Evidence map›Paper›PMID 15870208›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2005

Molecular dynamics and protein function.

M Karplus, J Kuriyan

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 411 papers.

0numbers the graph read from it
0cells of the map it votes in
411citing papers in PubMed
14.1field-weighted citation impact, top 1% 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

411 citing papers in PubMed, 1,140 citations in OpenAlex.

  1. Article
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  6. AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
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  11. Making invisible excited-state structures of pro-interleukin-18 visible by combining NMR and machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  12. Article
  13. Unveiling hidden intermediate states in protein folding with AI-based conditional transition clustering.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
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  17. Large Language Model Agent for Modular Task Execution in Drug Discovery.Journal of chemical information and modeling · 2026
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  19. Article
  20. Article

351 more citing papers are in PubMed but not listed here.

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

2 authors at 2 institutions in 2 countries.

M KarplusDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA. marci@tammy.harvard.edu
J Kuriyan
Howard Hughes Medical Institute · USInstitut de Science et d'Ingénierie Supramoléculaires · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A fundamental appreciation for how biological macromolecules work requires knowledge of structure and dynamics. Molecular dynamics simulations provide powerful tools for the exploration of the conformational energy landscape accessible to these molecules, and the rapid increase in computational power coupled with improvements in methodology makes this an exciting time for the application of simulation to structural biology. In this Perspective we survey two areas, protein folding and enzymatic catalysis, in which simulations have contributed to a general understanding of mechanism. We also describe results for the F(1) ATPase molecular motor and the Src family of signaling proteins as examples of applications of simulations to specific biological systems.

Indexed as

AnimalsAntineoplastic AgentsBenzamidesBiophysicsCatalysisComputational BiologyComputer SimulationEnzymesHumansHydrogenImatinib MesylateModels, MolecularMonte Carlo MethodOxygenPiperazinesProtein ConformationAntineoplastic AgentsBenzamidesEnzymesHydrogenImatinib MesylateOxygenPiperazinesProteinsProton-Translocating ATPasesProto-Oncogene Proteins c-ablPyrimidinessrc-Family Kinases

Identifiers

PMID15870208
PMCPMC1100762
OpenAlexW2058206198

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.