Prediction of peptides binding to MHC class I and II alleles by temporal motif mining

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dc.WoS.categoriesBiochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biologyen_US
dc.authorid0000-0002-9253-8152en_US
dc.contributor.authorOtu, Hasan H.
dc.contributor.authorMeydan, Cem
dc.contributor.authorSezerman, Osman Uğur
dc.date.accessioned2021-02-25T08:37:58Z
dc.date.available2021-02-25T08:37:58Z
dc.date.issued2013-01-21
dc.description.abstractBackground: MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently. Results: The prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II. Conclusions: Temporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths.en_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.1186/1471-2105-14-S2-S13en_US
dc.identifier.issn1471-2105
dc.identifier.pmid23368521en_US
dc.identifier.scopus2-s2.0-84884193260en_US
dc.identifier.urihttps://hdl.handle.net/11411/3298
dc.identifier.urihttps://doi.org/10.1186/1471-2105-14-S2-S13
dc.identifier.wosWOS:000314468200013en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors3en_US
dc.publisherBmcen_US
dc.relation.ispartofBmc Bioinformaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectT-CELL EPITOPESen_US
dc.subjectSURVIVALen_US
dc.subjectLIGANDSen_US
dc.subjectTOOLSen_US
dc.titlePrediction of peptides binding to MHC class I and II alleles by temporal motif miningen_US
dc.typeArticleen_US
dc.volume14en_US

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