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

Helgi I. Ingólfsson(Lawrence Livermore National Laboratory), Chris Neale(Los Alamos National Laboratory), Timothy S. Carpenter(Lawrence Livermore National Laboratory), Rebika Shrestha(Frederick National Laboratory for Cancer Research), César A. López(Los Alamos National Laboratory), Timothy H. Tran(Frederick National Laboratory for Cancer Research), Tomas Oppelstrup(Lawrence Livermore National Laboratory), Harsh Bhatia(Lawrence Livermore National Laboratory), Liam Stanton(San Jose State University), Xiaohua Zhang(Lawrence Livermore National Laboratory), Shiv Sundram(Lawrence Livermore National Laboratory), Francesco Di Natale(Lawrence Livermore National Laboratory), Animesh Agarwal(Los Alamos National Laboratory), Gautham Dharuman(Lawrence Livermore National Laboratory), Sara Kokkila-Schumacher(IBM (United States)), Thomas J. Turbyville(Frederick National Laboratory for Cancer Research), Gülçin Gülten(Frederick National Laboratory for Cancer Research), Que N. Van(Frederick National Laboratory for Cancer Research), Debanjan Goswami(Frederick National Laboratory for Cancer Research), Frantz Jean-François(Frederick National Laboratory for Cancer Research), Constance Agamasu(Frederick National Laboratory for Cancer Research), De Chen(Frederick National Laboratory for Cancer Research), Jeevapani J. Hettige(Los Alamos National Laboratory), Timothy Travers(Los Alamos National Laboratory), Sumantra Sarkar(Los Alamos National Laboratory), Michael P. Surh(Lawrence Livermore National Laboratory), Yue Yang(Lawrence Livermore National Laboratory), Adam Moody(Lawrence Livermore National Laboratory), Shusen Liu(Lawrence Livermore National Laboratory), Brian C. Van Essen(Lawrence Livermore National Laboratory), Arthur F. Voter(Los Alamos National Laboratory), Arvind Ramanathan(Argonne National Laboratory), Nicolas Hengartner(Los Alamos National Laboratory), Dhirendra K. Simanshu(Frederick National Laboratory for Cancer Research), Andrew G. Stephen(Frederick National Laboratory for Cancer Research), Peer‐Timo Bremer(Lawrence Livermore National Laboratory), S. Gnanakaran(Los Alamos National Laboratory), James N. Glosli(Lawrence Livermore National Laboratory), Felice C. Lightstone(Lawrence Livermore National Laboratory), Frank McCormick(University of California, San Francisco), Dwight V. Nissley(Frederick National Laboratory for Cancer Research), Frederick H. Streitz(Lawrence Livermore National Laboratory)
Proceedings of the National Academy of Sciences
January 4, 2022
10.1073/pnas.2113297119
Cited by 87Open Access
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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.

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