Next Article in Journal
Complex Behavior of Nano-Scale Tribo-Ceramic Films in Adaptive PVD Coatings under Extreme Tribological Conditions
Next Article in Special Issue
Drug-Drug Interaction Extraction via Recurrent Hybrid Convolutional Neural Networks with an Improved Focal Loss
Previous Article in Journal
Uniform Convergence of Cesaro Averages for Uniquely Ergodic C*-Dynamical Systems
Previous Article in Special Issue
Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Entropy 2018, 20(12), 988; https://doi.org/10.3390/e20120988

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

1
Department of Computer Science and Engineering, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan
2
Institute of Genomics and Bioinformatics, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan
3
Ph.D. Program in Medical Biotechnology, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan
4
China Medical University Hospital, No. 2, Yude Rd., Taichung 404, Taiwan
5
Biotechnology Center, Agricultural Biotechnology Center, Institute of Molecular Biology, Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan
*
Author to whom correspondence should be addressed.
Received: 21 November 2018 / Revised: 11 December 2018 / Accepted: 16 December 2018 / Published: 19 December 2018
(This article belongs to the Special Issue Information Theoretic Learning and Kernel Methods)
Full-Text   |   PDF [356 KB, uploaded 19 December 2018]   |  
  |   Review Reports

Abstract

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. View Full-Text
Keywords: protein thermostability; single-site mutations; machine learning; feature selection; hill-climbing algorithm protein thermostability; single-site mutations; machine learning; feature selection; hill-climbing algorithm
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Chen, C.-W.; Chang, K.-P.; Ho, C.-W.; Chang, H.-P.; Chu, Y.-W. KStable: A Computational Method for Predicting Protein Thermal Stability Changes by K-Star with Regular-mRMR Feature Selection. Entropy 2018, 20, 988.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top