Design of Cytotoxic T Cell Epitopes by Machine Learning of Human Degrons.

Autor: Truex NL; Department of Chemistry, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.; Department of Chemistry and Biochemistry, University of South Carolina; 631 Sumter St., Columbia, South Carolina, 29208, USA., Mohapatra S; Department of Materials Science and Engineering, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.; Machine Intelligence and Manufacturing Operations Group, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA., Melo M; The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; 500 Main Street, Cambridge, Massachusetts 02142, USA.; Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology; 400 Technology Square, Cambridge, Massachusetts 02139, USA., Rodriguez J; Department of Chemistry, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA., Li N; The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; 500 Main Street, Cambridge, Massachusetts 02142, USA., Abraham W; The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; 500 Main Street, Cambridge, Massachusetts 02142, USA., Sementa D; Department of Chemistry, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA., Touti F; Department of Chemistry, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA., Keskin DB; Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, Massachusetts, 02215, USA.; Harvard Medical School; Boston, Massachusetts, 02115, USA.; Broad Institute of MIT and Harvard; Cambridge, Massachusetts, USA.; Translational Immunogenomics Laboratory (TIGL), Dana-Farber Cancer Institute; Boston, Massachusetts, 02215, USA.; Department of Computer Science, Metropolitan College, Boston University; Boston, Massachusetts, USA.; Section for Bioinformatics, Department of Health Technology, Technical University of Denmark; Lyngby, DK., Wu CJ; Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, Massachusetts, 02215, USA.; Harvard Medical School; Boston, Massachusetts, 02115, USA.; Broad Institute of MIT and Harvard; Cambridge, Massachusetts, USA.; Department of Medicine, Brigham and Women's Hospital; Boston, MA 02215, USA., Irvine DJ; Department of Materials Science and Engineering, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.; The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; 500 Main Street, Cambridge, Massachusetts 02142, USA.; Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology; 400 Technology Square, Cambridge, Massachusetts 02139, USA.; Department of Biological Engineering, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.; Howard Hughes Medical Institute; 4000 Jones Bridge Rd, Chevy Chase, Maryland 20815, USA., Gómez-Bombarelli R; Department of Materials Science and Engineering, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA., Pentelute BL; Department of Chemistry, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.; The Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; 500 Main Street, Cambridge, Massachusetts 02142, USA.; Broad Institute of MIT and Harvard; Cambridge, Massachusetts, USA.; Center for Environmental Health Sciences, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
Jazyk: angličtina
Zdroj: BioRxiv : the preprint server for biology [bioRxiv] 2023 Sep 26. Date of Electronic Publication: 2023 Sep 26.
DOI: 10.1101/2023.08.22.554289
Abstrakt: Antigen processing is critical for producing epitope peptides that are presented by HLA molecules for T cell recognition. Therapeutic vaccines aim to harness these epitopes for priming cytotoxic T cell responses against cancer and pathogens, but insufficient processing often reduces vaccine efficacy through limiting the quantity of epitopes released. Here, we set out to improve antigen processing by harnessing protein degradation signals called degrons from the ubiquitin-proteasome system. We used machine learning to generate a computational model that ascribes a proteasomal degradation score between 0 and 100. Epitope peptides with varying degron activities were synthesized and translocated into cells using nontoxic anthrax proteins: protective antigen (PA) and the N-terminus of lethal factor (LF N ). Immunogenicity studies revealed epitope sequences with a low score (<25) show pronounced T-cell activation but epitope sequences with a higher score (>75) provide limited activation. This work sheds light on the sequence-activity relationships between proteasomal degradation and epitope immunogenicity, through conserving the epitope region but varying the flanking sequence. We anticipate that future efforts to incorporate proteasomal degradation signals into vaccine designs will lead to enhanced cytotoxic T cell priming by vaccine therapeutics in clinical settings.
Competing Interests: Competing interests B.L.P. is a co-founder and/or member of the scientific advisory board of several companies focusing on the development of protein and peptide therapeutics. DBK is a scientific advisor for Immunitrack and Breakbio. DBK owns equity in Affimed N.V., Agenus, Armata Pharmaceuticals, Breakbio, BioMarin Pharmaceutical, Celldex Therapeutics, Editas Medicine, Gilead Sciences, Immunitybio, ImmunoGen, IMV, Lexicon Pharmaceuticals, Neoleukin Therapeutics. BeiGene, a Chinese biotech company, supported unrelated SARS COV-2 research at TIGL.
Databáze: MEDLINE
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