物理驱动的粗晶模型，用于生物分子相分离，具有接近定量的精度,Nature Computational Science

已经开发了各种物理和数据驱动的序列依赖性蛋白质粗粒模型来研究生物分子相分离并阐明主要的物理化学驱动力。在这里，我们介绍了 Mpipi，这是一个多尺度粗粒度模型，它几乎定量地描述了蛋白质临界温度的变化作为氨基酸序列的函数。该模型根据原子模拟和生物信息学数据进行参数化，并考虑了 π-π 和杂化阳离子-π/π-π 相互作用的主导作用以及精氨酸建立的比赖氨酸强得多的有吸引力的接触。我们为 Mpipi 和其他七个残基水平粗粒度模型提供了一套全面的基准，针对实验回转半径和定量体外相图，表明 Mpipi 预测与这两个方面的实验非常吻合。此外，Mpipi 可以解释蛋白质-RNA 相互作用，正确预测电荷匹配的多-精氨酸/聚-赖氨酸/RNA 系统的多相行为，并概括 FUS、DDX4 和 LAF-1 蛋白序列突变的实验液-液相分离趋势。

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Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy

Various physics- and data-driven sequence-dependent protein coarse-grained models have been developed to study biomolecular phase separation and elucidate the dominant physicochemical driving forces. Here we present Mpipi, a multiscale coarse-grained model that describes almost quantitatively the change in protein critical temperatures as a function of amino acid sequence. The model is parameterized from both atomistic simulations and bioinformatics data and accounts for the dominant role of π–π and hybrid cation–π/π–π interactions and the much stronger attractive contacts established by arginines than lysines. We provide a comprehensive set of benchmarks for Mpipi and seven other residue-level coarse-grained models against experimental radii of gyration and quantitative in vitro phase diagrams, demonstrating that Mpipi predictions agree well with experiments on both fronts. Moreover, Mpipi can account for protein–RNA interactions, correctly predicts the multiphase behavior of a charge-matched poly-arginine/poly-lysine/RNA system, and recapitulates experimental liquid–liquid phase separation trends for sequence mutations on FUS, DDX4 and LAF-1 proteins.