Combined assessment of MHC binding and antigen abundance improves T cell epitope predictions.
Kosaloglu-Yalcin, Z., Lee, J., Greenbaum, J., Schoenberger, S. P., Miller, A., Kim, Y. J., Sette, A., Nielsen, M. and Peters, B.
Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA.
Division of Hematology and Oncology, Center for Personalized Cancer Therapy, San Diego Moore's Cancer Center, University of California, San Diego, San Diego, CA, USA.
Laboratory of Cellular Immunology, La Jolla Institute for Immunology, La Jolla, CA 92037, USA.
Department of Otolaryngology-Head & Neck Surgery, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Department of Medicine, University of California, San Diego, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA.
Department of Health Technology, Technical University of Denmark, DK Lyngby, 2800, Denmark.
Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, CP San Martin, B1650, Argentina.
Many 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.
iScience 25(2): 103850 (2022)