Investigating protein–protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein–protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein–protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

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预测拥挤细胞环境中蛋白质-蛋白质相互作用的计算方法

研究蛋白质-蛋白质相互作用对于理解细胞生物学过程至关重要，因为蛋白质通常在分子复合物内而不是孤立地发挥作用。虽然实验和计算方法为这些相互作用提供了有价值的见解，但它们常常忽视一个关键因素：拥挤的细胞环境。这种环境显着影响蛋白质的行为，包括结构稳定性、扩散以及最终的结合性质。在这篇综述中，我们讨论了理论和计算方法，这些方法允许生物系统建模来指导和补充实验，从而可以显着推进细胞质拥挤环境中蛋白质-蛋白质相互作用的研究，甚至可能的预测。我们探讨的主题包括晶格模拟的统计力学、流体动力学相互作用、高粘度环境中的扩散过程以及基于分子动力学模拟的几种方法。通过协同利用生物物理学和计算生物学的方法，我们回顾了最先进的计算方法，以研究分子拥挤对蛋白质-蛋白质相互作用的影响，并讨论其对人类相互作用组表征的潜在革命性影响。