Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 Deep Mutational Interaction Set

Abstract

ABSTRACTSince 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). Also, when we simplified the prediction problem to a binary classification, i.e., whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with 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. These observations suggest plenty of room to improve the conventional affinity predictors for guessing the variant-induced binding profile changes of a host-pathogen system, such as the Spike-ACE2. To aid such improvements we provide our benchmarking data athttps://github.com/CSB-KaracaLab/RBD-ACE2-MutBenchwith the option to visualize our mutant models athttps://rbd-ace2-mutbench.github.io/