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      Crystal Structure of Botulinum Neurotoxin Type A in Complex with the Cell Surface Co-Receptor GT1b—Insight into the Toxin–Neuron Interaction

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          Abstract

          Botulinum neurotoxins have a very high affinity and specificity for their target cells requiring two different co-receptors located on the neuronal cell surface. Different toxin serotypes have different protein receptors; yet, most share a common ganglioside co-receptor, GT1b. We determined the crystal structure of the botulinum neurotoxin serotype A binding domain (residues 873–1297) alone and in complex with a GT1b analog at 1.7 Å and 1.6 Å, respectively. The ganglioside GT1b forms several key hydrogen bonds to conserved residues and binds in a shallow groove lined by Tryptophan 1266. GT1b binding does not induce any large structural changes in the toxin; therefore, it is unlikely that allosteric effects play a major role in the dual receptor recognition. Together with the previously published structures of botulinum neurotoxin serotype B in complex with its protein co-receptor, we can now generate a detailed model of botulinum neurotoxin's interaction with the neuronal cell surface. The two branches of the GT1b polysaccharide, together with the protein receptor site, impose strict geometric constraints on the mode of interaction with the membrane surface and strongly support a model where one end of the 100 Å long translocation domain helix bundle swing into contact with the membrane, initiating the membrane anchoring event.

          Author Summary

          Botulinum neurotoxins are the most toxic substances known and are classified as a category A bioterrorism agent. Ongoing work on the development of countermeasures for the neurotoxin has been limited by an incomplete understanding of the means by which the toxin enters the cell. Our study provides a detailed look at how the toxin binds its ganglioside co-receptor on the cell surface. Together with earlier work this generates a detailed description of how the toxin binds its two co-receptors to position it for entrance into the neuronal cell. This structural data provides critical new insight about the action of the botulinum neurotoxins that can be applied toward the development of agents to block toxin uptake in the digestive system and/or inhibit the binding of the toxin at the neuromuscular junction.

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          Most cited references33

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          Automatic processing of rotation diffraction data from crystals of initially unknown symmetry and cell constants

          W Kabsch (1993)
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            ESPript/ENDscript: Extracting and rendering sequence and 3D information from atomic structures of proteins.

            The fortran program ESPript was created in 1993, to display on a PostScript figure multiple sequence alignments adorned with secondary structure elements. A web server was made available in 1999 and ESPript has been linked to three major web tools: ProDom which identifies protein domains, PredictProtein which predicts secondary structure elements and NPS@ which runs sequence alignment programs. A web server named ENDscript was created in 2002 to facilitate the generation of ESPript figures containing a large amount of information. ENDscript uses programs such as BLAST, Clustal and PHYLODENDRON to work on protein sequences and such as DSSP, CNS and MOLSCRIPT to work on protein coordinates. It enables the creation, from a single Protein Data Bank identifier, of a multiple sequence alignment figure adorned with secondary structure elements of each sequence of known 3D structure. Similar 3D structures are superimposed in turn with the program PROFIT and a final figure is drawn with BOBSCRIPT, which shows sequence and structure conservation along the Calpha trace of the query. ESPript and ENDscript are available at http://genopole.toulouse.inra.fr/ESPript.
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              Automated protein model building combined with iterative structure refinement.

              In protein crystallography, much time and effort are often required to trace an initial model from an interpretable electron density map and to refine it until it best agrees with the crystallographic data. Here, we present a method to build and refine a protein model automatically and without user intervention, starting from diffraction data extending to resolution higher than 2.3 A and reasonable estimates of crystallographic phases. The method is based on an iterative procedure that describes the electron density map as a set of unconnected atoms and then searches for protein-like patterns. Automatic pattern recognition (model building) combined with refinement, allows a structural model to be obtained reliably within a few CPU hours. We demonstrate the power of the method with examples of a few recently solved structures.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Pathog
                plos
                plospath
                PLoS Pathogens
                Public Library of Science (San Francisco, USA )
                1553-7366
                1553-7374
                August 2008
                August 2008
                15 August 2008
                : 4
                : 8
                : e1000129
                Affiliations
                [1 ]Department of Molecular Biology, The Scripps Research Institute, La Jolla, California, United States of America
                [2 ]Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University, Yoshida, Sakyo-ku, Kyoto, Japan
                The Rockefeller University, United States of America
                Author notes

                Conceived and designed the experiments: PS JD RCS. Performed the experiments: PS JD. Analyzed the data: PS RCS. Contributed reagents/materials/analysis tools: AI MK. Wrote the paper: PS RCS.

                Article
                08-PLPA-RA-0383R2
                10.1371/journal.ppat.1000129
                2493045
                18704164
                bc3d5c50-ee44-48ca-bfc3-22f4fd056ea3
                Stenmark et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 21 April 2008
                : 18 July 2008
                Page count
                Pages: 10
                Categories
                Research Article
                Biochemistry/Biomacromolecule-Ligand Interactions
                Biochemistry/Experimental Biophysical Methods
                Infectious Diseases/Bacterial Infections
                Infectious Diseases/Infectious Diseases of the Nervous System

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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