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.

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。我们的方法优化了以目标人群 HLA 单倍型分布和预期表位漂移为条件的多种疫苗肽的呈现可能性。我们提出的 SARS-CoV-2 MHC I 类疫苗配方可提供 93.21% 的预测人群覆盖率，每人至少有 5 种疫苗肽-HLA 平均命中率（≥ 1 种肽：99.91%），所有疫苗肽在 4,690 个地理采样的 SARS-中完美保守。 CoV-2 基因组。我们提出的 MHC II 类疫苗配方可提供 97.21% 的预测覆盖率，每人至少有 5 个疫苗肽-HLA 平均命中，所有肽的观察到的突变概率≤ 0.001。我们在这里提供了我们的设计方法 (OptiVax)、疫苗评估工具 (EvalVax) 以及我们设计工作中使用的数据的开源实现：https://github.com/gifford-lab/optivax。