Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike-ACE2 recognition mechanism. To this end, two recent deep mutational scanning studies traced the impact of all possible mutations/variants across the Spike-ACE2 interface. Expanding on these studies, we benchmarked four widely used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe) and two naïve predictors (HADDOCK, UEP) on the variant Spike-ACE2 deep mutational interaction set. Among these approaches, FoldX ranked first with a 64% success rate, followed by EvoEF1 with a 57% accuracy. Upon performing residue-based analyses, we revealed critical algorithmic biases, especially in ranking mutations with increasing/decreasing hydrophobicity/volume. We also showed that the approaches using evolutionary-based terms in their affinity predictions classify most mutations as affinity depleting. These observations suggest plenty of room to improve the conventional affinity predictors for predicting the binding affinity change of the viral host-pathogen system SARS-CoV-2-ACE2. To aid the improvement of the available approaches, we provide our mutant models, together with our benchmarking data at https://github.com/CSB-KaracaLab/ace2-rbd-point-mutation-benchmark
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