Cyclic di-GMP is a bacterial second messenger that is involved in switching between motile and sessile lifestyles. Given the medical importance of biofilm formation, there has been increasing interest in understanding the synthesis and degradation of cyclic di-GMPs and their regulation in various bacterial pathogens. Environmental cues are detected by sensing domains coupled to GGDEF and EAL or HD-GYP domains that have diguanylate cyclase and phosphodiesterase activities, respectively, producing and degrading cyclic di-GMP. The Escherichia coli protein DosC (also known as YddV) consists of an oxygen-sensing domain belonging to the class of globin sensors that is coupled to a C-terminal GGDEF domain via a previously uncharacterized middle domain. DosC is one of the most strongly expressed GGDEF proteins in E. coli, but to date structural information on this and related proteins is scarce. Here, the high-resolution structural characterization of the oxygen-sensing globin domain, the middle domain and the catalytic GGDEF domain in apo and substrate-bound forms is described. The structural changes between the iron(III) and iron(II) forms of the sensor globin domain suggest a mechanism for oxygen-dependent regulation. The structural information on the individual domains is combined into a model of the dimeric DosC holoprotein. These findings have direct implications for the oxygen-dependent regulation of the activity of the cyclase domain.
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http://dx.doi.org/10.1107/S139900471501545X | DOI Listing |
J Microbiol Immunol Infect
November 2024
Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; Research Center for Cancer Biology, China Medical University, Taichung, Taiwan. Electronic address:
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National Key Laboratory of Agricultural Microbiology, Huazhong Agriculture University, Wuhan, China.
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View Article and Find Full Text PDFProtein Sci
December 2024
Institute of Biochemistry, Graz University of Technology, Graz, Austria.
Protein structure prediction and (re)design have gone through a revolution in the last 3 years. The tremendous progress in these fields has been almost exclusively driven by readily available machine learning algorithms applied to protein folding and sequence design problems. Despite these advancements, predicting site-specific mutational effects on protein stability and function remains an unsolved problem.
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November 2024
Instituto de Agrobiotecnología del Litoral (IAL, CONICET-UNL), Santa Fe, Argentina.
Int J Biol Macromol
December 2024
School of Life Sciences, Shanghai University, Shanghai 200444, PR China. Electronic address:
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