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dc.contributor.authorLi, Yunqi
dc.contributor.authorFang, Jianwen
dc.date.accessioned2014-03-19T18:15:48Z
dc.date.available2014-03-19T18:15:48Z
dc.date.issued2012-10-15
dc.identifier.citationLi, Y., & Fang, J. (2012). PROTS-RF: A Robust Model for Predicting Mutation-Induced Protein Stability Changes. PLoS ONE, 7(10). http://dx.doi.org/10.1371/journal.pone.0047247
dc.identifier.urihttp://hdl.handle.net/1808/13261
dc.description.abstractThe ability to improve protein thermostability via protein engineering is of great scientific interest and also has significant practical value. In this report we present PROTS-RF, a robust model based on the Random Forest algorithm capable of predicting thermostability changes induced by not only single-, but also double- or multiple-point mutations. The model is built using 41 features including evolutionary information, secondary structure, solvent accessibility and a set of fragment-based features. It achieves accuracies of 0.799,0.782, 0.787, and areas under receiver operating characteristic (ROC) curves of 0.873, 0.868 and 0.862 for single-, double- and multiple- point mutation datasets, respectively. Contrary to previous suggestions, our results clearly demonstrate that a robust predictive model trained for predicting single point mutation induced thermostability changes can be capable of predicting double and multiple point mutations. It also shows high levels of robustness in the tests using hypothetical reverse mutations. We demonstrate that testing datasets created based on physical principles can be highly useful for testing the robustness of predictive models.
dc.publisherPublic Library of Science
dc.rights©Li and Fang. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectForecasting
dc.subjectFree energy
dc.subjectMachine learning algorithms
dc.subjectMutation
dc.subjectPoint mutation
dc.subjectProtein structure
dc.subjectProtein structure predicition
dc.subjectReverse mutation
dc.titlePROTS-RF: A Robust Model for Predicting Mutation-Induced Protein Stability Changes
dc.typeArticle
kusw.kuauthorLi, Yunqi
kusw.kuauthorFang, Jainwen
kusw.kudepartmentMolecular Biosciences
kusw.oastatusfullparticipation
dc.identifier.doi10.1371/journal.pone.0047247
kusw.oaversionScholarly/refereed, publisher version
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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©Li and Fang. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as: ©Li and Fang. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.