{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:08:23Z","timestamp":1760242103321,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,19]],"date-time":"2018-12-19T00:00:00Z","timestamp":1545177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["106-2221-E-005-077-MY2,107-2634-F-005-002 and 107-2321-B-005 -013"],"award-info":[{"award-number":["106-2221-E-005-077-MY2,107-2634-F-005-002 and 107-2321-B-005 -013"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Chung Hsing University and Chung-Shan Medical University","award":["NCHU-CSMU-10705"],"award-info":[{"award-number":["NCHU-CSMU-10705"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2013maximal 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.<\/jats:p>","DOI":"10.3390\/e20120988","type":"journal-article","created":{"date-parts":[[2018,12,19]],"date-time":"2018-12-19T12:12:44Z","timestamp":1545221564000},"page":"988","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["KStable: A Computational Method for Predicting Protein Thermal Stability Changes by K-Star with Regular-mRMR Feature Selection"],"prefix":"10.3390","volume":"20","author":[{"given":"Chi-Wei","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan"},{"name":"Institute of Genomics and Bioinformatics, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai-Po","family":"Chang","sequence":"additional","affiliation":[{"name":"Ph.D. Program in Medical Biotechnology, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan"},{"name":"China Medical University Hospital, No. 2, Yude Rd., Taichung 404, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng-Wei","family":"Ho","sequence":"additional","affiliation":[{"name":"Institute of Genomics and Bioinformatics, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsung-Pin","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5525-4011","authenticated-orcid":false,"given":"Yen-Wei","family":"Chu","sequence":"additional","affiliation":[{"name":"Institute of Genomics and Bioinformatics, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan"},{"name":"Ph.D. Program in Medical Biotechnology, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan"},{"name":"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"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"i63","DOI":"10.1093\/bioinformatics\/bth928","article-title":"A neural-network-based method for predicting protein stability changes upon single point mutations","volume":"20","author":"Capriotti","year":"2004","journal-title":"Bioinformatics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1002\/prot.20400","article-title":"Neural network-based prediction of mutation-induced protein stability changes in staphylococcal nuclease at 20 residue positions","volume":"59","author":"Frenz","year":"2005","journal-title":"Proteins Struct. 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