Open AccessMachine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins
Author(s) -
Helgi I. Ingólfsson,
Chris Neale,
Timothy S. Carpenter,
Rebika Shrestha,
Cesar Augusto Lopez Bautista,
Timothy H. Tran,
Tomas Oppelstrup,
Harsh Bhatia,
Liam Stanton,
Xiaohua Zhang,
Shiv Sundram,
Francesco Di Natale,
Animesh Agarwal,
Gautham Dharuman,
Sara I. L. Kokkila Schumacher,
Thomas J. Turbyville,
Gulcin Gulten,
Que N. Van,
Debanjan Goswami,
Frantz Jean-François,
Constance Agamasu,
De Chen,
Jeevapani J. Hettige,
Timothy Travers,
Sumantra Sarkar,
Michael P. Surh,
Yue Yang,
Adam Moody,
Shusen Liu,
Brian C. Van Essen,
Arthur F. Voter,
Arvind Ramanathan,
Nicolas W. Hengartner,
Dhirendra K. Simanshu,
Andrew Stephen,
Peer-Timo Bremer,
S. Gnanakaran,
James N. Glosli,
Felice C. Lightstone,
Frank McCormick,
Dwight V. Nissley,
Frederick H. Streitz
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2113297119
Subject(s) - effector , kras , molecular dynamics , signal transduction , microbiology and biotechnology , biology , cell signaling , mechanism (biology) , cell , computational biology , cell membrane , systems biology , biological system , biophysics , chemistry , mutation , physics , biochemistry , gene , computational chemistry , quantum mechanics
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.