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      Discovery of a potent, Kv7.3-selective potassium channel opener from a Polynesian traditional botanical anticonvulsant

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      Communications Chemistry
      Nature Publishing Group UK
      Pharmacology, Drug screening

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          Abstract

          Plants remain an important source of biologically active small molecules with high therapeutic potential. The voltage-gated potassium (Kv) channel formed by Kv7.2/3 (KCNQ2/3) heteromers is a major target for anticonvulsant drug development. Here, we screened 1444 extracts primarily from plants collected in California and the US Virgin Islands, for their ability to activate Kv7.2/3 but not inhibit Kv1.3, to select against tannic acid being the active component. We validated the 7 strongest hits, identified Thespesia populnea ( miro, milo, portia tree) as the most promising, then discovered its primary active metabolite to be gentisic acid (GA). GA highly potently activated Kv7.2/3 (EC 50, 2.8 nM). GA is, uniquely to our knowledge, 100% selective for Kv7.3 versus other Kv7 homomers; it requires S5 residue Kv7.3-W265 for Kv7.2/3 activation, and it ameliorates pentylenetetrazole-induced seizures in mice. Structure-activity studies revealed that the FDA-approved vasoprotective drug calcium dobesilate, a GA analog, is a previously unrecognized Kv7.2/3 channel opener. Also an active aspirin metabolite, GA provides a molecular rationale for the use of T. populnea as an anticonvulsant in Polynesian indigenous medicine and presents novel pharmacological prospects for potent, isoform-selective, therapeutic Kv7 channel activation.

          Abstract

          The voltage-gated potassium (Kv) channel formed by Kv7.2/3 heteromers is a major target for anticonvulsant drug development, however, specificity and potency are key challenges for Kv7.2/3 opener development. Here, the authors report the discovery of gentisic acid as a potent and selective Kv7.3 opener from Thespesia populnea — a plant reportedly used as an anticonvulsant in Polynesian traditional medicine.

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          Most cited references70

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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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            UCSF Chimera--a visualization system for exploratory research and analysis.

            The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and associated structures; ViewDock, for screening docked ligand orientations; Movie, for replaying molecular dynamics trajectories; and Volume Viewer, for display and analysis of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/. Copyright 2004 Wiley Periodicals, Inc.
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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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                Author and article information

                Contributors
                abbottg@hs.uci.edu
                Journal
                Commun Chem
                Commun Chem
                Communications Chemistry
                Nature Publishing Group UK (London )
                2399-3669
                10 October 2024
                10 October 2024
                2024
                : 7
                : 233
                Affiliations
                GRID grid.266093.8, ISNI 0000 0001 0668 7243, Bioelectricity Laboratory, Department of Physiology and Biophysics, School of Medicine, , University of California, ; Irvine, CA USA
                Author information
                http://orcid.org/0000-0003-4552-496X
                Article
                1318
                10.1038/s42004-024-01318-9
                11467302
                39390220
                5d1ead95-8ddb-4a9a-8a1e-a7ea2583d795
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 June 2024
                : 1 October 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000057, U.S. Department of Health Human Services | NIH | National Institute of General Medical Sciences (NIGMS);
                Award ID: GM130377
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                © Springer Nature Limited 2024

                pharmacology,drug screening
                pharmacology, drug screening

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