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      (Re-)Viewing Role of Intracellular Glucose Beyond Extracellular Regulation of Glucose-Stimulated Insulin Secretion by Pancreatic Cells

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          Abstract

          For glucose-stimulated insulin secretion (GSIS) by pancreatic β-cells in animals, it is believed that ATP generated from glucose metabolism is primarily responsible. However, this ignores two well-established aspects in literature: (a) intracellular ATP generation from other sources resulting in an overall pool of ATP, regardless of the original source, and (b) that intracellular glucose transport is 10- to 100-fold higher than intracellular glucose phosphorylation in β-cells. The latter especially provides an earlier unaddressed, but highly appealing, observation pertaining to (at least transient) the presence of intracellular glucose molecules. Could these intracellular glucose molecules be responsible for the specificity of GSIS to glucose (instead of the widely believed ATP production from its metabolism)? In this work, we provide a comprehensive compilation of literature on glucose and GSIS using various cellular systems - all studies focus only on the extracellular role of glucose in GSIS. Further, we carried out a comprehensive analysis of differential gene expression in Mouse Insulinoma 6 (MIN6) cells, exposed to low and high extracellular glucose concentrations (EGC), from the existing whole transcriptome data. The expression of other genes involved in glycolysis, Krebs cycle, and electron transport chain was found to be unaffected by EGC, except Gapdh, Atp6v0a4, and Cox20. Remarkably, 3 upregulated genes ( Atp6v0a4, Cacnb4, Kif11) in high EGC were identified to have an association with cellular secretion. Using glucose as a possible ligand for the 3 proteins, computational investigations were carried out (that will require future ‘wet validation’, both in vitro and in vivo, e.g., using primary islets and animal models). The glucose-affinity/binding scores (in kcal/mol) obtained were also compared with glucose binding scores for positive controls (GCK and GLUT2), along with negative controls (RPA1, KU70–80, POLA1, ACAA1A, POLR1A). The binding affinity scores of glucose molecules for the 3 proteins were found to be closer to positive controls. Therefore, we report the glucose binding ability of 3 secretion-related proteins and a possible direct role of intracellular glucose molecules in GSIS.

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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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            AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.

            We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique. (c) 2009 Wiley Periodicals, Inc.
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              IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045

              To provide global, regional, and country-level estimates of diabetes prevalence and health expenditures for 2021 and projections for 2045.
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                Author and article information

                Journal
                ACS Omega
                ACS Omega
                ao
                acsodf
                ACS Omega
                American Chemical Society
                2470-1343
                29 February 2024
                12 March 2024
                : 9
                : 10
                : 11755-11768
                Affiliations
                []Kusuma School of Biological Sciences, Indian Institute of Technology Delhi (IIT Delhi) , Hauz Khas, New Delhi 110016, India
                []Supercomputing Facility for Bioinformatics and Computational Biology (SCFBio), IIT Delhi , Hauz Khas, New Delhi, 110016, India
                Author notes
                Author information
                https://orcid.org/0000-0002-4030-0951
                Article
                10.1021/acsomega.3c09171
                10938456
                38496986
                13f8cdc1-23a9-48bb-8a28-1c57dd53c5af
                © 2024 The Authors. Published by American Chemical Society

                Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works ( https://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 17 November 2023
                : 07 February 2024
                : 31 January 2024
                Funding
                Funded by: Council of Scientific and Industrial Research, India, doi 10.13039/501100001412;
                Award ID: NA
                Funded by: Ministry of Education, India, doi 10.13039/501100004541;
                Award ID: NA
                Categories
                Article
                Custom metadata
                ao3c09171
                ao3c09171

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