Publication:
Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool

dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: BSEL - Laboratorio de Ciencia e Ingeniería Biomédicaes
dc.contributor.authorWert Carvajal, Carlos
dc.contributor.authorSánchez García, Rubén
dc.contributor.authorMacías, José R.
dc.contributor.authorSanz Pamplona, Rebeca
dc.contributor.authorMéndez Pérez, Almudena
dc.contributor.authorAlemany, Ramon
dc.contributor.authorVeiga, Esteban
dc.contributor.authorSánchez Sorzano, Carlos Óscar
dc.contributor.authorMuñoz Barrutia, María Arrate
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2022-05-16T10:37:16Z
dc.date.available2022-05-16T10:37:16Z
dc.date.issued2021-05-24
dc.description.abstractLack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope binding affinity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAPCNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC comparable or superior to state-of-the-art methods, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the system’s predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http:// bioco mp. cnb. csic. es/ Neoan tigen sApp/ with scripts and datasets accessible through the download section.en
dc.description.sponsorshipThis work was funded by the Spanish Ministry of Economy, Industry and Competitiveness (TEC2016-28052-R, RTC2017-6600-1, SAF2017-84091-R, BFERO2020.04), the Spanish Ministry of Science and Innovation (FPU18/03199, PID2019-109820RB-I00), the “la Caixa” Foundation (LCF/BQ/EU19/11710071), FERO foundation and Centro Superior de Investigaciones Científicas (JAEINT18/EX/0636).en
dc.format.extent10
dc.identifier.bibliographicCitationWert-Carvajal, C., Sánchez-García, R., Macías, J. R., Sanz-Pamplona, R., Pérez, A. M., Alemany, R., Veiga, E., Sorzano, C. Ó. S., & Muñoz-Barrutia, A. (2021). Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool. In Scientific Reports (Vol. 11, Issue 1). Springer Science and Business Media LLC.en
dc.identifier.doihttps://doi.org/10.1038/s41598-021-89927-5
dc.identifier.issn2045-2322
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue1
dc.identifier.publicationlastpage10
dc.identifier.publicationtitleScientific Reportsen
dc.identifier.publicationvolume11
dc.identifier.urihttps://hdl.handle.net/10016/34809
dc.identifier.uxxiAR/0000030533
dc.language.isoengen
dc.publisherSpringer Science and Business Media LLCen
dc.relation.projectIDGobierno de España. PID2019-109820RB-I00es
dc.relation.projectIDGobierno de España. TEC2016-28052-Res
dc.relation.projectIDGobierno de España. RTC2017-6600-1es
dc.relation.projectIDGobierno de España. SAF2017-84091-Res
dc.relation.projectIDGobierno de España. BFERO2020.04es
dc.relation.projectIDGobierno de España. FPU18/03199es
dc.rights© The Author(s) 2021en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaBiología y Biomedicinaes
dc.titlePredicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online toolen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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