Issue December 2003

category image Volume 21
No. 3 (p 311-468)
December 2003
ISSN 0739-1102

A Comparison Between Elastic Network Interpolation and MD Simulation of 16S Ribosomal RNA (p. 395-406)

In this paper a coarse-grained method called elastic network interpolation (ENI) is used to generate feasible transition pathways between two given conformations of the core central domain of 16S Ribosomal RNA (16S rRNA). The two given conformations are the extremes generated by a molecular dynamics (MD) simulation, which differ from each other by 10Å in root-mean-square deviation (RMSD). It takes only several hours to build an ENI pathway on a 1.5GHz Pentium with 512 MB memory, while the MD takes several weeks on high-performance multi-processor servers such as the SGI ORIGIN 2000/2100. It is shown that multiple ENI pathways capture the essential anharmonic motions of millions of timesteps in a particular MD simulation. A coarse-grained normal mode analysis (NMA) is performed on each intermediate ENI conformation, and the lowest 1% of the normal modes (representing about 40 degrees of freedom (DOF)) are used to parameterize fluctuations. This combined ENI/NMA method captures all intermediate conformations in the MD run with 1.5Å RMSD on average. In addition, if we restrict attention to the time interval of the MD run between the two extreme conformations, the RMSD between the closest ENI/NMA pathway and the MD results is about 1Å. These results may serve as a paradigm for reduced-DOF dynamic simulations of large biological macromolecules as well as a method for the reduced-parameter interpretation of massive amounts of MD data.

Key words: 16S ribosomal RNA, Elastic network interpolation, Intermediate conformation, Molecular dynamics, Normal mode analysis.

Moon K. Kim1
Wen Li2
Bruce A. Shapiro2
Gregory S. Chirikjian1,*

1Department of Mechanical Engineering
The Johns Hopkins University
Baltimore, MD 21218, USA
2Laboratory of Experimental and Computational Biology
NCI Center for Cancer Research
National Cancer Institute Frederick
National Institute of Health
Building 469
Room 150
Frederick, MD 21702, USA
*gregc@jhu.edu

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