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dc.contributor.authorDehzangi, Abdollah
dc.contributor.authorLopez, Yosvany
dc.contributor.authorLal, Sunil Pranit
dc.contributor.authorTaherzadeh, Ghazaleh
dc.contributor.authorMichaelson, Jacob
dc.contributor.authorSattar, Abdul
dc.contributor.authorTsunoda, Tatsuhiko
dc.contributor.authorSharma, Alok
dc.date.accessioned2017-07-09T23:06:16Z
dc.date.available2017-07-09T23:06:16Z
dc.date.issued2017
dc.identifier.issn0022-5193
dc.identifier.doi10.1016/j.jtbi.2017.05.005
dc.identifier.urihttp://hdl.handle.net/10072/341632
dc.description.abstractPost-translational modification (PTM) is a covalent and enzymatic modification of proteins, which contributes to diversify the proteome. Despite many reported PTMs with essential roles in cellular functioning, lysine succinylation has emerged as a subject of particular interest. Because its experimental identification remains a costly and time-consuming process, computational predictors have been recently proposed for tackling this important issue. However, the performance of current predictors is still very limited. In this paper, we propose a new predictor called PSSM-Suc which employs evolutionary information of amino acids for predicting succinylated lysine residues. Here we described each lysine residue in terms of profile bigrams extracted from position specific scoring matrices. We compared the performance of PSSM-Suc to that of existing predictors using a widely used benchmark dataset. PSSM-Suc showed a significant improvement in performance over state-of-the-art predictors. Its sensitivity, accuracy and Matthews correlation coefficient were 0.8159, 0.8199 and 0.6396, respectively.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom97
dc.relation.ispartofpageto102
dc.relation.ispartofjournalJournal of Theoretical Biology
dc.relation.ispartofvolume425
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode089999
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.titlePSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorSattar, Abdul
gro.griffith.authorSharma, Alok
gro.griffith.authorTaherzadeh, Ghazaleh


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