NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T cell epitope predictions

REYNISSON, BIRKIR; TERNETTE, NICOLA; NIELSEN, MORTEN; BARRA, CAROLINA; CONNELLEY, TIM; ALVAREZ, BRUNO; BUUS, SØREN; ANDREATTA, MASSIMO

MOLECULAR & CELLULAR PROTEOMICS

AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC

Lugar: Bethesda, Maryland; Año: 2019

1535-9476

The set of peptides presented on a cell?s surface by MHC molecules is known as the immunopeptidome. Currentmass spectrometry technologies allow for identification of large peptidomes, and studies have proven these data tobe a rich source of information for learning the rules of MHC-mediated antigen presentation. Immunopeptidomesare usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in thesystem under investigation. Motif deconvolution -the process of associating each ligand to its presenting MHCmolecule(s)- is therefore a critical and challenging step in the analysis of MS-eluted MHC ligand data. Here, wedescribe NNAlign_MA, a computational method designed to address this challenge and fully benefit from large,poly-specific data sets of MS-eluted ligands. NNAlign_MA simultaneously performs the tasks of i) clusteringpeptides into individual specificities; ii) automatic annotation of each cluster to an MHC molecule; and iii) trainingof a prediction model covering all MHCs present in the training set. NNAlign_MA was benchmarked on large anddiverse datasets, covering class I and class II data. In all cases, the method was demonstrated to outperform state-ofthe-art methods, effectively expanding the coverage of alleles for which accurate predictions can be made, resultingin improved identification of both eluted ligands and T cell epitopes. Given its high flexibility and ease of use, weexpect NNAlign_MA to serve as an effective tool to increase our understanding of the rules of MHC antigenpresentation and guide the development of novel T cell-based therapeutics.