Biology subjects

Andreatta, M.

Publications and source records attributed to Andreatta, M..

3 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↗

bioRxiv2017

NetMHCpan 4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data

Cytotoxic T cells are of central importance in the immune systems response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC (major histocompatibility complex) class I molecules. Peptide binding to MHC molecules is the single most selective step in the antigen presentation pathway. On the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has therefore attracted large attention.\n\nIn the past, predictors of peptide-MHC interaction have in most cases been trained on binding affinity data. Recently an increasing amount of MHC presented peptides identified by mass spectrometry has been published containing information about peptide processing steps in the presentation pathway and the length distribution of naturally presented peptides. Here, we present NetMHCpan-4.0, a method trained on both binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increased predictive performance compared to state-of-the-art when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.

bioinformatics↗