Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins
| Autor: | Harsh Bhatia, Dwight V. Nissley, Arthur F. Voter, Helgi I. Ingólfsson, Frank McCormick, Sumantra Sarkar, Debanjan Goswami, Gulcin Gulten, Timothy S. Carpenter, Sara Kokkila Schumacher, Frederick H. Streitz, Peer-Timo Bremer, Jeevapani J. Hettige, Xiaohua Zhang, Liam Stanton, Shusen Liu, Yue Yang, Arvind Ramanathan, Nicolas W. Hengartner, Timothy H. Tran, Dhirendra K. Simanshu, Thomas J. Turbyville, Rebika Shrestha, Constance Agamasu, Shiv Sundram, Michael P. Surh, Brian Van Essen, Cesar A. Lopez, Tomas Oppelstrup, Timothy Travers, James N. Glosli, Felice C. Lightstone, Andrew G. Stephen, Frantz Jean-Francios, De Chen, Chris Neale, Que Van, Sandrasegaram Gnanakaran, Francesco Di Natale, Adam Moody, Gautham Dharuman, Animesh Agarwal |
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| Rok vydání: | 2022 |
| Předmět: |
Multidisciplinary
Computer science Dynamics (mechanics) Cell Membrane multiscale infrastructure Computational biology Biological Sciences Molecular Dynamics Simulation Multiscale modeling Lipids multiscale modeling Proto-Oncogene Proteins p21(ras) Machine Learning Biophysics and Computational Biology Networking and Information Technology R&D Signaling proteins RAS-membrane biology RAS dynamics Humans Generic health relevance Protein Multimerization massive parallel simulations Signal Transduction |
| Zdroj: | Proceedings of the National Academy of Sciences of the United States of America, vol 119, iss 1 Proceedings of the National Academy of Sciences of the United States of America |
| ISSN: | 1091-6490 0027-8424 |
| Popis: | Significance Here we present an unprecedented multiscale simulation platform that enables modeling, hypothesis generation, and discovery across biologically relevant length and time scales to predict mechanisms that can be tested experimentally. We demonstrate that our predictive simulation-experimental validation loop generates accurate insights into RAS-membrane biology. Evaluating over 100,000 correlated simulations, we show that RAS–lipid interactions are dynamic and evolving, resulting in: 1) a reordering and selection of lipid domains in realistic eight-lipid bilayers, 2) clustering of RAS into multimers correlating with specific lipid fingerprints, 3) changes in the orientation of the RAS G-domain impacting its ability to interact with effectors, and 4) demonstration that RAS–RAS G-domain interfaces are nonspecific in these putative signaling domains. 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. |
| Databáze: | OpenAIRE |
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