MAPDP: A Cloud-Based Computational Platform for Immunopeptidomics Analyses

Abstract

The immunopeptidome corresponds to the repertoire of peptides presented at the cell surface by the major histocompatibility complex (MHC) molecules. Cytotoxic T cells scan this repertoire to identify nonself antigens that can arise from tumors or infected cells. The identification of actionable antigenic targets is key to the development of therapeutic cancer vaccines, T-cell therapy, and other T-cell receptor-based biologics. The growing clinical interest for immunopeptidomics has accelerated the development of high throughput proteogenomic platforms that provide a system-level analysis of MHC-associated peptides. Improvement in sensitivity and throughput of mass spectrometers now allows the detection of a few thousands of peptides from less than 100 million cells. To manage the amount of data generated by these instruments, we have developed the MHC-associated peptide discovery platform (MAPDP), a novel open-source cloud-based computational platform for immunopeptidomic analyses. It provides convenient access from a web portal to immunopeptidomes stored in the database, filtering tools, various visualizations, annotations (e.g., IEDB, dbSNP, gnomAD), peptide-binding affinity prediction (mhcflurry, NetMHC), HLA genotyping, and the generation of personalized proteome databases. MAPDP functionalities are demonstrated here by the discovery of MHC peptides featuring new genetic variants identified in two previously published ovarian carcinoma data sets.

Most non-canonical proteins uniquely populate the proteome or immunopeptidome

“…Finally, a false discovery rate (FDR) of 1% was applied on the remaining peptide scores, corresponding to sample-specific FDRs in the range of 1.4 to 2,9% if applied on total PSMs (DoHH2 = 1.6%, SU-DHL-4 = 2.9%, HBL-1 = 1.4%). These filtering steps were made with the use of MAPDP (Courcelles et al, 2020). For each identified peptide, we interrogated all protein sequences to identify those that could be at the source of the peptide.…”

“…Also, we evaluated the relative mass error for each MAP and compared the distributions for the two MAPs category (canonical and non-canonical). Peptide relative mass error is presented in parts per million mass errors (ppm) unit and was assessed through the MAPDP platform (Courcelles et al, 2020).…”

“…MS analysis provides concrete evidence for the translation of a given polypeptide. Large-scale MS analyses of proteins and MAPs have been considerably refined over the last few years, with notable increases in sensitivity and accuracy (Chong et al, 2020;Courcelles et al, 2020;Ghosh et al, 2020;Ouspenskaia et al, 2020;Purcell et al, 2019;Vizcaíno et al, 2020). However, shotgun MS still requires creating a reference database to identify peptides present in a given sample.…”

MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Datasets

“…Over the last recent years, considerable advances in HLA ligand purification protocols, MS instrumentations, and computational methods have pushed forward the boundaries of MS-based immunopeptidomics. For instance, state-of-the-art data-independent acquisition methods have been applied ( 39 , 60 , 61 ), specialized computational workflows have been created ( 62 , 63 ), and new automated and semiautomated HLA ligand purification platforms have been developed to increase the throughput, speed, sensitivity, and reproducibility of immunopeptidome analysis by MS ( 57 , 64 , 65 ). In the context of this progressive technological development, the current version of MVP will inevitably need to be further developed to reach its full QC capabilities and potential for the field.…”

Immunopeptidomics for Dummies: Detailed Experimental Protocols and Rapid, User-Friendly Visualization of MHC I and II Ligand Datasets with MhcVizPipe

“…Alternatively, specialized immunopeptidomics software tools (e.g. MHCquant and MAPDP) were created for the analysis of canonical and non-canonical MHC-associated peptides from raw MS data but may remain relatively challenging to install and operate for non-experts with limited computational background and resources (28, 29). Hence, the development of an easy-to-install and easy-to-operate software tools for the rapid assessment of the quality and the specificity of immunopeptidomic datasets are needed and will find utility for any non-expert wishing to embark effectively into the immunopeptidomics’ journey!…”