Bruno Alvarez

This work introduces a deep learning approach using CNNs to discover peptide binding motifs in MHC molecules, showing alignment with experimental motifs and potential applications in vaccine design.

NNAlign_MA deconvolves poly-specific MHC peptidome data to accurately characterize binding motifs and improve T cell epitope predictions.

The paper describes computational approaches for analyzing immunopeptidomics data, focusing on motif identification, false hit filtering, and machine learning applications for predicting MHC ligands.