Proceedings of the National Academy of Sciences of the United States of America
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.
National Institutes of Health
Massive parallel simulations, Multiscale infrastructure, Multiscale modeling, RAS dynamics, RAS-membrane biology
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Mathematics and Statistics
Helgi I. Ingolfsson; Chris Neale; Timothy S. Carpenter; Rebika Shrestha; Cesar A. Lopez; Timothy H. Tran; Tomas Oppelstrup; Harsh Bhatia; Liam G. Stanton; Xiaohua Zhang; Shiv Sundram; Francesco Di Natale; Animesh Agarwal; Gautham Dharuman; Sara I.L. Kokkila Schumacher; Thomas Turbyville; Gulcin Gulten; Que N. Van; and For full author list, see comments below. "Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins" Proceedings of the National Academy of Sciences of the United States of America (2022). https://doi.org/10.1073/pnas.2113297119