An expert system to predict protein thermostability using decision tree

  • Authors:

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

  • Affiliations:

  • Institute of System Biology and Bioinformatics, National Central University, Taiwan;Department of Computer Science and Information Engineering, National Central University, Taiwan;Institute of Bioinformatics, National Chiao-Tung University, Taiwan;Computer Science and Information Engineering, Yuan Ze University, Taiwan;Institute of System Biology and Bioinformatics, National Central University, Taiwan and Department of Computer Science and Information Engineering, National Central University, Taiwan and Departme ...

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

Quantified Score

Hi-index 12.05

Visualization

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.