Data for: Interfaces Between Alpha-helical Integral Membrane Proteins: Characterization, Prediction, and Docking

Protein-protein interactions (PPIs) are an essential mechanism by which proteins perform their biological functions. For globular proteins, the molecular characteristics of such interactions have been well analyzed, and many computational tools are available for predicting PPI sites and constructing structural models of the complex. In contrast, little is known about the molecular features of the interaction between integral membrane proteins (IMPs) and few methods exist for constructing structural models of their complexes. Here, we analyze the interfaces from a non-redundant set of complexes of α-helical IMPs whose structures have been determined to a high resolution. We find that the interface is not significantly different from the rest of the surface in terms of amino acid composition and hydrophobicity. However, the interface is significantly better conserved and, on average, inter-subunit contacting residue pairs correlate more strongly than non-contacting pairs. We also develop a neural network-based method, with an area under the curve for the receiver operating characteristic of 0.75 and a Pearson correlation coefficient of 0.70, for predicting interface residues and their weighted contact numbers (WCNs). We further show that predicted interface residues and their WCNs can be used as restraints to reconstruct the structure α-helical IMP dimers through docking for fourteen out of a benchmark set of sixteen complexes. The RMSD100 values of the best-docked ligand subunit to its native structure are <2.5 Å for these fourteen cases. The structural analysis conducted in this work provides molecular details about the interface between α-helical IMPs and the WCN restraints represent an efficient means to score α-helical IMP docking candidates.

蛋白质-蛋白质相互作用（PPIs）是蛋白质执行其生物学功能的关键机制。对于球状蛋白，此类相互作用的分子特性已得到充分分析，且已有众多计算工具可用于预测PPI位点及构建复合物的结构模型。相比之下，关于整合膜蛋白（IMPs）相互作用的分子特性知之甚少，且构建其复合物结构模型的方法寥寥无几。本研究中，我们分析了α-螺旋IMPs非冗余复合物集的界面，这些复合物的结构已以高分辨率确定。我们发现，界面在氨基酸组成和疏水性方面与表面其他部分并无显著差异。然而，界面在保守性方面显著提高，并且，平均而言，亚基接触残基对之间的相关性比非接触对更强。此外，我们开发了一种基于神经网络的预测方法，该方法在接收者操作特征曲线下的面积为0.75，皮尔逊相关系数为0.70，用于预测界面残基及其加权接触数（WCNs）。我们还进一步表明，预测的界面残基及其WCNs可用作约束条件，通过对接重建α-螺旋IMPs二聚体结构，在16个基准集的14个复合物中实现。对于这些14个案例，最佳对接配体亚基与原结构之间的RMSD100值均小于2.5 Å。本研究进行的结构分析提供了α-螺旋IMPs界面的分子细节，而WCNs约束则代表了一种评估α-螺旋IMPs对接候选体的有效手段。