Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape.
Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape.
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DOI：
10.1371/journal.pcbi.1010160
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发表时间：
2022-05
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
传染性增强的 SARS-CoV-2 变种对全球健康构成严重威胁。在这里，我们报告了机器学习模型，该模型可以预测受体结合域（RBD）突变对受体（ACE2）亲和力（与感染性相关）的影响，以及从人血清抗体中逃逸（与病毒中和相关）的影响。重要的是，这些模型预测了当前和以前关注的变体中 RBD 突变对受体亲和力和抗体逃逸的许多已知影响，以及强烈调节这两种特性的新突变组。此外，这些模型揭示了 RBD 突变对传播性的关键相反影响，因为预计会增加抗体逃逸的许多 RBD 突变组也预计会降低受体亲和力，反之亦然。这些模型协同使用时，可以捕捉 SARS-CoV-2 突变对传播性相关特性的复杂影响，并有望改善下一代疫苗和生物治疗药物的开发。机器学习是一种强大的预测工具，非常适合各种传染病应用。在这项研究中，我们应用机器学习来全面预测 SARS-CoV-2 受体结合结构域的突变对介导病毒感染性的受体亲和力和介导病毒中和的人血清抗体的逃逸的影响。这些方法识别了当前和以前值得关注的 SARS-CoV-2 变体中的关键突变，并预测可能需要进一步考虑疫苗和治疗开发的新高风险变体。此外，这些模型为未来旨在了解和缓解 COVID-19 的研究提供了一个有价值的框架，特别是在病毒持续进化仍然是全球健康威胁的关键情况下。
SARS-CoV-2 variants with enhanced transmissibility represent a serious threat to global health. Here we report machine learning models that can predict the impact of receptor-binding domain (RBD) mutations on receptor (ACE2) affinity, which is linked to infectivity, and escape from human serum antibodies, which is linked to viral neutralization. Importantly, the models predict many of the known impacts of RBD mutations in current and former Variants of Concern on receptor affinity and antibody escape as well as novel sets of mutations that strongly modulate both properties. Moreover, these models reveal key opposing impacts of RBD mutations on transmissibility, as many sets of RBD mutations predicted to increase antibody escape are also predicted to reduce receptor affinity and vice versa. These models, when used in concert, capture the complex impacts of SARS-CoV-2 mutations on properties linked to transmissibility and are expected to improve the development of next-generation vaccines and biotherapeutics. Machine learning is a powerful predictive tool that is well suited for diverse infectious disease applications. In this study, we apply machine learning to comprehensively predict the impact of mutations in the SARS-CoV-2 receptor-binding domain on both receptor affinity, which mediates viral infectivity, and escape from human serum antibodies, which mediates virus neutralization. These methods identify key mutations in current and former SARS-CoV-2 Variants of Concern, and predict novel high-risk variants that may warrant further consideration for vaccine and therapeutic development. Moreover, these models provide a valuable framework for future investigations aimed at understanding and mitigating COVID-19, especially as continued viral evolution remains a key global health threat.
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影响因子：
16.6
作者：
Greaney AJ;Starr TN;Barnes CO;Weisblum Y;Schmidt F;Caskey M;Gaebler C;Cho A;Agudelo M;Finkin S;Wang Z;Poston D;Muecksch F;Hatziioannou T;Bieniasz PD;Robbiani DF;Nussenzweig MC;Bjorkman PJ;Bloom JD
通讯作者：
Bloom JD
影响因子：
--
作者：
Ali F;Kasry A;Amin M
通讯作者：
Amin M
影响因子：
8
作者：
Midelfort, KS;Wittrup, KD
通讯作者：
Wittrup, KD
影响因子：
17.1
作者：
Laurini E;Marson D;Aulic S;Fermeglia A;Pricl S
通讯作者：
Pricl S
影响因子：
64.5
作者：
Garcia-Beltran WF;Lam EC;St Denis K;Nitido AD;Garcia ZH;Hauser BM;Feldman J;Pavlovic MN;Gregory DJ;Poznansky MC;Sigal A;Schmidt AG;Iafrate AJ;Naranbhai V;Balazs AB
通讯作者：
Balazs AB