Computational Assessment of Protein-protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes.
Computational Assessment of Protein-protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes.
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DOI：
10.1016/j.gpb.2021.03.004
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发表时间：
2021-12
影响因子：
9.5
通讯作者：
Wu, Yinghao
中科院分区：
文献类型：
--
作者：
Wang, Bo;Su, Zhaoqian;Wu, Yinghao
蛋白质的细胞功能是通过形成各种复合体来维持的。这些复合体的稳定性是通过结合亲和力的测量来量化的，改变结合亲和力的突变可以导致各种疾病，如癌症和糖尿病。因此，准确估计结合稳定性和突变对结合亲和力变化的影响是理解蛋白质的生物学功能及其功能失调后果的关键一步。人们假设，蛋白质复合体的稳定性不仅取决于通过成对相互作用在其结合界面上的残基，而且还取决于所有其他没有出现在结合界面上的残基。在这里，我们通过将结合亲和力分解为蛋白质复合体中界面残基和其他非界面残基的贡献来计算重建结合亲和力。我们进一步假设，界面残基和非界面残基对结合亲和力的贡献取决于它们的局部结构环境，如溶剂可访问的表面和二级结构类型。通过蒙特卡罗模拟对所有相应参数的权重进行优化。在大规模数据集上进行交叉验证后，我们表明该模型不仅显示出实验结合亲和力的绝对值与计算的结合亲和力绝对值之间的强相关性，而且还可以作为一种有效的方法来预测结合亲和力的相对变化。此外，我们还发现，许多参数的优化权重可以捕捉分子识别的第一原理化学和物理特征，从而反向工程蛋白质络合物的能级。这些结果表明，我们的方法可以作为当前计算方法的有用补充，用于预测结合亲和力和了解蛋白质-蛋白质相互作用的分子机制。
The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the 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. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial 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 show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict 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.