Wert Carvajal, CarlosSánchez García, RubénMacías, José R.Sanz Pamplona, RebecaMéndez Pérez, AlmudenaAlemany, RamonVeiga, EstebanSánchez Sorzano, Carlos ÓscarMuñoz Barrutia, María Arrate2022-05-162022-05-162021-05-24Wert-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.2045-2322http://hdl.handle.net/10016/34809Lack 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.10eng© The Author(s) 2021Atribución 3.0 Españaresearch articleBiología y Biomedicinahttps://doi.org/10.1038/s41598-021-89927-5open access1110Scientific Reports11AR/0000030533