Evidence map›Paper›PMID 25194354›Full record

ArticleThe Journal of chemical physics2014

Perspective: Markov models for long-timescale biomolecular dynamics.

C R Schwantes, R T McGibbon, V S Pande

Abstract read
In one paragraph

Article in The Journal of chemical physics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Article
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  5. Building insightful, memory-enriched models to capture long-time biochemical processes from short-time simulations.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  6. Article
  7. Article
  8. The Two-Step Clustering Approach for Metastable States Learning.International journal of molecular sciences · 2021
    Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Data-Driven Molecular Dynamics: A Multifaceted Challenge.Pharmaceuticals (Basel, Switzerland) · 2020
    Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Review
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  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

3 authors.

C R SchwantesDepartment of Chemistry, Stanford University, Stanford, California 94305, USA.
R T McGibbonDepartment of Chemistry, Stanford University, Stanford, California 94305, USA.
V S PandeDepartment of Chemistry, Stanford University, Stanford, California 94305, USA.

Funding

Topology, nucleation, and the protein folding barrierR01GM062868 · NIGMS · STANFORD UNIVERSITY · PI PANDE, VIJAY S · 2002 to 2018
$5.5M
NIGMS NIH HHS R01 GM062868NIGMS NIH HHS R01-GM062868
6 · The paper itself

Abstract

Molecular dynamics simulations have the potential to provide atomic-level detail and insight to important questions in chemical physics that cannot be observed in typical experiments. However, simply generating a long trajectory is insufficient, as researchers must be able to transform the data in a simulation trajectory into specific scientific insights. Although this analysis step has often been taken for granted, it deserves further attention as large-scale simulations become increasingly routine. In this perspective, we discuss the application of Markov models to the analysis of large-scale biomolecular simulations. We draw attention to recent improvements in the construction of these models as well as several important open issues. In addition, we highlight recent theoretical advances that pave the way for a new generation of models of molecular kinetics.

Indexed as

Markov ChainsMolecular Dynamics Simulationbeta-LactamasesProtein ConformationProtein FoldingProteinsReceptors, Adrenergic, betabeta-LactamasesProteinsReceptors, Adrenergic, beta

Identifiers

PMID25194354
PMCPMC4156582

What OpenQuestion holds

Textmetadata
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.