Evidence map›Paper›PMID 33236007›Full record

ArticlebioRxiv : the preprint server for biology2020

AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics.

Lorenzo Casalino, Abigail Dommer, Zied Gaieb, Emilia P Barros, Terra Sztain, Surl-Hee Ahn, Anda Trifan, Alexander Brace, Anthony Bogetti, Heng Ma and 18 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed, 46 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

  • Updated by
5 · Who and what money

Authors and funding

28 authors at 10 institutions in 3 countries.

Lorenzo CasalinoUniversity of California San Diego.
Abigail DommerUniversity of California San Diego.
Zied GaiebUniversity of California San Diego.
Emilia P BarrosUniversity of California San Diego.
Terra SztainUniversity of California San Diego.
Surl-Hee AhnUniversity of California San Diego.
Anda TrifanArgonne National Lab.
Alexander BraceArgonne National Lab.
Anthony BogettiUniversity of Pittsburgh.
Heng MaArgonne National Lab.
Hyungro LeeRutgers University & Brookhaven National Lab.
Matteo TurilliRutgers University & Brookhaven National Lab.
Syma KhalidUniversity of Southampton.
Lillian ChongUniversity of Pittsburgh.
Carlos SimmerlingStony Brook University.
David J HardyUniversity of Illinois at Urbana-Champaign.
Julio D C MaiaUniversity of Illinois at Urbana-Champaign.
James C PhillipsUniversity of Illinois at Urbana-Champaign.
Thorsten KurthNVIDIA Corporation.
Abraham SternNVIDIA Corporation.
Lei HuangTexas Advanced Computing Center.
John McCalpinTexas Advanced Computing Center.
Mahidhar TatineniSan Diego Supercomputing Center.
Tom GibbsNVIDIA Corporation.
John E StoneUniversity of Illinois at Urbana-Champaign.
Shantenu JhaRutgers University & Brookhaven National Lab.
Arvind RamanathanArgonne National Lab.
Rommie E AmaroUniversity of California San Diego.
University of California San Diego · USArgonne National Laboratory · USUniversity of Illinois Urbana-Champaign · USNvidia (United States) · USRutgers, The State University of New Jersey · USTexas Advanced Computing CenterUniversity of Pittsburgh · USSan Diego Supercomputer Center · USStony Brook University · USUniversity of Southampton · GB

Funding

WHOLE CELL SIMULATIONP41GM104601 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI TAJKHORSHID, EMAD · 2012 to 2021
$19.0M
A MULTISCALE APPROACH TO TARGET THE ACHILLES HEEL OF P53 CANCER MUTANTS (Equipment Supplement)R01GM132826 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI AMARO, ROMMIE E · 2019 to 2022
$1.3M
NIGMS NIH HHS P41 GM104601NIGMS NIH HHS R01 GM132826
6 · The paper itself

Abstract

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

Indexed as

AIcomputational virologyCOVID19deep learningGPUHPCmolecular dynamicsmultiscale simulationSARS-CoV-2weighted ensemble

Identifiers

PMID33236007
PMCPMC7685317
OpenAlexW3106351511

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.