Article |
oGNM: online computation of structural dynamics using the Gaussian Network Model
1 Department of Computational Biology, School of Medicine, University of Pittsburgh Pittsburgh, PA 15213, USA 2 Department of Information Science and Telecommunications, School of Information Science, University of Pittsburgh Pittsburgh, PA 15213, USA
*To whom correspondence should be addressed. Tel: +1 412 648 3333; Fax: +1 412 648 3163; Email: bahar{at}ccbb.pitt.edu
Received December 14, 2005. Revised January 25, 2006. Accepted March 6, 2006.
An assessment of the equilibrium dynamics of biomolecular systems, and in particular their most cooperative fluctuations accessible under native state conditions, is a first step towards understanding molecular mechanisms relevant to biological function. We present a web-based system, oGNM that enables users to calculate online the shape and dispersion of normal modes of motion for proteins, oligonucleotides and their complexes, or associated biological units, using the Gaussian Network Model (GNM). Computations with the new engine are 5–6 orders of magnitude faster than those using conventional normal mode analyses. Two cases studies illustrate the utility of oGNM. The first shows that the thermal fluctuations predicted for 1250 non-homologous proteins correlate well with X-ray crystallographic data over a broad range [7.3–15 Å] of inter-residue interaction cutoff distances and the correlations improve with increasing observation temperatures. The second study, focused on 64 oligonucleotides and oligonucleotide–protein complexes, shows that good agreement with experiments is achieved by representing each nucleotide by three GNM nodes (as opposed to one-node-per-residue in proteins) along with uniform interaction ranges for all components of the complexes. These results open the way to a rapid assessment of the dynamics of DNA/RNA-containing complexes. The server can be accessed at http://ignm.ccbb.pitt.edu/GNM_Online_Calculation.htm.
Present addresses: A.J. Rader, Department of Physics, Indiana University-Purdue University Indianapolis, USA
Shann Ching Chen, Department of Biomedical Engineering, Carnegie Mellon University, USA
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  L. Yang, G. Song, and R. L. Jernigan Protein elastic network models and the ranges of cooperativity PNAS, July 28, 2009; 106(30): 12347 - 12352. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L.-W. Yang, E. Eyal, I. Bahar, and A. Kitao Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics Bioinformatics, March 1, 2009; 25(5): 606 - 614. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Lu and J. Ma A minimalist network model for coarse-grained normal mode analysis and its application to biomolecular x-ray crystallography PNAS, October 7, 2008; 105(40): 15358 - 15363. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Liu, E. Eyal, and I. Bahar Analysis of correlated mutations in HIV-1 protease using spectral clustering Bioinformatics, May 15, 2008; 24(10): 1243 - 1250. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 X. Liu and H. A. Karimi High-throughput modeling and analysis of protein structural dynamics Brief Bioinform, November 1, 2007; 8(6): 432 - 445. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Eyal, C. Chennubhotla, L.-W. Yang, and I. Bahar Anisotropic fluctuations of amino acids in protein structures: insights from X-ray crystallography and elastic network models Bioinformatics, July 1, 2007; 23(13): i175 - i184. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Eyal, L.-W. Yang, and I. Bahar Anisotropic network model: systematic evaluation and a new web interface Bioinformatics, November 1, 2006; 22(21): 2619 - 2627. [Abstract] [Full Text] [PDF]
|
|