PhytoAFP: In Silico Approaches for Designing Plant-Derived Antifungal Peptides

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dc.contributor.authorTyagi, Atulcs
dc.contributor.authorRoy, Sudeepcs
dc.contributor.authorSingh, Sanjaycs
dc.contributor.authorSemwal, Manojcs
dc.contributor.authorShasany, Ajitcs
dc.contributor.authorSharma, Ashokcs
dc.contributor.authorProvazník, Valentýnacs
dc.coverage.issue7cs
dc.coverage.volume10cs
dc.date.issued2021-07-05cs
dc.description.abstractEmerging infectious diseases (EID) are serious problems caused by fungi in humans and plant species. They are a severe threat to food security worldwide. In our current work, we have developed a support vector machine (SVM)-based model that attempts to design and predict therapeutic plant-derived antifungal peptides (PhytoAFP). The residue composition analysis shows the preference of C, G, K, R, and S amino acids. Position preference analysis shows that residues G, K, R, and A dominate the N-terminal. Similarly, residues N, S, C, and G prefer the C-terminal. Motif analysis reveals the presence of motifs like NYVF, NYVFP, YVFP, NYVFPA, and VFPA. We have developed two models using various input functions such as mono-, di-, and tripeptide composition, as well as binary, hybrid, and physiochemical properties, based on methods that are applied to the main data set. The TPC-based monopeptide composition model achieved more accuracy, 94.4%, with a Matthews correlation coefficient (MCC) of 0.89. Correspondingly, the second-best model based on dipeptides achieved an accuracy of 94.28% under the MCC 0.89 of the training dataset.en
dc.formattextcs
dc.format.extent1-12cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationAntibiotics. 2021, vol. 10, issue 7, p. 1-12.en
dc.identifier.doi10.3390/antibiotics10070815cs
dc.identifier.issn2079-6382cs
dc.identifier.orcid0000-0001-9569-3273cs
dc.identifier.orcid0000-0002-7825-0152cs
dc.identifier.orcid0000-0002-3422-7938cs
dc.identifier.other173151cs
dc.identifier.researcheridF-4121-2012cs
dc.identifier.scopus000000278250152cs
dc.identifier.scopus6701729526cs
dc.identifier.urihttp://hdl.handle.net/11012/203026
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofAntibioticscs
dc.relation.urihttps://www.mdpi.com/2079-6382/10/7/815cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2079-6382/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectplant defensinsen
dc.subjectinnate immunityen
dc.subjecthost defense peptidesen
dc.subjectantimicrobial peptidesen
dc.titlePhytoAFP: In Silico Approaches for Designing Plant-Derived Antifungal Peptidesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-173151en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:39:51en
sync.item.modts2025.01.17 18:47:44en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
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