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

Current mass spectrometry technologies allow for identification of MHC-associated peptide ligands. Studies have proven these data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. Such immunopeptidomes are usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in the system under investigation. Motif deconvolution -the process of associating each ligand to its presenting MHC molecule(s)- is a critical and challenging step in the analysis of MS-eluted MHC ligand data. Here, we describe NNAlign_MA, a computational method designed to address this challenge. NNAlign_MA simultaneously performs the tasks of i) clustering peptides into individual specificities; ii) automatic annotation of each cluster to an MHC molecule; and iii) training of a prediction model covering all MHCs present in the training set. NNAlign_MA was benchmarked on large and diverse datasets, covering class I and class II data. In all cases, the method was demonstrated to outperform state-of-the-art methods, effectively expanding the coverage of alleles for which accurate predictions can be made, resulting in improved identification of both eluted ligands and T cellepitopes. Given its high flexibility and ease of use, we expect NNAlign_MA to serve as an effective tool to increase our understanding of the rules of MHC antigen presentation and guide the development of novel T cell-based therapeutics.