Evidence map›Paper›PMID 21711533›Full record

ArticleBMC bioinformatics2011

MAVENs: motion analysis and visualization of elastic networks and structural ensembles.

Michael T Zimmermann, Andrzej Kloczkowski, Robert L Jernigan

Abstract read
In one paragraph

Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0.Acta crystallographica. Section D, Structural biology · 2022
    Review
  2. The Bio3D packages for structural bioinformatics.Protein science : a publication of the Protein Society · 2021
    Article
  3. Computational Ways to Enhance Protein Inhibitor Design.Frontiers in molecular biosciences · 2020
    Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Protocols for Molecular Dynamics Simulations of RNA Nanostructures.Methods in molecular biology (Clifton, N.J.) · 2017
    Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. The use of experimental structures to model protein dynamics.Methods in molecular biology (Clifton, N.J.) · 2015
    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.

Michael T ZimmermannLH Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA.
Andrzej Kloczkowski
Robert L Jernigan

Funding

Coarse-Grained Models of ProteinsR01GM072014 · NIGMS · IOWA STATE UNIVERSITY · PI JERNIGAN, ROBERT L · 2004 to 2012
$2.3M
Modeling Ribosomal Control, Function and AssemblyR01GM073095 · NIGMS · IOWA STATE UNIVERSITY · PI JERNIGAN, ROBERT L · 2006 to 2009
$1.0M
High-Accuracy Protein Models Derived from Lower Resolution DataR01GM081680 · NIGMS · IOWA STATE UNIVERSITY · PI KLOCZKOWSKI, ANDRZEJ · 2007 to 2009
$763k
NIGMS NIH HHS R01 GM072014NIGMS NIH HHS R01GM072014NIGMS NIH HHS R01 GM073095NIGMS NIH HHS R01GM073095NIGMS NIH HHS R01GM081680
6 · The paper itself

Abstract

backgroundThe ability to generate, visualize, and analyze motions of biomolecules has made a significant impact upon modern biology. Molecular Dynamics has gained substantial use, but remains computationally demanding and difficult to setup for many biologists. Elastic network models (ENMs) are an alternative and have been shown to generate the dominant equilibrium motions of biomolecules quickly and efficiently. These dominant motions have been shown to be functionally relevant and also to indicate the likely direction of conformational changes. Most structures have a small number of dominant motions. Comparing computed motions to the structure's conformational ensemble derived from a collection of static structures or frames from an MD trajectory is an important way to understand functional motions as well as evaluate the models. Modes of motion computed from ENMs can be visualized to gain functional and mechanistic understanding and to compute useful quantities such as average positional fluctuations, internal distance changes, collectiveness of motions, and directional correlations within the structure.

resultsOur new software, MAVEN, aims to bring ENMs and their analysis to a broader audience by integrating methods for their generation and analysis into a user friendly environment that automates many of the steps. Models can be constructed from raw PDB files or density maps, using all available atomic coordinates or by employing various coarse-graining procedures. Visualization can be performed either with our software or exported to molecular viewers. Mixed resolution models allow one to study atomic effects on the system while retaining much of the computational speed of the coarse-grained ENMs. Analysis options are available to further aid the user in understanding the computed motions and their importance for its function.

conclusionMAVEN has been developed to simplify ENM generation, allow for diverse models to be used, and facilitate useful analyses, all on the same platform. This represents an integrated approach that incorporates all four levels of the modeling process - generation, evaluation, analysis, visualization - and also brings to bear multiple ENM types. The intension is to provide a versatile modular suite of programs to a broader audience. MAVEN is available for download at http://maven.sourceforge.net.

Indexed as

Molecular Dynamics SimulationSoftwareModels, MolecularMolecular ConformationProteinsProteins

Identifiers

PMID21711533
PMCPMC3213244

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.