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J Mol Biol. 1999 Mar 5;286(4):1059-74.

A free-energy-based stochastic simulation of the Tar receptor complex.

Author information

1
Department of Zoology, Cambridge University, Downing Street, Cambridge, CB2 3EJ, UK.

Abstract

We recently developed a stochastic-based program that allows individual molecules in a cell signalling pathway to be simulated. This program has now been used to model the Tar complex, a multimeric signalling complex employed by coliform bacteria. This complex acts as a solid-state computational cassette, integrating and disseminating information on the presence of attractants and repellents in the environment of the bacterium. In our model, the Tar complex exists in one of two conformations which differ in the rate at which they generate labile phosphate groups and hence signal to the flagellar motor. Individual inputs to the complex (aspartate binding, methylation at different sites, binding of CheB, CheR and CheY) are represented as binary flags, and each combination of flags confers a different free energy to the two conformations. Binding and catalysis by the complex are performed stochastically according to the complete set of known reactions allowing the swimming performance of the bacterium to be predicted. The assumption of two conformational states together with the use of free energy values allows us to bring together seemingly unrelated experimental parameters. Because of thermodynamic constraints, we find that the binding affinity for aspartate is linked to changes in phosphorylation activity. We estimate the pattern of Tar methylation and effective affinity constant of receptors over a range of aspartate levels. We also obtain evidence that both the methylating and demethylating enzymes must operate exclusively on one or other of the two conformations, and that sites of methylation of the complex are occupied in sequential order rather than independently. Detailed analysis of the response to aspartate reveals several quantitative discrepancies between simulated and experimental data which indicate areas for future research.

PMID:
10047482
DOI:
10.1006/jmbi.1999.2535
[Indexed for MEDLINE]

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