Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins-Reference-Cited by-同舟云学术

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

Published:2022-01-04 Issue:1 Volume:119 Page:
ISSN:0027-8424
Container-title:Proceedings of the National Academy of Sciences
language:en
Short-container-title:Proc. Natl. Acad. Sci. U.S.A.

Author:

Ingólfsson Helgi I.¹,Neale Chris²,Carpenter Timothy S.¹^ORCID,Shrestha Rebika³,López Cesar A.²^ORCID,Tran Timothy H.³^ORCID,Oppelstrup Tomas¹,Bhatia Harsh⁴,Stanton Liam G.⁵,Zhang Xiaohua¹,Sundram Shiv¹,Di Natale Francesco⁴,Agarwal Animesh²,Dharuman Gautham¹,Kokkila Schumacher Sara I. L.⁶,Turbyville Thomas³,Gulten Gulcin³,Van Que N.³^ORCID,Goswami Debanjan³,Jean-Francois Frantz³,Agamasu Constance³,Chen De³,Hettige Jeevapani J.²,Travers Timothy²,Sarkar Sumantra⁷,Surh Michael P.¹,Yang Yue¹,Moody Adam⁴,Liu Shusen⁴,Van Essen Brian C.⁴,Voter Arthur F.⁸,Ramanathan Arvind⁹^ORCID,Hengartner Nicolas W.²^ORCID,Simanshu Dhirendra K.³^ORCID,Stephen Andrew G.³^ORCID,Bremer Peer-Timo⁴^ORCID,Gnanakaran S.²^ORCID,Glosli James N.¹,Lightstone Felice C.¹,McCormick Frank³¹⁰^ORCID,Nissley Dwight V.³^ORCID,Streitz Frederick H.¹

Affiliation:

1. Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550;

2. Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545;

3. RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701;

4. Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550;

5. Department of Mathematics and Statistics, San José State University, San José, CA 95192;

6. Data Centric Systems, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598;

7. Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545;

8. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545;

9. Computing, Environment & Life Sciences Directorate, Argonne National Laboratory, Lemont, IL 60439;

10. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94115

Abstract

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.

Funder

U.S. Department of Energy

HHS | NIH | National Cancer Institute

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

Link

https://pnas.org/doi/pdf/10.1073/pnas.2113297119

Reference74 articles.

1. Comprehensive Characterization of Cancer Driver Genes and Mutations

2. The RAS–Effector Interaction as a Drug Target