An expert system to predict protein thermostability using decision tree

Li-Cheng Wu, Jian-Xin Lee, Hsien-Da Huang, Baw-Juine Liu, Jorng-Tzong Horng

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

Protein thermostability information is closely linked to commercial production of many biomaterials. Recent developments have shown that amino acid composition, special sequence patterns and hydrogen bonds, disulfide bonds, salt bridges and so on are of considerable importance to thermostability. In this study, we present a system to integrate these various factors that predict protein thermostability. In this study, the features of proteins in the PGTdb are analyzed. We consider both structure and sequence features and correlation coefficients are incorporated into the feature selection algorithm. Machine learning algorithms are then used to develop identification systems and performances between the different algorithms are compared. In this research, two features, (E + F + M + R)/residue and charged/non-charged, are found to be critical to the thermostability of proteins. Although the sequence and structural models achieve a higher accuracy, sequence-only models provides sufficient accuracy for sequence-only thermostability prediction. (C) 2008 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)9007-9014
Number of pages8
JournalExpert Systems with Applications
Volume36
DOIs
StatePublished - Jul 2009

Keywords

  • Expert system; Machine learning; Bioinformatics; Protein thermostability; Decision Tree

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