Abstract：
The chapter emphasizes the importance of understanding protein-protein interactions in cellular mechanisms and highlights the role of computational modeling in predicting these interactions. It discusses sequence-based approaches such as evolutionary trace (ET), correlated mutation analysis (CMA), and subtractive correlated mutation (SCM) for identifying crucial amino acid residues, considering interface conservation or evolutionary changes. The chapter also explores methods like differential ET, hidden-site class model, and spatial cluster detection (SCD) for interface specificity and spatial clustering. Furthermore, it examines approaches combining structural and sequential methodologies and evaluates modeled predictions through initiatives like critical assessment of prediction of interactions (CAPRI). Additionally, the chapter provides an overview of various software programs used for molecular docking, detailing their search, sampling, refinement and scoring stages, along with innovative techniques and tools like normal mode analysis (NMA) and adaptive Poisson-Boltzmann solver (APBS) for electrostatic calculations. These computational and experimental approaches are crucial for unraveling protein-protein interactions and aid in developing potential therapeutics for various diseases.
摘要：
本章强调了理解细胞机制中蛋白质-蛋白质相互作用的重要性，并强调了计算模型在预测这些相互作用中的作用。它讨论了基于序列的方法，如进化跟踪(ET)、相关突变分析（CMA），和消减相关突变（SCM）用于识别关键的氨基酸残基，考虑界面守恒或进化变化。本章还探讨了差分ET等方法，隐藏站点类模型，和空间聚类检测(SCD)，用于接口特异性和空间聚类。此外，它研究了结合结构和顺序方法的方法，并通过诸如关键评估相互作用预测（CAPRI）之类的计划来评估建模预测。此外,本章概述了用于分子对接的各种软件程序，详细说明他们的搜索，采样，细化和评分阶段，以及用于静电计算的创新技术和工具，如正常模式分析（NMA）和自适应泊松-玻尔兹曼求解器（APBS）。这些计算和实验方法对于解开蛋白质-蛋白质相互作用至关重要，并有助于开发各种疾病的潜在疗法。
