dc.contributor.author | Wardah, Wafaa | |
dc.contributor.author | Dehzangi, Abdollah | |
dc.contributor.author | Taherzadeh, Ghazaleh | |
dc.contributor.author | Rashid, Mahmood A | |
dc.contributor.author | Khan, MGM | |
dc.contributor.author | Tsunoda, Tatsuhiko | |
dc.contributor.author | Sharma, Alok | |
dc.date.accessioned | 2020-08-30T21:05:58Z | |
dc.date.available | 2020-08-30T21:05:58Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 0022-5193 | |
dc.identifier.doi | 10.1016/j.jtbi.2020.110278 | |
dc.identifier.uri | http://hdl.handle.net/10072/396831 | |
dc.description.abstract | Motivation: 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.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Elsevier | |
dc.relation.ispartofpagefrom | 110278 | |
dc.relation.ispartofjournal | Journal of Theoretical Biology | |
dc.relation.ispartofvolume | 496 | |
dc.subject.fieldofresearch | Mathematical sciences | |
dc.subject.fieldofresearch | Biological sciences | |
dc.subject.fieldofresearchcode | 49 | |
dc.subject.fieldofresearchcode | 31 | |
dc.subject.keywords | Science & Technology | |
dc.subject.keywords | Life Sciences & Biomedicine | |
dc.subject.keywords | Biology | |
dc.subject.keywords | Mathematical & Computational Biology | |
dc.subject.keywords | Life Sciences & Biomedicine - Other Topics | |
dc.title | Predicting protein-peptide binding sites with a deep convolutional neural network | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dcterms.bibliographicCitation | Wardah, 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.dateAccepted | 2020-04-08 | |
dcterms.license | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.date.updated | 2020-08-27T01:44:05Z | |
dc.description.version | Accepted 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.hasfulltext | Full Text | |
gro.griffith.author | Rashid, Mahmood A. | |