JOURNAL ARTICLE

Computational Assessment of Protein-Protein Binding Affinity by Reverse Engineering the Energetics in Protein Complexes

Bo Wang, Zhaoqian Su, Yinghao Wu
Genomics, Proteomics & Bioinformatics 2021 April 7
33838354
The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, while mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, the accurate estimation of binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. Based on the hypothesis that the stability of protein complexes is dependent on both the pairwise interactions of residues at its binding interface and all other remaining residues that do not appear at the binding interface, here we computationally reconstruct the binding affinity by decomposing it into the contribution of interfacial residues and the energetic component of other non-interface residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we showed that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinity but can also be an effective approach to predicting the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein-protein interactions.

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