Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity is a major concern in coronavirus disease 2019 (COVID-19) prevention and economic reopening. However, rigorous determination of SARS-CoV-2 infectivity is very difficult owing to its continuous evolution with over 10,000 single nucleotide polymorphisms (SNP) variants in many subtypes. We employ an algebraic topology-based machine learning model to quantitatively evaluate the binding free energy changes of SARS-CoV-2 spike glycoprotein (S protein) and host angiotensin-converting enzyme 2 receptor following mutations. We reveal that the SARS-CoV-2 virus becomes more infectious. Three out of six SARS-CoV-2 subtypes have become slightly more infectious, while the other three subtypes have significantly strengthened their infectivity. We also find that SARS-CoV-2 is slightly more infectious than SARS-CoV according to computed S protein-angiotensin-converting enzyme 2 binding free energy changes. Based on a systematic evaluation of all possible 3686 future mutations on the S protein receptor-binding domain, we show that most likely future mutations will make SARS-CoV-2 more infectious. Combining sequence alignment, probability analysis, and binding free energy calculation, we predict that a few residues on the receptor-binding motif, i.e., 452, 489, 500, 501, and 505, have high chances to mutate into significantly more infectious COVID-19 strains.

严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 感染性是 2019 年冠状病毒病 (COVID-19) 预防和经济重新开放的主要问题。然而，由于 SARS-CoV-2 不断进化，许多亚型中存在超过 10,000 个单核苷酸多态性 (SNP) 变异，因此严格确定 SARS-CoV-2 感染性非常困难。我们采用基于代数拓扑的机器学习模型来定量评估突变后 SARS-CoV-2 刺突糖蛋白（S 蛋白）和宿主血管紧张素转换酶 2 受体的结合自由能变化。我们发现 SARS-CoV-2 病毒的传染性更强。六种 SARS-CoV-2 亚型中的三种亚型的传染性略有增强，而其他三种亚型的传染性则显着增强。根据计算的 S 蛋白-血管紧张素转换酶 2 结合自由能变化，我们还发现 SARS-CoV-2 的传染性比 SARS-CoV 稍强。基于对 S 蛋白受体结合域上所有可能的 3686 个未来突变的系统评估，我们表明未来的突变很可能会使 SARS-CoV-2 更具传染性。结合序列比对、概率分析和结合自由能计算，我们预测受体结合基序上的一些残基，即452、489、500、501和505，有很高的机会突变成传染性更强的新冠病毒。 19株。