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dc.contributor.authorTaherzadeh, Ghazaleh
dc.contributor.authorYang, Yuedong
dc.contributor.authorXu, Haodong
dc.contributor.authorXue, Yu
dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.authorZhou, Yaoqi
dc.date.accessioned2019-06-07T01:44:04Z
dc.date.available2019-06-07T01:44:04Z
dc.date.issued2018
dc.identifier.issn0192-8651
dc.identifier.doi10.1002/jcc.25353
dc.identifier.urihttp://hdl.handle.net/10072/382167
dc.description.abstractMalonylation is a recently discovered post‐translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT‐Mal, is developed to predict malonylation sites by employing sequence and predicted structural features. Evolutionary information and physicochemical properties are found to be the two most discriminative features whereas a structural feature called half‐sphere exposure provides additional improvement to the prediction performance. SPRINT‐Mal trained on mouse data yields robust performance for 10‐fold cross validation and independent test set with Area Under the Curve (AUC) values of 0.74 and 0.76 and Matthews’ Correlation Coefficient (MCC) of 0.213 and 0.20, respectively. Moreover, SPRINT‐Mal achieved comparable performance when testing on H. sapiens proteins without species‐specific training but not in bacterium S. erythraea. This suggests similar underlying physicochemical mechanisms between mouse and human but not between mouse and bacterium. SPRINT‐Mal is freely available as an online server at: http://sparks-lab.org/server/SPRINT-Mal/. © 2018 Wiley Periodicals, Inc.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherJohn Wiley & Sons
dc.publisher.placeUnited States
dc.relation.ispartofpagefrom1757
dc.relation.ispartofpageto1763
dc.relation.ispartofissue22
dc.relation.ispartofjournalJournal of Computational Chemistry
dc.relation.ispartofvolume39
dc.subject.fieldofresearchPhysical chemistry
dc.subject.fieldofresearchTheoretical and computational chemistry
dc.subject.fieldofresearchTheoretical and computational chemistry not elsewhere classified
dc.subject.fieldofresearchNanotechnology
dc.subject.fieldofresearchcode3406
dc.subject.fieldofresearchcode3407
dc.subject.fieldofresearchcode340799
dc.subject.fieldofresearchcode4018
dc.subject.keywordsSupport vector machines
dc.subject.keywordsLysine‐malonylation sites prediction
dc.subject.keywordsPost translational modification
dc.titlePredicting lysine-malonylation sites of proteins using sequence and predicted structural features
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung


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