Evidence map›Paper›PMID 16891367›Full record

ArticleBiophysical journal2006

Optimization and evaluation of a coarse-grained model of protein motion using x-ray crystal data.

Dmitry A Kondrashov, Qiang Cui, George N Phillips

Abstract read
In one paragraph

Article in Biophysical journal, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

44 citing papers in PubMed.

  1. Article
  2. Article
  3. Coarse-Grained Protein Dynamics Studies Using Elastic Network Models.International journal of molecular sciences · 2018
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Large scale rigidity-based flexibility analysis of biomolecules.Structural dynamics (Melville, N.Y.) · 2016
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Elastic Network Models are Robust to Variations in Formalism.Journal of chemical theory and computation · 2012
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. 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

3 authors.

Dmitry A KondrashovDepartment of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Qiang Cui
George N Phillips

Funding

Research Training for Computation and Informatics in Biology and MedicineT15LM007359 · NLM · UNIVERSITY OF WISCONSIN-MADISON · PI Mark W. Craven, Colin Noel Dewey · 2002 to 2026
$22.6M
NLM NIH HHS 5T15LM007359NLM NIH HHS T15 LM007359
6 · The paper itself

Abstract

Simple coarse-grained models, such as the Gaussian network model, have been shown to capture some of the features of equilibrium protein dynamics. We extend this model by using atomic contacts to define residue interactions and introducing more than one interaction parameter between residues. We use B-factors from 98 ultra-high resolution (<or=1.0 A) x-ray crystal structures to optimize the interaction parameters. The average correlation between Gaussian network-model fluctuation predictions and the B-factors is 0.64 for the data set, consistent with a previous large-scale study. By separating residue interactions into covalent and noncovalent, we achieve an average correlation of 0.74, and addition of ligands and cofactors further improves the correlation to 0.75. However, further separating the noncovalent interactions into nonpolar, polar, and mixed yields no significant improvement. The addition of simple chemical information results in better prediction quality without increasing the size of the coarse-grained model.

Indexed as

Computer SimulationModels, MolecularCalmodulinCrystallography, X-RayMotionCalmodulin

Identifiers

PMID16891367
PMCPMC1578481

What OpenQuestion holds

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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.