Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.
Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.
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DOI：
10.1073/pnas.2113297119
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发表时间：
2022-01-04
影响因子：
11.1
通讯作者：
Streitz FH
中科院分区：
文献类型：
--
作者：
Ingólfsson HI;Neale C;Carpenter TS;Shrestha R;López CA;Tran TH;Oppelstrup T;Bhatia H;Stanton LG;Zhang X;Sundram S;Di Natale F;Agarwal A;Dharuman G;Kokkila Schumacher SIL;Turbyville T;Gulten G;Van QN;Goswami D;Jean-Francois F;Agamasu C;Chen D;Hettige JJ;Travers T;Sarkar S;Surh MP;Yang Y;Moody A;Liu S;Van Essen BC;Voter AF;Ramanathan A;Hengartner NW;Simanshu DK;Stephen AG;Bremer PT;Gnanakaran S;Glosli JN;Lightstone FC;McCormick F;Nissley DV;Streitz FH
在这里，我们提出了一个前所未有的多尺度模拟平台，使建模，假设生成，并发现跨生物相关的长度和时间尺度，以预测可以通过实验测试的机制。我们证明，我们的预测模拟实验验证循环生成准确的见解RAS膜生物学。评估超过100，000个相关模拟，我们表明RAS-脂质相互作用是动态和不断发展的，导致：1）在实际的八脂质双层中脂质结构域的重新排序和选择，2）RAS聚簇成与特定脂质指纹相关的多聚体，3）RAS G结构域的方向变化影响其与效应物相互作用的能力，和4）证明RAS-RAS G-结构域界面在这些推定的信号传导结构域中是非特异性的。RAS是一种与细胞膜相关的信号蛋白，在高达30%的人类癌症中发生突变。RAS信号转导被认为是由细胞膜的动态异质性调节的。调查这样的机制需要在宏观的时间和空间尺度上的近原子的细节，这是不可能与传统的计算或实验技术。我们在这里展示了一个多尺度模拟基础设施，该基础设施使用机器学习在复杂的非对称膜上创建了超过100，000个活性野生型KRAS模拟的尺度桥接集合。初始化和实验数据验证（包括活性野生型KRAS的新结构），这些模拟代表了表征RAS膜生物学的能力的实质性进展。我们报告了与界面混杂RAS多聚化相关的局部脂质组成的独特模式。这些脂质指纹被耦合到RAS动力学，预测影响效应器结合，因此可能是调节细胞信号级联的机制。
Here we present an unprecedented multiscale simulation platform that enables modeling, hypothesis generation, and discovery across biologically relevant length and time scales to predict mechanisms that can be tested experimentally. We demonstrate that our predictive simulation-experimental validation loop generates accurate insights into RAS-membrane biology. Evaluating over 100,000 correlated simulations, we show that RAS–lipid interactions are dynamic and evolving, resulting in: 1) a reordering and selection of lipid domains in realistic eight-lipid bilayers, 2) clustering of RAS into multimers correlating with specific lipid fingerprints, 3) changes in the orientation of the RAS G-domain impacting its ability to interact with effectors, and 4) demonstration that RAS–RAS G-domain interfaces are nonspecific in these putative signaling domains. RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.
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影响因子：
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作者：
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通讯作者：
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