Muthukumar Keerthivasan, Devarajan Dinesh Sundaravadivelu, Kim Young C, Mittal Jeetain
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, United States.
Center for Materials Physics and Technology, Naval Research Laboratory, Washington, DC 20375, United States.
bioRxiv. 2025 Jul 15:2025.07.09.664001. doi: 10.1101/2025.07.09.664001.
Intrinsically disordered proteins (IDPs) play a central role in shaping the dynamics and material properties of biomolecular condensates. Understanding how sequence features determine these properties is critical for elucidating physiological function and guiding the rational design of synthetic condensates. Here, we use molecular dynamics simulations to investigate condensates formed by model IDPs with systematically varied chain length and charge patterning, two features characteristic of natural IDPs. Our results show that chain relaxation times, governed by sequence-dependent electrostatic interactions, quantitatively predict condensate viscosity and diffusivity. These condensates exhibit dynamics consistent with a crossover regime between Rouse and reptation behavior. While the Rouse model with idealized friction fails to capture sequence effects, the sticky Rouse model, which incorporates transient interchain contact lifetimes, accurately predicts chain reconfiguration times and, consequently, macroscopic material properties. This work establishes a predictive, sequence-resolved framework that links molecular interactions to condensate dynamics across length and time scales.
内在无序蛋白（IDP）在塑造生物分子凝聚物的动力学和材料特性方面发挥着核心作用。了解序列特征如何决定这些特性对于阐明生理功能和指导合成凝聚物的合理设计至关重要。在这里，我们使用分子动力学模拟来研究由具有系统变化链长和电荷模式的模型IDP形成的凝聚物，这是天然IDP的两个特征。我们的结果表明，由序列依赖性静电相互作用控制的链松弛时间定量地预测了凝聚物的粘度和扩散率。这些凝聚物表现出与Rouse和reptation行为之间的交叉区域一致的动力学。虽然具有理想化摩擦的Rouse模型无法捕捉序列效应，但结合了瞬态链间接触寿命的粘性Rouse模型准确地预测了链重排时间，从而预测了宏观材料特性。这项工作建立了一个预测性的、序列解析的框架，该框架将分子相互作用与跨长度和时间尺度的凝聚物动力学联系起来。
