[论文解读] Omicron BA.2 (B.1.1.529.2): high potential to becoming the next dominating variant

Jiahui Chen, Guo‐Wei Wei|arXiv (Cornell University)|Feb 10, 2022

SARS-CoV-2 and COVID-19 Research被引用 9

一句话总结

本研究利用基于代数拓扑的深度学习模型，基于数万个突变及深层突变数据点进行训练，预测奥密克戎BA.2亚变体的传染性约为BA.1的1.5倍，传播力是德尔塔毒株的4.2倍，疫苗突破感染潜力比BA.1高出30%，比德尔塔毒株高出17倍，表明其极有可能成为下一个主要的SARS-CoV-2变异株。

ABSTRACT

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly replaced the Delta variant as a dominating SARS-CoV-2 variant because of natural selection, which favors the variant with higher infectivity and stronger vaccine breakthrough ability. Omicron has three lineages or subvariants, BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3 (B.1.1.529.3). Among them, BA.1 is the currently prevailing subvariant. BA.2 shares 32 mutations with BA.1 but has 28 distinct ones. BA.3 shares most of its mutations with BA.1 and BA.2 except for one. BA.2 is found to be able to alarmingly reinfect patients originally infected by Omicron BA.1. An important question is whether BA.2 or BA.3 will become a new dominating "variant of concern". Currently, no experimental data has been reported about BA.2 and BA.3. We construct a novel algebraic topology-based deep learning model trained with tens of thousands of mutational and deep mutational data to systematically evaluate BA.2's and BA.3's infectivity, vaccine breakthrough capability, and antibody resistance. Our comparative analysis of all main variants namely, Alpha, Beta, Gamma, Delta, Lambda, Mu, BA.1, BA.2, and BA.3, unveils that BA.2 is about 1.5 and 4.2 times as contagious as BA.1 and Delta, respectively. It is also 30% and 17-fold more capable than BA.1 and Delta, respectively, to escape current vaccines. Therefore, we project that Omicron BA.2 is on its path to becoming the next dominating variant. We forecast that like Omicron BA.1, BA.2 will also seriously compromise most existing mAbs, except for sotrovimab developed by GlaxoSmithKline.

研究动机与目标

• 预测奥密克戎亚变体BA.2和BA.3的传染性、疫苗突破能力及抗体抵抗性，当时二者尚缺乏实验数据。
• 通过计算建模评估BA.2或BA.3是否可能超越BA.1，成为下一个主要的SARS-CoV-2变异株。
• 评估刺突蛋白受体结合结构域（RBD）突变对结合自由能（BFE）变化的影响，将结构变化与病毒适应度及免疫逃逸联系起来。
• 通过现有实验数据验证模型的预测能力，包括RBD-ACE2结合、抗体逃逸及假病毒感染数据。
• 预测BA.2对现有单克隆抗体疗法的潜在影响，除葛兰素史克开发的sotrovimab外

提出的方法

• 开发了一种新型基于代数拓扑的深度学习模型，基于SARS-CoV-2变异株的数万个突变及深层突变数据点进行训练。
• 将生物物理原理与神经网络相结合，预测因突变导致的RBD-ACE2及RBD-抗体复合物中结合自由能（BFE）的变化。
• 采用多层深度神经网络，结合反向传播与带动量的随机梯度下降法进行优化。
• 通过10折交叉验证确保模型稳健性，皮尔逊相关系数为0.864，均方根误差为1.019 kcal/mol。
• 使用带有L2正则化的损失函数以防止过拟合，定义为：$\mathop{\text{argmin}}_{{W},{b}}L({W},{b})=\frac{1}{2}\sum_{i=1}^{N}(y_{i}-f({x}_{i};\{{W},{b}\}))^{2}+\lambda\|{W}\|^{2}$。
• 将预测结果与独立实验数据集进行验证，包括深层突变富集度、逃逸分数及IC50倍数变化，显示高度相关性（r = 0.69–0.80）

实验结果

研究问题

• RQ1 基于RBD-ACE2结合自由能变化，奥密克戎BA.2是否比BA.1和德尔塔毒株更具传播性？
• RQ2 BA.2在多大程度上能逃避免疫反应，相较于其他变异株，其逃逸能力在既往BA.1感染或疫苗接种后免疫反应中表现如何？
• RQ3 与其它变异株相比，BA.2在逃避免疫疗法方面的能力如何，特别是在单克隆抗体疗法方面？
• RQ4 基于突变数据训练的深度学习模型能否准确预测RBD突变对传染性和免疫逃逸功能影响？
• RQ5 考虑到其突变谱，BA.3成为主导变异株的潜力如何预测？

主要发现

• 基于RBD-ACE2结合自由能变化，预测奥密克戎BA.2的传染性为BA.1的1.5倍。
• 估计BA.2的传播力为德尔塔毒株的4.2倍，表明其具有显著的适应度优势。
• BA.2的疫苗突破感染潜力比BA.1高出30%，比德尔塔毒株高出17倍。
• 模型预测BA.2将严重削弱大多数现有单克隆抗体疗法的效果，但对葛兰素史克开发的sotrovimab影响较小。
• 该深度学习模型表现出高度预测准确性，经实验数据验证，与RBD-ACE2及RBD-CTC-445.2结合数据的相关系数分别为0.69和0.70。
• 模型预测结果与实验数据在抗体逃逸方面高度一致，包括E484K和K417T等高影响力突变，证实了其可靠性。