Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2.
Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2.
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DOI：
10.1073/pnas.2106480118
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发表时间：
2021-10-19
影响因子：
11.1
通讯作者：
Maranas CD
中科院分区：
文献类型：
--
作者：
Chen C;Boorla VS;Banerjee D;Chowdhury R;Cavener VS;Nissly RH;Gontu A;Boyle NR;Vandegrift K;Nair MS;Kuchipudi SV;Maranas CD
SARS-CoV-2感染通过病毒表面刺突蛋白与人类ACE2蛋白的结合进行。感染的全球传播导致出现了更健康和更具传染性的变异，在人类和非人类宿主中都具有更强的适应性。刺突与ACE2蛋白结合事件的分子模拟提供了一种途径，通过测量结合强度的变化来评估感染性的潜在增加或减少。我们训练了一个神经网络模型，准确地将模拟结合能映射到刺突蛋白中氨基酸变化时结合强度的实验变化。这种计算工作流程可用于先验地评估当前流行的和未来可能出现的病毒变体对hACE2的亲和力。严重急性呼吸综合征冠状病毒2 （SARS-CoV-2）刺突蛋白受体结合域（RBD）与人血管紧张素转换酶2 （hACE2）的结合是进入细胞所需的第一步。随着几种新变体的出现，SARS-CoV-2继续进化，RBD中氨基酸的变化与适应性增强和免疫逃避的可能性有关。可靠地预测氨基酸变化对RBD与hACE2更强相互作用能力的影响，可以帮助评估对公共卫生的影响，以及对其他动物的溢出和适应的可能性。在这里，我们引入了一个两步框架，首先依赖于RBD - hACE2变体的48个独立的4-ns分子动力学（MD）轨迹来收集分解为库仑、共价、范德华、亲脂、广义玻恩溶剂化、氢键、π−π填充和自接触校正项的结合能项。第二步，利用分解的能量项作为描述符，实现神经网络分类和定量预测结合亲和力的变化。该计算基对RBD单氨基酸取代变异对hACE2的结合亲和力进行分类的验证准确率为82.8%，预测和实验计算的结合亲和力变化之间的相关系数为0.73。这两个指标都是使用五倍交叉验证测试计算的。因此，我们的方法建立了一个框架，用于筛选未知的单氨基酸和多氨基酸变化引起的结合亲和力变化，为预测SARS-CoV-2变体对更紧密的hACE2结合的宿主适应性提供了有价值的工具。
SARS-CoV-2 infection proceeds through the binding of viral surface spike protein to the human ACE2 protein. The global spread of the infection has led to the emergence of fitter and more transmissible variants with increased adaptation both in human and nonhuman hosts. Molecular simulations of the binding event between the spike and ACE2 proteins offer a route to assess potential increase or decrease in infectivity by measuring the change in binding strength. We trained a neural network model that accurately maps simulated binding energies to experimental changes in binding strength upon amino acid changes in the spike protein. This computational workflow can be used to a priori assess currently circulating and prospectively future viral variants for their affinity for hACE2. The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 has continued to evolve with the emergence of several novel variants, and amino acid changes in the RBD have been implicated with increased fitness and potential for immune evasion. Reliably predicting the effect of amino acid changes on the ability of the RBD to interact more strongly with the hACE2 can help assess the implications for public health and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD−hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born solvation, hydrogen bonding, π−π packing, and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity changes using the decomposed energy terms as descriptors. The computational base achieves a validation accuracy of 82.8% for classifying single–amino acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient of 0.73 between predicted and experimentally calculated changes in binding affinities. Both metrics are calculated using a fivefold cross-validation test. Our method thus sets up a framework for screening binding affinity changes caused by unknown single– and multiple–amino acid changes offering a valuable tool to predict host adaptation of SARS-CoV-2 variants toward tighter hACE2 binding.
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