Your browser doesn't support javascript.
Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and Their Augmentation by Compact Peptide Sets.
Liu, Ge; Carter, Brandon; Gifford, David K.
  • Liu G; MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; MIT Electrical Engineering and Computer Science, Cambridge, MA, USA.
  • Carter B; MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; MIT Electrical Engineering and Computer Science, Cambridge, MA, USA.
  • Gifford DK; MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; MIT Electrical Engineering and Computer Science, Cambridge, MA, USA; MIT Biological Engineering, Cambridge, MA, USA. Electronic address: gifford@mit.edu.
Cell Syst ; 12(1): 102-107.e4, 2021 01 20.
Article in English | MEDLINE | ID: covidwho-947149
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
Subunit vaccines induce immunity to a pathogen by presenting a component of the pathogen and thus inherently limit the representation of pathogen peptides for cellular immunity-based memory. We find that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) subunit peptides may not be robustly displayed by the major histocompatibility complex (MHC) molecules in certain individuals. We introduce an augmentation strategy for subunit vaccines that adds a small number of SARS-CoV-2 peptides to a vaccine to improve the population coverage of pathogen peptide display. Our population coverage estimates integrate clinical data on peptide immunogenicity in convalescent COVID-19 patients and machine learning predictions. We evaluate the population coverage of 9 different subunits of SARS-CoV-2, including 5 functional domains and 4 full proteins, and augment each of them to fill a predicted coverage gap.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines, Subunit / Machine Learning / COVID-19 Vaccines / COVID-19 / Immunity, Cellular Type of study: Experimental Studies / Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: Cell Syst Year: 2021 Document Type: Article Affiliation country: J.cels.2020.11.010

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Vaccines, Subunit / Machine Learning / COVID-19 Vaccines / COVID-19 / Immunity, Cellular Type of study: Experimental Studies / Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: Cell Syst Year: 2021 Document Type: Article Affiliation country: J.cels.2020.11.010