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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 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.

SUBMITTER: Ingolfsson HI

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

REPOSITORIES: biostudies-literature

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

Ingólfsson Helgi I HI Neale Chris C Carpenter Timothy S TS Shrestha Rebika R López Cesar A CA Tran Timothy H TH Oppelstrup Tomas T Bhatia Harsh H Stanton Liam G LG Zhang Xiaohua X Sundram Shiv S Di Natale Francesco F Agarwal Animesh A Dharuman Gautham G Kokkila Schumacher Sara I L SIL Turbyville Thomas T Gulten Gulcin G Van Que N QN Goswami Debanjan D Jean-Francois Frantz F Agamasu Constance C Chen De Hettige Jeevapani J JJ Travers Timothy T Sarkar Sumantra S Surh Michael P MP Yang Yue Y Moody Adam A Liu Shusen S Van Essen Brian C BC Voter Arthur F AF Ramanathan Arvind A Hengartner Nicolas W NW Simanshu Dhirendra K DK Stephen Andrew G AG Bremer Peer-Timo PT Gnanakaran S S Glosli James N JN Lightstone Felice C FC McCormick Frank F Nissley Dwight V DV Streitz Frederick H FH

Proceedings of the National Academy of Sciences of the United States of America 20220101 1

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-bridg ...[more]

PMID: 34983849

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

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

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