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dc.contributor.authorLv, Xuan
dc.contributor.authorChen, Jianwen
dc.contributor.authorLu, Yutong
dc.contributor.authorChen, Zhiguang
dc.contributor.authorXiao, Nong
dc.contributor.authorYang, Yuedong
dc.date.accessioned2020-08-25T04:27:15Z
dc.date.available2020-08-25T04:27:15Z
dc.date.issued2020
dc.identifier.issn1549-9596
dc.identifier.doi10.1021/acs.jcim.0c00064
dc.identifier.urihttp://hdl.handle.net/10072/396748
dc.description.abstractAccurately predicting the impact of point mutation on protein stability has crucial roles in protein design and engineering. In this study, we proposed a novel method (BoostDDG) to predict stability changes upon point mutations from protein sequences based on the extreme gradient boosting. We extracted features comprehensively from evolutional information and predicted structures and performed feature selection by a strategy of sequential forward selection. The features and parameters were optimized by homologue-based cross-validation to avoid overfitting. Finally, we found that 14 features from six groups led to the highest Pearson correlation coefficient (PCC) of 0.535, which is consistent with the 0.540 on an independent test. Our method was indicated to consistently outperform other sequence-based methods on three precompiled test sets, and 7363 variants on two proteins (PTEN and TPMT). These results highlighted that BoostDDG is a powerful tool for predicting stability changes upon point mutations from protein sequences.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherAmerican Chemical Society (ACS Publications)
dc.relation.ispartofpagefrom2388
dc.relation.ispartofpageto2395
dc.relation.ispartofissue4
dc.relation.ispartofjournalJournal of Chemical Information and Modeling
dc.relation.ispartofvolume60
dc.subject.fieldofresearchMedicinal and biomolecular chemistry
dc.subject.fieldofresearchTheoretical and computational chemistry
dc.subject.fieldofresearchcode3404
dc.subject.fieldofresearchcode3407
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsPhysical Sciences
dc.subject.keywordsChemistry, Medicinal
dc.titleAccurately Predicting Mutation-Caused Stability Changes from Protein Sequences Using Extreme Gradient Boosting
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationLv, X; Chen, J; Lu, Y; Chen, Z; Xiao, N; Yang, Y, Accurately Predicting Mutation-Caused Stability Changes from Protein Sequences Using Extreme Gradient Boosting, Journal of Chemical Information and Modeling, 2020, 60 (4), pp. 2388-2395
dc.date.updated2020-08-25T04:26:00Z
gro.hasfulltextNo Full Text
gro.griffith.authorYang, Yuedong


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