Bruno Alvarez

TL;DR: NetMHCpan-4.1 and NetMHCIIpan- 4.0, two web servers created to predict binding between peptides and M HC-I and MHC-II, respectively, exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors.

TL;DR: The GibbsCluster 2.0 presented here is an improved version incorporating insertion and deletions accounting for variations in motif length in the peptide input, and is used to deconvolute multiple specificities in large-scale peptidome data generated by mass spectrometry.

TL;DR: Improved performance for the prediction of MHC-II ligands and T cell epitopes is demonstrated and a new generation of improved peptide to MHC -II prediction tools accounting for the plurality of factors that determine natural presentation of antigens are foreshadowed.

TL;DR: NNAlign_MA is a computational method designed to address this challenge and fully benefit from large, poly-specific data sets of MS-eluted MHC ligand data, effectively expanding the coverage of alleles for which accurate predictions can be made, resulting in improved identification of both eluted ligands and T-cell epitopes.

TL;DR: A number of practical and efficient approaches to analyze immunopeptidomics data are described, discussing the identification of meaningful sequence motifs in various scenarios and considering current limitations, to generate accurate prediction models directly from mass‐spectrometry eluted ligand data sets.