Characterizing Changes in the Rate of Protein-Protein Dissociation upon Interface Mutation Using Hotspot Energy and Organization

Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects.预测突变对蛋白质-蛋白质相互作用（protein-protein interactions）动力学速率常数的影响，对于复杂疾病建模与强效肽类药物抑制剂开发均具有核心意义。然而，绝大多数现有研究多聚焦于结合速率常数的测定，解离速率则较少受到关注。本研究采用一种全新策略：通过开展突变前后的丙氨酸扫描（alanine scan），将突变引发的解离速率变化与热点残基（hotspot）及热点区域（hotregion）的能量特征与结构排布相关联。基于上述扫描结果，我们构建了一组可表征热点残基能量与分布变化的描述符（descriptor）。本方法基于SKEMPI数据库中713个经动力学表征的突变体进行基准测试。研究结果表明，借助热点残基描述符，单点丙氨酸突变所获得的能量信息可用于预测任意残基替换突变以及多点突变的解离速率变化。我们结合分子特征与热点残基描述符构建了多种机器学习模型，其中最优模型与实验解离速率的皮尔逊相关系数（Pearson's Correlation Coefficient）可达0.79，在稀有稳定突变检测任务中的马修斯相关系数（Matthew's Correlation Coefficient）为0.6。我们通过专用特征选择模型，识别出两类描述符：一类对不同突变类别、界面区域与蛋白质复合物的突变效应预测具有高度特异性，另一类则具备广泛的普适重要性。研究结果还显示，蛋白质-蛋白质界面上关键稳定区域的分布，受复合物尺寸的影响程度显著高于界面面积。此外，界面边缘位点的突变对小型蛋白质复合物的稳定性至关重要，但这类突变往往更难进行表征。我们还探究了热点区域尺寸与解离速率之间的关联，并通过构建可表征热点区域内协同效应的热点残基描述符，阐明了不同尺寸的热点区域在不同协同效应下的贡献变化规律。