Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions.
Cell Syst
; 11(2): 131-144.e6, 2020 08 26.
Article
in English
| MEDLINE | ID: covidwho-676381
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Haplotypes
/
Viral Vaccines
/
Histocompatibility Antigens Class I
/
Histocompatibility Antigens Class II
/
Sequence Analysis, DNA
/
Vaccines, Subunit
/
Betacoronavirus
Type of study:
Experimental Studies
/
Prognostic study
Topics:
Vaccines
Limits:
Humans
Language:
English
Journal:
Cell Syst
Year:
2020
Document Type:
Article
Affiliation country:
J.cels.2020.06.009
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