Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

ABSTRACT: 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 bi

SUBMITTER: Ingolfsson HI 

PROVIDER: S-EPMC8740753 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature