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      Gas-Selective Catalytic Regulation by a Newly Identified Globin-Coupled Sensor Phosphodiesterase Containing an HD-GYP Domain from the Human Pathogen Vibrio fluvialis

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      Biochemistry
      American Chemical Society

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          Abstract

          Globin-coupled sensors constitute an important family of heme-based gas sensors, an emerging class of heme proteins. In this study, we have identified and characterized a globin-coupled sensor phosphodiesterase containing an HD-GYP domain (GCS-HD-GYP) from the human pathogen Vibrio fluvialis, which is an emerging foodborne pathogen of increasing public health concern. The amino acid sequence encoded by the AL536_01530 gene from V. fluvialis indicated the presence of an N-terminal globin domain and a C-terminal HD-GYP domain, with HD-GYP domains shown previously to display phosphodiesterase activity toward bis(3′,5′)-cyclic dimeric guanosine monophosphate (c-di-GMP), a bacterial second messenger that regulates numerous important physiological functions in bacteria, including in bacterial pathogens. Optical absorption spectral properties of GCS-HD-GYP were found to be similar to those of myoglobin and hemoglobin and of other bacterial globin-coupled sensors. The binding of O 2 to the Fe(II) heme iron complex of GCS-HD-GYP promoted the catalysis of the hydrolysis of c-di-GMP to its linearized product, 5′-phosphoguanylyl-(3′,5′)-guanosine (pGpG), whereas CO and NO binding did not enhance the catalysis, indicating a strict discrimination of these gaseous ligands. These results shed new light on the molecular mechanism of gas-selective catalytic regulation by globin-coupled sensors, with these advances apt to lead to a better understanding of the family of globin-coupled sensors, a still growing family of heme-based gas sensors. In addition, given the importance of c-di-GMP in infection and virulence, our results suggested that GCS-HD-GYP could play an important role in the ability of V. fluvialis to sense O 2 and NO in the context of host–pathogen interactions.

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          Most cited references50

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

            The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.
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              Cyclic di-GMP: the first 25 years of a universal bacterial second messenger.

              Twenty-five years have passed since the discovery of cyclic dimeric (3'→5') GMP (cyclic di-GMP or c-di-GMP). From the relative obscurity of an allosteric activator of a bacterial cellulose synthase, c-di-GMP has emerged as one of the most common and important bacterial second messengers. Cyclic di-GMP has been shown to regulate biofilm formation, motility, virulence, the cell cycle, differentiation, and other processes. Most c-di-GMP-dependent signaling pathways control the ability of bacteria to interact with abiotic surfaces or with other bacterial and eukaryotic cells. Cyclic di-GMP plays key roles in lifestyle changes of many bacteria, including transition from the motile to the sessile state, which aids in the establishment of multicellular biofilm communities, and from the virulent state in acute infections to the less virulent but more resilient state characteristic of chronic infectious diseases. From a practical standpoint, modulating c-di-GMP signaling pathways in bacteria could represent a new way of controlling formation and dispersal of biofilms in medical and industrial settings. Cyclic di-GMP participates in interkingdom signaling. It is recognized by mammalian immune systems as a uniquely bacterial molecule and therefore is considered a promising vaccine adjuvant. The purpose of this review is not to overview the whole body of data in the burgeoning field of c-di-GMP-dependent signaling. Instead, we provide a historic perspective on the development of the field, emphasize common trends, and illustrate them with the best available examples. We also identify unresolved questions and highlight new directions in c-di-GMP research that will give us a deeper understanding of this truly universal bacterial second messenger.
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                Author and article information

                Journal
                Biochemistry
                Biochemistry
                bi
                bichaw
                Biochemistry
                American Chemical Society
                0006-2960
                1520-4995
                24 January 2024
                20 February 2024
                : 63
                : 4
                : 523-532
                Affiliations
                []Department of Chemistry, Faculty of Science, Tokyo University of Science , 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan
                []Department of Chemistry, Graduate School of Science, Tokyo University of Science , 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan
                Author notes
                [* ]Email: kita24@ 123456rs.tus.ac.jp . Tel.: +81-3-3260-4272 (ext. 5738).
                Author information
                https://orcid.org/0000-0002-2110-8104
                Article
                10.1021/acs.biochem.3c00484
                10882959
                38264987
                e55550a1-e5d9-4bc5-b5f1-8a135111eed2
                © 2024 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 12 September 2023
                : 05 January 2024
                : 05 January 2024
                Funding
                Funded by: Japan Society for the Promotion of Science, doi 10.13039/501100001691;
                Award ID: JP22K05446
                Categories
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                bi3c00484
                bi3c00484

                Biochemistry
                Biochemistry

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