Department of Chemistry and Structural Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel.
Institute of Molecular Biology gGmbH (IMB), Ackermannweg 4, 55128, Mainz, Germany.
J Mol Model. 2023 Jul 10;29(8):239. doi: 10.1007/s00894-023-05626-0.
Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆G, K) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆G from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆G without involving intermediate structural descriptors.
Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆G computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Pr) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.
蛋白质-蛋白质相互作用(PPI)是与几乎所有细胞过程相关的关键组成部分。无论是酶催化(蛋白质的“经典类型功能”)还是信号转导(“非经典类型”),蛋白质通常通过稳定或准稳定的多蛋白相互作用来发挥功能。这种相互作用的物理基础存在于相互作用的蛋白质伴侣在其界面处的形状和静电互补性(Sc、EC)的综合效应中,这为相互作用的稳定性和亲和力提供了间接概率估计。虽然 Sc 是蛋白质间相互作用的必要标准,但 EC 既可以是有利的,也可以是不利的(例如,在瞬时相互作用中)。通过实验手段估计平衡热力学参数(∆G、K)既昂贵又耗时,从而为计算结构干预开辟了窗口。最近,从粗粒度结构描述符(主要是基于表面积的术语)经验性探测 ∆G 的尝试已经被基于物理、基于知识及其混合方法(MM/PBSA、FoldX 等)所取代,这些方法直接计算 ∆G 而不涉及中间结构描述符。
在这里,我们介绍了 EnCPdock(https://www.scinetmol.in/EnCPdock/),这是一个用于直接联合比较分析蛋白质互补性和结合能的用户友好型网络界面。EnCPdock 返回由结合互补性(Sc、EC)和其他高级结构描述符(输入特征向量)组合而成的人工智能预测的 ∆G,并提供与最先进方法相当的预测准确性。EnCPdock 进一步根据其二维互补性图(CP)中的 Sc 和 EC 值(视为有序对)来定位 PPI 复合物。此外,它还为界面原子接触网络生成可移动的分子图形,以供进一步分析。EnCPdock 还提供了个体特征趋势以及与观察到的最高频率事件相关的特征得分的相对概率估计(Pr)。这些功能共同为结构调整和干预提供了实际的实用价值,这可能与靶向蛋白质界面的设计相关。将所有功能和应用程序结合在一起,EnCPdock 提供了一个独特的在线工具,应该对结构生物学家和相关领域的研究人员都有益。