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DNA Distortion, Sequence Recognition and the Control of R-M Gene Expression

Project: Research


Bacteria use the so-called restriction-modification (R-M) system to protect themselves from invasion by foreign DNA (e.g. from bacterial viruses). The R-M system works by producing two enzymes. The first, a so-called methyltransferase (M), marks the bacterium's own DNA by adding methyl groups at strategic positions. The second enzyme, an endonuclease (R), then breaks down DNA that is not correctly marked. The R-M system thus provides a way to destroy foreign DNA selectively. However, if the timing of the production of the two enzymes is disturbed, the endonuclease will destroy the bacterium's own DNA, leading to the bacterial equivalent of an autoimmune disease and to death of the bacterium. To avoid this, so-called controller (C) proteins regulate the synthesis of the two enzymes by binding to the appropriate sites on the DNA that control the individual R-M genes. This leads to a complex regulatory network with positive and negative 'feedback' circuits. Our aim in this proposal is to understand exactly how subtle changes in the DNA sequence influence how strongly the controller protein binds to the control regions of the genes, and how this dictates the order in which they are switched on and off. Also, we want to know how the shape of the DNA is distorted to allow the proteins to interact with the double helix, and how this is involved in the synergy involved when proteins bind to adjacent sites on the DNA. Once we understand these things in molecular detail, it may be possible to design novel anti-bacterial drugs that are specific for different strains of bacteria.
Effective start/end date1/04/1031/03/13


  • Biotechnology and Biological Sciences Research Council: £445,031.00

Award relations

DNA Distortion, Sequence Recognition and the Control of R-M Gene Expression

Kneale, G., Professor John McGeehan & Swiderska, A.

Biotechnology and Biological Sciences Research Council: £445,031.00


Award date: 18/12/09

Funding: R: ResearchAward


ID: 3274523