Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions
Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions
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DOI：
10.1016/j.cels.2020.06.009
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发表时间：
2020-08-26
期刊：
影响因子：
9.3
通讯作者：
Gifford, David K.
中科院分区：
文献类型：
--
作者：
Liu, Ge;Carter, Brandon;Gifford, David K.
我们提出了一种组合机器学习方法来评估和优化SARS-CoV-2的肽疫苗配方。我们的方法优化了一组不同的疫苗肽条件下的目标人群HLA单倍型分布和预期的表位漂移的介绍可能性。我们提出的SARS-CoV-2 MHC I类疫苗制剂提供了93.21%的预测人群覆盖率，每个人至少有5个疫苗肽-HLA平均命中1个肽（>= 1个肽99.91%），所有疫苗肽在4，690个地理采样的SARS-CoV-2基因组中完全保守。我们提出的MHC II类疫苗制剂提供了97.21%的预测覆盖率，其中每个人至少五个疫苗肽-HLA平均命中，所有肽具有观察到的突变概率为
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