Skip NavigationSkip to Content
Return Return to Search Results

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

  1. Author:
    Ingólfsson, Helgi I
    Neale, Chris
    Carpenter, Timothy S [ORCID]
    Shrestha,Rebika
    López, Cesar A [ORCID]
    Tran, Timothy H [ORCID]
    Oppelstrup, Tomas
    Bhatia, Harsh
    Stanton, Liam G
    Zhang, Xiaohua
    Sundram, Shiv
    Di Natale, Francesco
    Agarwal, Animesh
    Dharuman, Gautham
    Kokkila Schumacher, Sara I L
    Turbyville,Tommy
    Gulten, Gulcin
    Van, Que N [ORCID]
    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 [ORCID]
    Hengartner, Nicolas W [ORCID]
    Simanshu, Dhirendra K [ORCID]
    Stephen, Andrew G [ORCID]
    Bremer, Peer-Timo [ORCID]
    Gnanakaran, S [ORCID]
    Glosli, James N
    Lightstone, Felice C
    McCormick, Frank [ORCID]
    Nissley, Dwight V [ORCID]
    Streitz, Frederick H

  2. Author Address

    Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550., Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545., RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701., Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550., Department of Mathematics and Statistics, San Jos 233; State University, San Jos 233;, CA 95192., Data Centric Systems, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598., Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545., Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545., Computing, Environment & Life Sciences Directorate, Argonne National Laboratory, Lemont, IL 60439., RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701; frank.mccormick@ucsf.edu nissleyd@mail.nih.gov streitz1@llnl.gov., Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94115., Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550; frank.mccormick@ucsf.edu nissleyd@mail.nih.gov streitz1@llnl.gov.,
    1. Year: 2022
    2. Date: Jan 04
  2. Journal: Proceedings of the National Academy of Sciences of the United States of America
    1. 119
    2. 1
  4. Type of Article: Article
  1. 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. Copyright © 2021 the Author(s). Published by PNAS.

    See More

  1. Keywords:

External Sources

  1. View Full Text
  2. DOI: 10.1073/pnas.2113297119
  3. PMID: 34983849
  4. PII : 2113297119

Library Notes

  1. Fiscal Year: FY2021-2022
NCI at Frederick

You are leaving a government website.

This external link provides additional information that is consistent with the intended purpose of this site. The government cannot attest to the accuracy of a non-federal site.

Linking to a non-federal site does not constitute an endorsement by this institution or any of its employees of the sponsors or the information and products presented on the site. You will be subject to the destination site's privacy policy when you follow the link.

ContinueCancel