[论文解读] Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies

Jiahui Chen, Kaifu Gao+2 more|arXiv (Cornell University)|Oct 13, 2020

SARS-CoV-2 and COVID-19 Research参考文献 78被引用 7

一句话总结

本研究结合深度学习、代数拓扑与生物物理学，预测SARS-CoV-2刺突蛋白与抗体或ACE2之间由突变引起的结合自由能（BFE）变化。研究识别出31种疫苗逃逸突变株，揭示65%的受体结合结构域（RBD）突变增强ACE2结合（提高传染性），而71%的突变削弱抗体结合，凸显当前疫苗与疗法面临严重风险，呼吁开发抗突变药物及季节性疫苗策略。

ABSTRACT

Antibody therapeutics and vaccines are among our last resort to end the raging COVID-19 pandemic. They, however, are prone to over 5,000 mutations on the spike (S) protein uncovered by a Mutation Tracker based on over 200,000 genome isolates. It is imperative to understand how mutations would impact vaccines and antibodies in the development. In this work, we study the mechanism, frequency, and ratio of mutations on the S protein. Additionally, we use 56 antibody structures and analyze their 2D and 3D characteristics. Moreover, we predict the mutation-induced binding free energy (BFE) changes for the complexes of S protein and antibodies or ACE2. By integrating genetics, biophysics, deep learning, and algebraic topology, we reveal that most of 462 mutations on the receptor-binding domain (RBD) will weaken the binding of S protein and antibodies and disrupt the efficacy and reliability of antibody therapies and vaccines. A list of 31 vaccine escape mutants is identified, while many other disruptive mutations are detailed as well. We also unveil that about 65\% existing RBD mutations, including those variants recently found in the United Kingdom (UK) and South Africa, are binding-strengthen mutations, resulting in more infectious COVID-19 variants. We discover the disparity between the extreme values of RBD mutation-induced BFE strengthening and weakening of the bindings with antibodies and ACE2, suggesting that SARS-CoV-2 is at an advanced stage of evolution for human infection, while the human immune system is able to produce optimized antibodies. This discovery implies the vulnerability of current vaccines and antibody drugs to new mutations. Our predictions were validated by comparison with more than 1,400 deep mutations on the S protein RBD. Our results show the urgent need to develop new mutation-resistant vaccines and antibodies and to prepare for seasonal vaccinations.

研究动机与目标

• 系统评估SARS-CoV-2刺突蛋白突变对现有疫苗与抗体疗法有效性的威胁。
• 理解在超过20万个病毒分离株中鉴定出的超过5,000种刺突蛋白突变的频率、机制与结构影响。
• 以高精度预测刺突蛋白与抗体或ACE2之间因突变引起的结合自由能（BFE）变化。
• 识别可能导致免疫逃逸或传染性增强的关键突变热点，为下一代治疗设计提供依据。
• 通过实验性深度突变分析验证预测结果，确保其在实际应用中的可靠性。

提出的方法

• 利用超过20万个SARS-CoV-2基因组分离株构建了全面的突变追踪系统，用于记录刺突蛋白突变。
• 从PDB数据库收集了56种抗体结构，分析其二维与三维特征，包括抗原表位组成与构象动力学。
• 开发了TopNetTree模型，结合代数拓扑与生物物理学原理，用于预测蛋白质-蛋白质复合物中因突变引起的BFE变化。
• 利用1,400个单一位点RBD突变的实验性深度突变数据对TopNetTree模型进行训练与验证，将预测的BFE变化与富集比进行相关性分析。
• 对突变引起的BFE变化进行统计与拓扑分析，识别出极端的结合增强与减弱模式。
• 将突变效应与受体结合结构域（RBD）的结构特征相关联，包括二级结构与残基保守性。

实验结果

研究问题

• RQ1 哪些刺突蛋白突变最显著削弱抗体结合，其对现有抗体疗法与疫苗构成何种威胁？
• RQ2 RBD突变如何影响刺突蛋白与人ACE2之间的结合亲和力，这对病毒传播力有何影响？
• RQ3 RBD范围内突变引起的结合自由能（BFE）变化的分布与幅度如何？其在抗体与ACE2相互作用中存在何种差异？
• RQ4 现有抗体对携带RBD突变的新兴变异株仍有多大概率有效？哪些突变最可能引发免疫逃逸？
• RQ5 基于结构与拓扑特征训练的机器学习模型能否准确预测单一位点突变引起的BFE变化？其与实验数据相比表现如何？

主要发现

• 71%的RBD突变削弱与已知抗体的结合，表明对当前抗体疗法与疫苗构成广泛威胁。
• 识别出31种不同的疫苗逃逸突变株，每种均预测会导致抗体结合亲和力显著降低。
• 64.9%的RBD突变增强与ACE2的结合，表明其具有提高传染性的选择优势，与英国与南非发现的变异株一致。
• 结合增强型突变的最大BFE变化幅度显著小于结合减弱型突变，表明人类抗体在进化上已针对原始刺突蛋白优化，对新突变高度敏感。
• TopNetTree的预测结果与实验性深度突变数据高度相关，验证了该模型在1,400个RBD单一位点突变中准确预测BFE变化的能力。
• 本研究揭示SARS-CoV-2已进入人类适应的高级阶段，其突变倾向于增强传染性与免疫逃逸能力，凸显开发下一代抗突变治疗手段的紧迫性。