Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool Articles uri icon

authors

  • WERT CARVAJAL, CARLOS
  • SÁNCHEZ GARCÍA, RUBÉN
  • MACÍAS, JOSÉ R.
  • SANZ PAMPLONA, REBECA
  • MÉNDEZ PÉREZ, ALMUDENA
  • ALEMANY, RAMON
  • VEIGA, ESTEBAN
  • SORZANO, CARLOS ÓSCAR S.
  • MUÑOZ BARRUTIA, MARIA ARRATE

publication date

  • May 2021

start page

  • 1

end page

  • 10

issue

  • 1

volume

  • 11

International Standard Serial Number (ISSN)

  • 2045-2322

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 http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section.