期刊
CELL SYSTEMS
卷 11, 期 2, 页码 131-+出版社
CELL PRESS
DOI: 10.1016/j.cels.2020.06.009
关键词
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资金
- Schmidt Futures
- NIH [R01CA218094]
- Frederick National Laboratory for Cancer Research [HHSN261200800001E]
- Intramural Research Program of the NIH, Frederick National Lab, Center for Cancer Research
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 (>= 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.
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