KStable: A Computational Method for Predicting Protein Thermal Stability Changes by K-Star with Regular-mRMR Feature Selection

• Published in: Entropy (Basel, Switzerland) Switzerland), 2018-12, Vol.20 (12), p.988
• Main Authors: Chen, Chi-Wei, Chang, Kai-Po, Ho, Cheng-Wei, Chang, Hsung-Pin, Chu, Yen-Wei
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Amino acids; Datasets; Feature selection; hill-climbing algorithm; machine learning; Methods; Mutation; protein thermostability; Proteins; Redundancy; single-site mutations; Software; Thermal stability
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e20120988

Thermostability is a protein property that impacts many types of studies, including protein activity enhancement, protein structure determination, and drug development. However, most computational tools designed to predict protein thermostability require tertiary structure data as input. The few tools that are dependent only on the primary structure of a protein to predict its thermostability have one or more of the following problems: a slow execution speed, an inability to make large-scale mutation predictions, and the absence of temperature and pH as input parameters. Therefore, we developed a computational tool, named KStable, that is sequence-based, computationally rapid, and includes temperature and pH values to predict changes in the thermostability of a protein upon the introduction of a mutation at a single site. KStable was trained using basis features and minimal redundancy–maximal relevance (mRMR) features, and 58 classifiers were subsequently tested. To find the representative features, a regular-mRMR method was developed. When KStable was evaluated with an independent test set, it achieved an accuracy of 0.708.