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Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions

  1. Author:
    Liu, Ge
    Carter, Brandon
    Bricken, Trenton
    Jain, Siddhartha
    Viard,Mathias
    Carrington,Mary
    Gifford, David K

  2. Author Address

    MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA; MIT Electrical Engineering and Computer Science, Cambridge, MA, USA., Duke University, Durham, NC, USA., MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA., Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA; Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA, USA., 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.,
    1. Year: 2020
    2. Date: AUG 26
    3. Epub Date: 2020 07 23
  2. Journal: Cell systems
    1. 11
    2. 2
    3. Pages: 131-144.e6
  4. Type of Article: Article
  5. ISSN: 2405-4712
  1. Abstract:

    We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations for SARS-CoV-2. Our approach optimizes the presentation likelihood of a diverse set of vaccine peptides conditioned on a target human-population HLA haplotype distribution and expected epitope drift. Our proposed SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA average hits per person (= 1 peptide: 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our proposed MHC class II vaccine formulations provide 97.21% predicted coverage with at least five vaccine peptide-HLA average hits per person with all peptides having an observed mutation probability of = 0.001. We provide an open-source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here: https://github.com/gifford-lab/optivax. Copyright © 2020 Elsevier Inc. All rights reserved.

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External Sources

  1. View Full Text
  2. DOI: 10.1016/j.cels.2020.06.009
  3. PMID: 32721383
  4. PMCID: PMC7384425
  5. View record in Web of Science®
  6. WOS: 000563112000003
  7. PII : S2405-4712(20)30238-6

Library Notes

  1. Fiscal Year: FY2019-2020
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