Evidence map›Paper›PMID 25491031›Full record

ArticleBMC bioinformatics2014

Integrating protein structural dynamics and evolutionary analysis with Bio3D.

Lars Skjærven, Xin-Qiu Yao, Guido Scarabelli, Barry J Grant

Abstract read
In one paragraph

Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 192 papers.

0numbers the graph read from it
0cells of the map it votes in
192citing 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

192 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. MK4 Repositioning for IAHSP: OvercomingACS chemical neuroscience · 2026
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

132 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

4 authors.

Lars SkjærvenDepartment of Biomedicine, University of Bergen, Bergen, Norway. lars.skjarven@biomed.uib.no.
Xin-Qiu YaoDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA. xinqyao@umich.edu.
Guido ScarabelliDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA. guidos@umich.edu.
Barry J GrantDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA. bjgrant@umich.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPopular bioinformatics approaches for studying protein functional dynamics include comparisons of crystallographic structures, molecular dynamics simulations and normal mode analysis. However, determining how observed displacements and predicted motions from these traditionally separate analyses relate to each other, as well as to the evolution of sequence, structure and function within large protein families, remains a considerable challenge. This is in part due to the general lack of tools that integrate information of molecular structure, dynamics and evolution.

resultsHere, we describe the integration of new methodologies for evolutionary sequence, structure and simulation analysis into the Bio3D package. This major update includes unique high-throughput normal mode analysis for examining and contrasting the dynamics of related proteins with non-identical sequences and structures, as well as new methods for quantifying dynamical couplings and their residue-wise dissection from correlation network analysis. These new methodologies are integrated with major biomolecular databases as well as established methods for evolutionary sequence and comparative structural analysis. New functionality for directly comparing results derived from normal modes, molecular dynamics and principal component analysis of heterogeneous experimental structure distributions is also included. We demonstrate these integrated capabilities with example applications to dihydrofolate reductase and heterotrimeric G-protein families along with a discussion of the mechanistic insight provided in each case.

conclusionsThe integration of structural dynamics and evolutionary analysis in Bio3D enables researchers to go beyond a prediction of single protein dynamics to investigate dynamical features across large protein families. The Bio3D package is distributed with full source code and extensive documentation as a platform independent R package under a GPL2 license from http://thegrantlab.org/bio3d/ .

Indexed as

Evolution, MolecularSoftwareComputational BiologyEscherichia coliHeterotrimeric GTP-Binding ProteinsHumansMolecular Dynamics SimulationMolecular StructureProtein ConformationTetrahydrofolate DehydrogenaseHeterotrimeric GTP-Binding ProteinsTetrahydrofolate Dehydrogenase

Identifiers

PMID25491031
PMCPMC4279791

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.