Campus Units

Biochemistry, Biophysics and Molecular Biology, Roy J. Carver Department of, Bioinformatics and Computational Biology, Baker Center for Bioinformatics and Biological Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

2011

Journal or Book Title

BMC Bioinformatics

Volume

12

First Page

264

DOI

10.1186/1471-2105-12-264

Abstract

Background

The 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.

Results

Our 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.

Conclusion

MAVEN 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.

Comments

This article is published as Zimmermann, Michael T., Andrzej Kloczkowski, and Robert L. Jernigan. "MAVENs: motion analysis and visualization of elastic networks and structural ensembles." BMC bioinformatics 12, no. 1 (2011): 264. doi: 10.1186/1471-2105-12-264. Posted with permission.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

Zimmermann et al

Language

en

File Format

application/pdf

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