摘要：SummaryMany steps of the MHC class I antigen processing pathway can be predicted using computational methods. Here we show that epitope predictions can be further improved by considering abundance levels of peptides' source proteins. We utilized biophysical principles and existing MHC binding prediction tools in concert with abundance estimates of source proteins to derive a function that estimates the likelihood of a peptide to be an MHC class I ligand. We found that this combination improved predictions for both naturally eluted ligands and cancer neoantigen epitopes. We compared the use of different measures of antigen abundance, including mRNA expression by RNA-Seq, gene translation by Ribo-Seq, and protein abundance by proteomics on a dataset of SARS-CoV-2 epitopes. Epitope predictions were improved above binding predictions alone in all cases and gave the highest performance when using proteomic data. Our results highlight the value of incorporating antigen abundance levels to improve epitope predictions.Graphical abstractDisplay OmittedHighlights•HLA ligands originate from highly expressed transcripts•Antigen abundance and HLA binding are independent predictors of ligands and epitopes•Utilizing RNA-Seq, Ribo-Seq, or proteomic data improves epitope predictions•Cancer-type-matched TCGA RNA-Seq data can be used to estimate gene expression in patientImmunology; Mathematical biosciences; Computational bioinformatics
