Department of Biology, Boston College, Chestnut Hill, MA, USA.
Université Grenoble Alpes, CEA, INAC-MEM, L Sim, Grenoble, France.
J R Soc Interface. 2024 Feb;21(211):20230614. doi: 10.1098/rsif.2023.0614. Epub 2024 Feb 7.
quantum mechanical models can characterize and predict intermolecular binding, but only recently have models including more than a few hundred atoms gained traction. Here, we simulate the electronic structure for approximately 13 000 atoms to predict and characterize binding of SARS-CoV-2 spike variants to the human ACE2 (hACE2) receptor using the quantum mechanics complexity reduction (QM-CR) approach. We compare four spike variants in our analysis: Wuhan, Omicron, and two Omicron-based variants. To assess binding, we mechanistically characterize the energetic contribution of each amino acid involved, and predict the effect of select single amino acid mutations. We validate our computational predictions experimentally by comparing the efficacy of spike variants binding to cells expressing hACE2. At the time we performed our simulations (December 2021), the mutation A484K which our model predicted to be highly beneficial to ACE2 binding had not been identified in epidemiological surveys; only recently (August 2023) has it appeared in variant BA.2.86. We argue that our computational model, QM-CR, can identify mutations critical for intermolecular interactions and inform the engineering of high-specificity interactors.
量子力学模型可以描述和预测分子间的结合，但直到最近，只有少数几百个原子的模型才开始得到应用。在这里，我们使用量子力学复杂度降低（QM-CR）方法模拟了大约 13000 个原子的电子结构，以预测和表征 SARS-CoV-2 刺突变体与人血管紧张素转换酶 2（hACE2）受体的结合。在我们的分析中，比较了四种刺突变体：武汉株、奥密克戎株，以及两种基于奥密克戎的变体。为了评估结合，我们从能量学角度对参与结合的每个氨基酸的贡献进行了特征描述，并预测了选择的单个氨基酸突变的影响。我们通过比较刺突变体与表达 hACE2 的细胞的结合效力来实验验证我们的计算预测。在我们进行模拟的时间（2021 年 12 月），我们的模型预测 A484K 突变对 ACE2 结合非常有利，但在流行病学调查中尚未发现；直到最近（2023 年 8 月），它才出现在 BA.2.86 变体中。我们认为，我们的计算模型 QM-CR 可以识别对分子间相互作用至关重要的突变，并为高特异性相互作用体的工程提供信息。
