Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling protei...
Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins
About this item
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Author / Creator
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States) , Los Alamos National Laboratory (LANL), Los Alamos, NM (United States) , 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 , Schumacher, Sara I. L. Kokkila , 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. and Streitz, Frederick H.
Publisher
United States: National Academy of Sciences
Journal title
Language
English
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Publication information
Publisher
United States: National Academy of Sciences
Subjects
More information
Scope and Contents
Contents
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 c...
Alternative Titles
Full title
Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins
Authors, Artists and Contributors
Author / Creator
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
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
Schumacher, Sara I. L. Kokkila
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.
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8740753
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8740753
Other Identifiers
ISSN
0027-8424,1091-6490
E-ISSN
1091-6490
DOI
10.1073/pnas.2113297119
