Jiang Yuming, Skanata Antun, Movileanu Liviu
Department of Physics, Syracuse University, 201 Physics Building, Syracuse, New York 13244-1130, United States.
The BioInspired Institute, Syracuse University, Syracuse, New York 13244, United States.
J Phys Chem B. 2025 Oct 2;129(39):9904-9912. doi: 10.1021/acs.jpcb.5c04305. Epub 2025 Sep 24.
Coordinated interactions between a protein hub, or receptor, and its cognate protein ligands are at the heart of cell signaling. Any significant perturbations in their kinetic and dynamic complexities result in major alterations in biochemical traffic at the subcellular and extracellular levels. The coexistence of multiple ligands with varying local concentrations and affinity constants, as well as the transient nature of their underlying protein-protein interactions (PPIs), makes predicting hub occupancy a challenging task. Here, we develop models of PPIs anchored in queuing theory to determine hub occupancy as a function of the kinetic rate constants and concentrations in complex mixtures of protein ligands. We find that in a ternary mixture of protein ligands spanning a range of kinetic rate constants, the concentration of one ligand can significantly influence the competitive PPIs between the other two ligands and the protein receptor, thereby impacting its overall occupancy. Further, for more complex mixtures, we developed a coarse-graining approach to compartmentalize large numbers of ligands competing for the same binding site of the receptor. Our analytical strategy provides a mechanistic and quantitative understanding of competitive PPIs, with broad applicability to biochemical processes, protein analytics, and drug development.
蛋白质中心(或受体)与其同源蛋白质配体之间的协同相互作用是细胞信号传导的核心。它们动力学和动态复杂性的任何显著扰动都会导致亚细胞和细胞外水平生化物质运输的重大改变。多种具有不同局部浓度和亲和常数的配体共存,以及其潜在蛋白质-蛋白质相互作用(PPI)的瞬态性质,使得预测中心占有率成为一项具有挑战性的任务。在这里,我们开发了基于排队论的PPI模型,以确定作为蛋白质配体复杂混合物中动力学速率常数和浓度函数的中心占有率。我们发现,在一系列动力学速率常数的蛋白质配体三元混合物中,一种配体的浓度可显著影响其他两种配体与蛋白质受体之间的竞争性PPI,从而影响其总体占有率。此外,对于更复杂的混合物,我们开发了一种粗粒化方法,将大量竞争受体相同结合位点的配体进行划分。我们的分析策略为竞争性PPI提供了机械和定量的理解,广泛适用于生化过程、蛋白质分析和药物开发。