Sequence based prediction of protein mutant stability and discrimination of thermophilic proteins

M. Michael Gromiha; Liang Tsung Huang; Lien Fu Lai

doi:10.1007/978-3-540-88436-1-1

Sequence based prediction of protein mutant stability and discrimination of thermophilic proteins

M. Michael Gromiha, Liang Tsung Huang, Lien Fu Lai

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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.

Original language	English
Title of host publication	3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008
Pages	1-12
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-540-88436-1-1
Publication status	Published - 2008 Dec 5
Event	3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008 - Melbourne, VIC, Australia Duration: 2008 Oct 15 → 2008 Oct 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5265 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008
Country	Australia
City	Melbourne, VIC
Period	08-10-15 → 08-10-17

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-88436-1-1

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Gromiha, M. M., Huang, L. T., & Lai, L. F. (2008). Sequence based prediction of protein mutant stability and discrimination of thermophilic proteins. In 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008 (pp. 1-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5265 LNBI). https://doi.org/10.1007/978-3-540-88436-1-1

Gromiha, M. Michael ; Huang, Liang Tsung ; Lai, Lien Fu. / Sequence based prediction of protein mutant stability and discrimination of thermophilic proteins. 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008. 2008. pp. 1-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Gromiha, MM, Huang, LT & Lai, LF 2008, Sequence based prediction of protein mutant stability and discrimination of thermophilic proteins. in 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5265 LNBI, pp. 1-12, 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008, Melbourne, VIC, Australia, 08-10-15. https://doi.org/10.1007/978-3-540-88436-1-1

Sequence based prediction of protein mutant stability and discrimination of thermophilic proteins. / Gromiha, M. Michael; Huang, Liang Tsung; Lai, Lien Fu.

3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008. 2008. p. 1-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5265 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Gromiha MM, Huang LT, Lai LF. Sequence based prediction of protein mutant stability and discrimination of thermophilic proteins. In 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008. 2008. p. 1-12. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-88436-1-1