Benchmarking Bioinformatics Pipelines in Data-Independent Acquisition Mass Spectrometry for Immunopeptidomics.

Immunopeptidomes are the peptide repertoires bound by the molecules encoded by the major histocompatibility complex [human leukocyte antigen (HLA) in humans]. These HLA-peptide complexes are presented on the cell surface for immune T-cell recognition. Immunopeptidomics denotes the utilization of tandem mass spectrometry to identify and quantify peptides bound to HLA molecules. Data-independent acquisition (DIA) has emerged as a powerful strategy for quantitative proteomics and deep proteome-wide identification; however, DIA application to immunopeptidomics analyses has so far seen limited use. Further, of the many DIA data processing tools currently available, there is no consensus in the immunopeptidomics community on the most appropriate pipeline(s) for in-depth and accurate HLA peptide identification. Herein, we benchmarked four commonly used spectral library-based DIA pipelines developed for proteomics applications (Skyline, Spectronaut, DIA-NN, and PEAKS) for their ability to perform immunopeptidome quantification. We validated and assessed the capability of each tool to identify and quantify HLA-bound peptides. Generally, DIA-NN and PEAKS provided higher immunopeptidome coverage with more reproducible results. Skyline and Spectronaut conferred more accurate peptide identification with lower experimental false-positive rates. All tools demonstrated reasonable correlations in quantifying precursors of HLA-bound peptides. Our benchmarking study suggests a combined strategy of applying at least two complementary DIA software tools to achieve the greatest degree of confidence and in-depth coverage of immunopeptidome data.

Thermostability profiling of MHC-bound peptides: a new dimension in immunopeptidomics and aid for immunotherapy design.

The features of peptide antigens that contribute to their immunogenicity are not well understood. Although the stability of peptide-MHC (pMHC) is known to be important, current assays assess this interaction only for peptides in isolation and not in the context of natural antigen processing and presentation. Here, we present a method that provides a comprehensive and unbiased measure of pMHC stability for thousands of individual ligands detected simultaneously by mass spectrometry (MS). The method allows rapid assessment of intra-allelic and inter-allelic differences in pMHC stability and reveals profiles of stability that are broader than previously appreciated. The additional dimensionality of the data facilitated the training of a model which improves the prediction of peptide immunogenicity, specifically of cancer neoepitopes. This assay can be applied to any cells bearing MHC or MHC-like molecules, offering insight into not only the endogenous immunopeptidome, but also that of neoepitopes and pathogen-derived sequences.

Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data.

Major histocompatibility complex (MHC) class II antigen presentation is a key component in eliciting a CD4+ T cell response. Precise prediction of peptide-MHC (pMHC) interactions has thus become a cornerstone in defining epitope candidates for rational vaccine design. Current pMHC prediction tools have, so far, primarily focused on inference from in vitro binding affinity. In the current study, we collate a large set of MHC class II eluted ligands generated by mass spectrometry to guide the prediction of MHC class II antigen presentation. We demonstrate that models developed on eluted ligands outperform those developed on pMHC binding affinity data. The predictive performance can be further enhanced by combining the eluted ligand and pMHC affinity data in a single prediction model. Furthermore, by including ligand data, the peptide length preference of MHC class II can be accurately learned by the prediction model. Finally, we demonstrate that our model significantly outperforms the current state-of-the-art prediction method, NetMHCIIpan, on an external dataset of eluted ligands and appears superior in identifying CD4+ T cell epitopes.