Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.
Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.
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DOI：
10.1371/journal.pcbi.1004494
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发表时间：
2015-10
影响因子：
4.3
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Brender JR;Zhang Y
蛋白质-蛋白质复合物的形成对于蛋白质在细胞中发挥其生理功能至关重要。阻止正确复合物正确形成的突变可能会对相关细胞过程产生严重后果。由于大规模进行蛋白质-蛋白质结合亲和力的实验测定仍然很困难，因此非常需要用于预测突变对结合亲和力的影响的计算方法。我们证明，基于从 PDB 中类似蛋白质-蛋白质相互作用收集的界面结构概况的评分函数是突变时蛋白质结合亲和力变化的有力预测因子。作为一个独立的特征，突变体和野生型蛋白质的界面分布评分之间的差异具有与最佳全原子势相当的准确性，尽管一旦构建了分布图，其速度要快两个数量级。由于其在收集类似结合相互作用的进化谱方面的独特敏感性和高计算速度，界面谱评分作为补充特征具有额外的优势，可以与基于物理的潜力相结合，以提高复合评分方法的准确性。通过将序列衍生的和残基级的粗粒势与界面结构轮廓评分相结合，通过随机森林训练构建了复合模型，该模型在突变时预测的结合自由能变化和观察到的结合自由能变化之间产生>0.8的皮尔逊相关系数。这种精度在大多数情况下可与当前最佳方法相媲美或优于当前最佳方法，但不需要突变结构的高分辨率全原子模型。结合界面分析方法应该在人类疾病突变识别和蛋白质界面设计研究中找到有用的应用。很少有蛋白质是孤立地执行其任务的。相反，蛋白质以复杂的方式相互结合，可能受到人与人之间发生的自然遗传变异的影响，也可能受到引起疾病的突变（例如癌症或遗传性疾病中发生的突变）的影响。要了解这些突变如何影响我们的健康，有必要了解突变如何影响将蛋白质结合在一起的相互作用的强度。在实验室中大规模完成这是一项艰巨的任务，科学家们越来越多地转向计算方法来提前预测这些影响。我们表明，通过观察界面区域相似蛋白质-蛋白质复合物结构的多重排列，可以基于蛋白质三维结构的进化做出新的约束，以预测哪些突变与两种蛋白质相互作用相容，哪些突变不相容。
The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies. Few proteins carry out their tasks in isolation. Instead, proteins combine with each other in complicated ways that can be affected by either the natural genetic variation that occurs among people or by disease causing mutations such as those that occur in cancer or in genetic disorders. To understand how these mutations affect our health, it is necessary to understand how mutations can affect the strength of the interactions that bind proteins together. This is a difficult task to do in a laboratory on a large scale and scientists are increasingly turning to computational methods to predict these effects in advance. We show that by looking at the multiple alignments of similar protein-protein complex structures at the interface regions, new constraints based on the evolution of the three dimensional structures of proteins can be made to predict which mutations are compatible with two proteins interacting and which are not.