Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)–ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = −0.51). When we simplified the prediction problem to a binary classification, that is, whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with a 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host–pathogen system, such as Spike–ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/.

中文翻译：

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在 Spike-ACE2 深度突变相互作用集上对基于结构的结合亲和力预测因子的准确性进行基准测试

自 COVID-19 大流行开始以来，人们投入了大量精力来了解 Spike (SARS-CoV-2)-ACE2 识别机制。为此，两项深度突变扫描研究追踪了 Spike 受体结合域 (RBD) 和人 ACE2 催化域所有可能突变的影响。通过集中研究这些实验数据的界面突变，我们对六种常用的基于结构的结合亲和力预测因子（FoldX、EvoEF1、MutaBind2、SSIPe、HADDOCK 和 UEP）进行了基准测试。这些预测器是根据其用户友好性、可访问性和速度来选择的。作为我们基准测试工作的结果，我们观察到没有一种方法可以与实验结合数据产生有意义的相关性。FoldX ( R = -0.51)实现了最佳相关性 。当我们将预测问题简化为二元分类（即突变是富集还是耗尽结合）时，我们发现 FoldX 实现了最高的准确率，成功率为 64%。令人惊讶的是，在这个集合上，简单的能量评分函数的表现明显优于使用额外的基于进化的术语的函数，如 Mutabind 和 SSIPe 中的函数。此外，我们证明了最近的人工智能方法 mmCSM-PPI 和 TopNetTree 产生了与基于力场的技术相当的性能。这些观察结果表明，在猜测宿主-病原体系统（例如 Spike-ACE2）的变异诱导的结合谱变化时，结合亲和力预测因子还有很大的改进空间。为了帮助进行此类改进，我们在 https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench 上提供了基准测试数据，并可以选择在 https://rbd-ace2-mutbench.github.io/ 上可视化我们的突变模型。