Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set.

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/.

Molecular Mechanism of Nucleosome Recognition by the Pioneer Transcription Factor Sox.

Pioneer transcription factors (PTFs) have the remarkable ability to directly bind to chromatin to stimulate vital cellular processes. In this work, we dissect the universal binding mode of Sox PTF by combining extensive molecular simulations and physiochemistry approaches, along with DNA footprinting techniques. As a result, we show that when Sox consensus DNA is located at the solvent-facing DNA strand, Sox binds to the compact nucleosome without imposing any significant conformational changes. We also reveal that the base-specific Sox:DNA interactions (base reading) and Sox-induced DNA changes (shape reading) are concurrently required for sequence-specific nucleosomal DNA recognition. Among three different nucleosome positions located on the positive DNA arm, a sequence-specific reading mechanism is solely satisfied at the superhelical location 2 (SHL2). While SHL2 acts transparently for solvent-facing Sox binding, among the other two positions, SHL4 permits only shape reading. The final position, SHL0 (dyad), on the other hand, allows no reading mechanism. These findings demonstrate that Sox-based nucleosome recognition is essentially guided by intrinsic nucleosome properties, permitting varying degrees of DNA recognition.