Evidence map›Paper›PMID 34983849›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2022

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

Helgi I Ingólfsson, Chris Neale, Timothy S Carpenter, Rebika Shrestha, Cesar A López, Timothy H Tran, Tomas Oppelstrup, Harsh Bhatia, Liam G Stanton, Xiaohua Zhang and 32 more

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
6.8field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 84 citations in OpenAlex.

  1. Review
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  8. An in vitro BRAF activation assay elucidates molecular mechanisms driving disassembly of the autoinhibited BRAF state.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  9. Review
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  14. Generating Protein Structures for Pathway Discovery Using Deep Learning.Journal of chemical theory and computation · 2024
    Article
  15. Review
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  18. Assessing the mechanism of fast-cycling cancer-associated mutations of Rac1 small Rho GTPase.Protein science : a publication of the Protein Society · 2024
    Article
  19. Diffusion Analyses along Mean and Gaussian-Curved Membranes with CurD.The journal of physical chemistry letters · 2024
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

42 authors at 7 institutions in 1 country.

Helgi I IngólfssonPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Chris NealeTheoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545.
Timothy S CarpenterPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.ORCID 0000-0001-7848-9983
Rebika ShresthaRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701.
Cesar A LópezTheoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545.ORCID 0000-0003-4684-3364
Timothy H TranRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701.ORCID 0000-0003-1342-7620
Tomas OppelstrupPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Harsh BhatiaComputing Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Liam G StantonDepartment of Mathematics and Statistics, San José State University, San José, CA 95192.
Xiaohua ZhangPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Shiv SundramPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Francesco Di NataleComputing Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Animesh AgarwalTheoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545.
Gautham DharumanPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Sara I L Kokkila SchumacherData Centric Systems, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598.
Thomas TurbyvilleRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701.
Gulcin GultenRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701.
Que N VanRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701.ORCID 0000-0002-2919-7375
Debanjan GoswamiRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701.
Frantz Jean-FrancoisRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701.
Constance AgamasuRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701.
De ChenRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701.
Jeevapani J HettigeTheoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545.
Timothy TraversTheoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545.
Sumantra SarkarCenter for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545.
Michael P SurhPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Yue YangPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Adam MoodyComputing Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Shusen LiuComputing Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Brian C Van EssenComputing Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Arthur F VoterTheoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545.
Arvind RamanathanComputing, Environment & Life Sciences Directorate, Argonne National Laboratory, Lemont, IL 60439.ORCID 0000-0002-1622-5488
Nicolas W HengartnerTheoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545.ORCID 0000-0002-4157-134X
Dhirendra K SimanshuRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701.ORCID 0000-0002-9717-4618
Andrew G StephenRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701.ORCID 0000-0002-8259-621X
Peer-Timo BremerComputing Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.ORCID 0000-0003-4107-3831
S GnanakaranTheoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545.ORCID 0000-0002-9368-3044
James N GlosliPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Felice C LightstonePhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550.
Frank McCormickRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701; frank.mccormick@ucsf.edu nissleyd@mail.nih.gov streitz1@llnl.gov.ORCID 0000-0002-6619-7120
Dwight V NissleyRAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD 21701; frank.mccormick@ucsf.edu nissleyd@mail.nih.gov streitz1@llnl.gov.ORCID 0000-0001-7523-116X
Frederick H StreitzPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550; frank.mccormick@ucsf.edu nissleyd@mail.nih.gov streitz1@llnl.gov.
Lawrence Livermore National Laboratory · USFrederick National Laboratory for Cancer Research · USLos Alamos National Laboratory · USArgonne National Laboratory · USIBM (United States) · USSan Jose State University · USUniversity of California, San Francisco · US

Funding

New Ways of Targeting K-RasR35CA197709 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MCCORMICK, FRANK PATRICK · 2016 to 2022
$6.7M
CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001ENCI NIH HHS R35 CA197709
6 · The paper itself

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.

Indexed as

Machine LearningMolecular Dynamics SimulationProtein MultimerizationSignal TransductionCell MembraneHumansLipidsProto-Oncogene Proteins p21(ras)KRAS protein, humanLipidsProto-Oncogene Proteins p21(ras)massive parallel simulationsmultiscale infrastructuremultiscale modelingRAS dynamicsRAS-membrane biology

Identifiers

PMID34983849
PMCPMC8740753
OpenAlexW4225365581

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.