Evidence map›Paper›PMID 36191203›Full record

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

Docking-based long timescale simulation of cell-size protein systems at atomic resolution.

Ilya A Vakser, Sergei Grudinin, Nathan W Jenkins, Petras J Kundrotas, Eric J Deeds

Abstract read
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 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. GRAMMCell: Docking-based Cell Modeling Resource.Journal of molecular biology · 2025
    Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. GRAMM Web Server for Protein Docking.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  12. Review
  13. Article
  14. 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

5 authors.

Ilya A VakserComputational Biology Program, The University of Kansas, Lawrence, KS.
Sergei GrudininUniversity of Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France.ORCID 0000-0002-1903-7220
Nathan W JenkinsComputational Biology Program, The University of Kansas, Lawrence, KS.ORCID 0000-0001-6817-1568
Petras J KundrotasComputational Biology Program, The University of Kansas, Lawrence, KS.
Eric J DeedsDepartment of Integrative Biology and Physiology, Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, CA.

Funding

Integrated Resource for Protein Recognition StudiesR01GM074255 · NIGMS · UNIVERSITY OF KANSAS LAWRENCE · PI VAKSER, ILYA · 2005 to 2024
$6.2M
NIGMS NIH HHS R01 GM074255
6 · The paper itself

Abstract

Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, thus far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions but are relatively slow, if carried out at all-atom resolution, or are significantly coarse grained. Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association (i.e., they do not involve the time coordinate). Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise fast Fourier transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parametrized on existing data and validated on a number of observations from experiments and molecular dynamics simulations. The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution.

Indexed as

Molecular Dynamics SimulationProteinsAlgorithmsBiophysical PhenomenaKineticsMonte Carlo MethodProteinsenergy landscapeprotein crowdingprotein interactionprotein recognition

Identifiers

PMID36191203
PMCPMC9565162

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.