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

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 119; no. 1
• Main Authors: Ingólfsson, Helgi I, Neale, Chris, Carpenter, Timothy S, Shrestha, Rebika, López, Cesar A, Tran, Timothy H, Oppelstrup, Tomas, Bhatia, Harsh, Stanton, Liam G, Zhang, Xiaohua, Sundram, Shiv, Di Natale, Francesco, Agarwal, Animesh, Dharuman, Gautham, Kokkila Schumacher, Sara I L, Turbyville, Thomas, Gulten, Gulcin, Van, Que N, Goswami, Debanjan, Jean-Francois, Frantz, Agamasu, Constance, Chen, De, Hettige, Jeevapani J, Travers, Timothy, Sarkar, Sumantra, Surh, Michael P, Yang, Yue, Moody, Adam, Liu, Shusen, Van Essen, Brian C, Voter, Arthur F, Ramanathan, Arvind, Hengartner, Nicolas W, Simanshu, Dhirendra K, Stephen, Andrew G, Bremer, Peer-Timo, Gnanakaran, S, Glosli, James N, Lightstone, Felice C, McCormick, Frank, Nissley, Dwight V, Streitz, Frederick H
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 04-01-2022; Proceedings of the National Academy of Sciences
• Subjects: BASIC BIOLOGICAL SCIENCES; Biological Sciences; Cell Membrane - enzymology; Cell membranes; Cell signaling; Computer applications; Heterogeneity; Humans; K-Ras protein; Learning algorithms; Lipid composition; Lipids; Lipids - chemistry; Machine Learning; massively parallel simulations; Membranes; Molecular Dynamics Simulation; multiscale infrastructure; multiscale modeling; Protein Multimerization; Proteins; Proto-Oncogene Proteins p21(ras) - chemistry; RAS dynamics; Ras protein; RAS-membrane biology; Signal Transduction; Signaling; Simulation; massive parallel simulations; RAS-membrane biology; multiscale modeling; RAS dynamics; multiscale infrastructure
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2113297119

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.