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dc.contributor.authorWardah, Wafaa
dc.contributor.authorDehzangi, Abdollah
dc.contributor.authorTaherzadeh, Ghazaleh
dc.contributor.authorRashid, Mahmood A
dc.contributor.authorKhan, MGM
dc.contributor.authorTsunoda, Tatsuhiko
dc.contributor.authorSharma, Alok
dc.date.accessioned2020-08-30T21:05:58Z
dc.date.available2020-08-30T21:05:58Z
dc.date.issued2020
dc.identifier.issn0022-5193
dc.identifier.doi10.1016/j.jtbi.2020.110278
dc.identifier.urihttp://hdl.handle.net/10072/396831
dc.description.abstractMotivation: Interactions between proteins and peptides influence biological functions. Predicting such bio-molecular interactions can lead to faster disease prevention and help in drug discovery. Experimental methods for determining protein-peptide binding sites are costly and time-consuming. Therefore, computational methods have become prevalent. However, existing models show extremely low detection rates of actual peptide binding sites in proteins. To address this problem, we employed a two-stage technique - first, we extracted the relevant features from protein sequences and transformed them into images applying a novel method and then, we applied a convolutional neural network to identify the peptide binding sites in proteins. Results: We found that our approach achieves 67% sensitivity or recall (true positive rate) surpassing existing methods by over 35%.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom110278
dc.relation.ispartofjournalJournal of Theoretical Biology
dc.relation.ispartofvolume496
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode31
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsBiology
dc.subject.keywordsMathematical & Computational Biology
dc.subject.keywordsLife Sciences & Biomedicine - Other Topics
dc.titlePredicting protein-peptide binding sites with a deep convolutional neural network
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationWardah, W; Dehzangi, A; Taherzadeh, G; Rashid, MA; Khan, MGM; Tsunoda, T; Sharma, A, Predicting protein-peptide binding sites with a deep convolutional neural network, Journal of Theoretical Biology, 2020, 496, pp. 110278
dcterms.dateAccepted2020-04-08
dcterms.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2020-08-27T01:44:05Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorRashid, Mahmood A.


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