Name
Abstract | ||
|---|---|---|
ABSTRACT Elastic network models (ENMs) and, in particular, the Gaussian Network Model (GNM) have been widely used in recent years to gain insights into the machinery,of proteins. The extension of ENMs to supramolecular assemblies presents computational challenges, because of the difficulty in retaining atomic details in mode,decomposition of large protein dynamics. Here, we present a novel approach to address this problem. We rely on the premise that, all the residues of the protein machinery,(network) must,communicate,with each other and operate,in a coordinated,manner,to perform,their function successfully. To gain insight into the mechanism of information transfer between residues, we study a Markov model of network communication. Using the Markov chain perspective, we map the full-atom network representation into a hierarchy of ENMs of decreasing resolution, perform analysis of dominant,communication,(or dynamic),patterns in reduced,space(s) and,reconstruct the detailed models,with minimal,loss of information. The communication,properties at different levels of the hierarchy,are intrinsically defined by the network,topology. This new representation has several features, including: soft clustering of the protein structure into stochastically coherent,regions thus providing,a useful assessment,of elements,serving as hubs and/or transmitters in propagating,information/interaction; automatic,computation,of the contact matrices for ENMs at each level of the hierarchy to facilitate computation,of both Gaussian and anisotropic fluctuation dynamics. We illustrate the utility of the hierarchical decomposition,in providing,an insightful description of the supramolecular,machinery,by applying,the methodology,to the chaperonin,GroEL–GroES. Key words: information propagation, Markov process, network model. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1089/cmb.2007.R015 | Journal of computational biology : a journal of computational molecular cell biology |
Keywords | Field | DocType |
markovian description,markov method,network model.,atomic detail,markov process,detailed model,information propagation,gnm analysis,elastic network model,hierarchical coarse-graining,gaussian network model,large protein dynamic,computational challenge,lower resolution network,full-atom gnm representation,novel approach,markov chain,network topology,information transfer,network model,markov model,protein structure | Statistical physics,Markov process,Computer science,Protein dynamics,Markov chain,Theoretical computer science,Gaussian network model,Stationary distribution,Normal mode,Granularity,Genetics,Network model | Journal |
Volume | Issue | ISSN |
14 | 6 | 1066-5277 |
ISBN | Citations | PageRank |
3-540-33295-2 | 3 | 0.74 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chakra Chennubhotla | 1 | 89 | 12.70 |
| Ivet Bahar | 2 | 361 | 39.41 |