of 89% in a dataset of 4684 proteins and 91% in a test set of 707 proteins.

Keywords

Protein stability rule generator discrimination prediction thermophilic proteins neural network machine learning techniques 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • M. Michael Gromiha
    • 1
  • Liang-Tsung Huang
    • 2
  • Lien-Fu Lai
    • 3
  1. 1.Computational Biology Research Center (CBRC)National Institute of Advanced, Industrial Science and Technology (AIST)TokyoJapan
  2. 2.Department of Computer Science and Information EngineeringMingDao UniversityTaiwan
  3. 3.Department of Computer Science and Information EngineeringNational Changhua University of EducationTaiwan

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