Chakravarti Ananya, Joseph Jerelle A
Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.
Omenn-Darling Bioengineering Institute, Princeton University, Princeton, NJ 08544, USA.
bioRxiv. 2025 Mar 6:2025.03.04.641540. doi: 10.1101/2025.03.04.641540.
Protein responses to environmental stress, particularly temperature fluctuations, have long been a subject of investigation, with a focus on how proteins maintain homeostasis and exhibit thermoresponsive properties. While UCST-type (upper critical solution temperature) phase behavior has been studied extensively and can now be predicted reliably using computational models, LCST-type (lower critical solution temperature) phase transitions remain less explored, with a lack of computational models capable of accurate prediction. This gap limits our ability to probe fully how proteins undergo phase transitions in response to temperature changes. Here, we introduce Mpipi-T, a residue-level coarse-grained model designed to predict LCST-type phase behavior of proteins. Parametrized using both atomistic simulations and experimental data, Mpipi-T accounts for entropically driven protein phase separation that occurs upon heating. Accordingly, Mpipi-T predicts temperature-driven protein behavior quantitatively in both single- and multi-chain systems. Beyond its predictive capabilities, we demonstrate that Mpipi-T provides a framework for uncovering the molecular mechanisms underlying heat stress responses, offering new insights into how proteins sense and adapt to thermal changes in biological systems.
蛋白质对环境压力，特别是温度波动的反应，长期以来一直是研究的主题，重点是蛋白质如何维持体内平衡并表现出热响应特性。虽然UCST型（上临界溶液温度）相行为已得到广泛研究，现在可以使用计算模型可靠地预测，但LCST型（下临界溶液温度）相变的研究仍较少，缺乏能够准确预测的计算模型。这一差距限制了我们全面探究蛋白质如何响应温度变化而发生相变的能力。在此，我们引入了Mpipi-T，这是一种残基水平的粗粒度模型，旨在预测蛋白质的LCST型相行为。通过原子模拟和实验数据进行参数化，Mpipi-T考虑了加热时熵驱动的蛋白质相分离。因此，Mpipi-T能够在单链和多链系统中定量预测温度驱动的蛋白质行为。除了其预测能力外，我们还证明Mpipi-T提供了一个框架，用于揭示热应激反应背后的分子机制，为蛋白质如何感知和适应生物系统中的热变化提供了新的见解。
