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Title: Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool
Author: Wert Carvajal, Carlos
Sánchez García, Rubén
Macías, José R
Sanz-Pamplona, Rebeca
Méndez Pérez, Almudena
Alemany Bonastre, Ramon
Veiga, Esteban
Sorzano, Carlos Óscar S.
Muñoz Barrutia, Arrate
Keywords: Càncer
Cèl·lules T
T cells
Issue Date: 24-May-2021
Publisher: Springer Science and Business Media LLC
Abstract: Lack 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 (NAP-CNB) 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 with scripts and datasets accessible through the download section.
Note: Reproducció del document publicat a:
It is part of: Scientific Reports, 2021, vol. 11, num. 10780
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Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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