Biology subjects

Alvarez, B.

Publications and source records attributed to Alvarez, B..

2 recordsLinked to original sources

bioRxiv2018

Footprints of antigen processing boost MHC class II natural ligand binding predictions

Major Histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide:MHC-II binding are trained on binding affinity data, generated in-vitro and therefore lacking information about antigen processing. For the first time, we here describe prediction models of peptide:MHC-II binding trained directly on naturally eluted peptides, and show that these, in addition to peptide binding to the MHC, incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the peptides. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the epitope presented. The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands, and foreshadow a new generation of improved peptide:MHC-II prediction tools of considerable importance for understanding and manipulating immune responses.

immunology↗

bioRxiv2017

Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes

Recent advances in proteomics and mass-spectrometry have widely expanded the detectable peptide repertoire presented by major histocompatibility complex (MHC) molecules on the cell surface, collectively known as the immunopeptidome. Finely characterizing the immunopeptidome brings about important basic insights into the mechanisms of antigen presentation, but can also reveal promising targets for vaccine development and cancer immunotherapy. In this report, we describe a number of practical and efficient approaches to analyze immunopeptidomics data, discussing the identification of meaningful sequence motifs in various scenarios and considering current limitations. We address the issue of filtering false hits and contaminants, and the problem of motif deconvolution in cell lines expressing multiple MHC alleles, both for the MHC class I and class II systems. Finally, we demonstrate how machine learning can be readily employed by non-expert users to generate accurate prediction models directly from mass-spectrometry eluted ligand data sets.

bioinformatics↗