A computational study on role of 6-(hydroxymethyl)-3-[3,4,5-trihydroxy-6-[(3,4,5-trihydroxyoxan-2-yl)oxymethyl]oxan-2-yl]oxyoxane-2,4,5-triol in the regulation of blood glucose level

6-(hydroxymethyl)-3-[3,4,5-trihydroxy-6-[(3,4,5-trihydroxyoxan-2-yl)oxymethyl]oxan-2-yl]oxyoxane-2,4,5-triol (SID 242078875) was isolated from the fruits of Syzygium densiflorum Wall. ex Wight & Arn (Myrtaceae), which has been traditionally used in the treatment of diabetes by the tribes of The Nilgiris, Tamil Nadu, India. In this study, reverse pharmacophore mapping approach and text-based database search identified the dipeptidyl peptidase-IV, protein-tyrosine phosphatase 1B, phosphoenolpyruvate carboxykinase, glycogen synthase kinase-3β and glucokinase as potential targets of SID 242078875 in diabetes management. Further, molecular docking was performed to predict the binding pose of SID 242078875 in the active site region of the target protein. In addition, dynamic behaviour and stability of protein–ligand complexes were observed for a period of 50 ns through molecular dynamics simulation.

[1]  Cheng Luo,et al.  Computational methods for drug design and discovery: focus on China , 2013, Trends in Pharmacological Sciences.

[2]  N. Handy,et al.  A new hybrid exchange–correlation functional using the Coulomb-attenuating method (CAM-B3LYP) , 2004 .

[3]  J. Pople,et al.  Self-consistent molecular orbital methods. 24. Supplemented small split-valence basis sets for second-row elements , 1982 .

[4]  Ryutaro Himeno,et al.  Binding Free-Energy Calculation Is a Powerful Tool for Drug Optimization: Calculation and Measurement of Binding Free Energy for 7-Azaindole Derivatives to Glycogen Synthase Kinase-3β , 2014, J. Chem. Inf. Model..

[5]  J. Pople,et al.  Self-consistent molecular orbital methods. 21. Small split-valence basis sets for first-row elements , 2002 .

[6]  Shaoyong Lu,et al.  The Mechanism of Allosteric Inhibition of Protein Tyrosine Phosphatase 1B , 2014, PloS one.

[7]  Wei Liu,et al.  T2D@ZJU: a knowledgebase integrating heterogeneous connections associated with type 2 diabetes mellitus , 2013, Database J. Biol. Databases Curation.

[8]  Tuan-sheng Chen,et al.  A Comparative Reverse Docking Strategy to Identify Potential Antineoplastic Targets of Tea Functional Components and Binding Mode , 2011, International journal of molecular sciences.

[9]  C. Fishwick,et al.  Computational Methods to Identify New Antibacterial Targets , 2015, Chemical biology & drug design.

[10]  Z. Zhao,et al.  Targets and Candidate Agents for Type 2 Diabetes Treatment with Computational Bioinformatics Approach , 2014, Journal of diabetes research.

[11]  Rezaul Karim,et al.  Use of computer in drug design and drug discovery: A review , 2012 .

[12]  F. Himo,et al.  Quantum chemical modeling of enzymatic reactions - applications to epoxide-transforming enzymes , 2010 .

[13]  Lirong Chen,et al.  A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus , 2013, Evidence-based complementary and alternative medicine : eCAM.

[14]  Mark S. Gordon,et al.  Self-consistent molecular-orbital methods. 22. Small split-valence basis sets for second-row elements , 1980 .

[15]  T. Holyoak,et al.  Structural Insights into the Mechanism of Phosphoenolpyruvate Carboxykinase Catalysis* , 2009, The Journal of Biological Chemistry.

[16]  S. Rees,et al.  Principles of early drug discovery , 2011, British journal of pharmacology.

[17]  Kai Huang,et al.  PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach , 2010, Nucleic Acids Res..

[18]  K. Muthusamy,et al.  Antidiabetic, antihyperlipidaemic, and antioxidant activity of Syzygium densiflorum fruits in streptozotocin and nicotinamide-induced diabetic rats , 2016, Pharmaceutical biology.

[19]  Ioannis Xenarios,et al.  The Human Diabetes Proteome Project (HDPP): From network biology to targets for therapies and prevention , 2013 .

[20]  Xiaomin Luo,et al.  TarFisDock: a web server for identifying drug targets with docking approach , 2006, Nucleic Acids Res..

[21]  Tong Wang,et al.  A Novel Method , 2020, ArXiv.

[22]  Sean J. Johnson,et al.  The molecular details of WPD-loop movement differ in the protein-tyrosine phosphatases YopH and PTP1B. , 2012, Archives of biochemistry and biophysics.

[23]  Parr,et al.  Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. , 1988, Physical review. B, Condensed matter.

[24]  W. L. Jorgensen,et al.  Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids , 1996 .

[25]  A. Laio,et al.  Predicting crystal structures: the Parrinello-Rahman method revisited. , 2002, Physical review letters.

[26]  Jung-Hsin Lin,et al.  idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach , 2012, Nucleic Acids Res..

[27]  Research and development in drug innovation: reflections from the 2013 bioeconomy conference in China, lessons learned and future perspectives , 2014, Acta pharmaceutica Sinica. B.

[28]  K. Muthusamy,et al.  Molecular modeling, quantum polarized ligand docking and structure-based 3D-QSAR analysis of the imidazole series as dual AT1 and ETA receptor antagonists , 2013, Acta Pharmacologica Sinica.

[29]  Muthusamy Karthikeyan,et al.  DAPD: A Knowledgebase for Diabetes Associated Proteins , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[30]  Woody Sherman,et al.  Novel Method for Generating Structure-Based Pharmacophores Using Energetic Analysis , 2009, J. Chem. Inf. Model..

[31]  A. Harvey,et al.  The re-emergence of natural products for drug discovery in the genomics era , 2015, Nature Reviews Drug Discovery.

[32]  Y. Z. Chen,et al.  Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach. , 2001, Journal of molecular graphics & modelling.

[33]  A. Sands,et al.  In vivo drug target discovery: identifying the best targets from the genome. , 2001, Current opinion in biotechnology.

[34]  Victor Guallar,et al.  Importance of accurate charges in molecular docking: Quantum mechanical/molecular mechanical (QM/MM) approach , 2005, J. Comput. Chem..

[35]  Adrià Cereto-Massagué,et al.  Identification of Novel Human Dipeptidyl Peptidase-IV Inhibitors of Natural Origin (Part I): Virtual Screening and Activity Assays , 2012, PloS one.

[36]  Barbara M. Bakker,et al.  Drug target identification through systems biology. , 2015, Drug discovery today. Technologies.

[37]  Richard M. R. Coulson,et al.  T1DBase: update 2011, organization and presentation of large-scale data sets for type 1 diabetes research , 2010, Nucleic Acids Res..

[38]  Pranita P. Kore,et al.  Computer-Aided Drug Design: An Innovative Tool for Modeling , 2012 .

[39]  A. W. Schüttelkopf,et al.  PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. , 2004, Acta crystallographica. Section D, Biological crystallography.

[40]  Anna Marabotti,et al.  Free energy of ligand binding to protein: evaluation of the contribution of water molecules by computational methods. , 2004, Current medicinal chemistry.

[41]  R. Hariharan,et al.  GSK3β: role in therapeutic landscape and development of modulators , 2010, British journal of pharmacology.

[42]  Woody Sherman,et al.  Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation , 2009, J. Comput. Aided Mol. Des..

[43]  P. O. Osadebe,et al.  Natural Products as Potential Sources of Antidiabetic Drugs , 2014 .

[44]  S. Agrawal,et al.  T2D-Db: An integrated platform to study the molecular basis of Type 2 diabetes , 2008, BMC Genomics.

[45]  S. Badami,et al.  Anti-diabetic and anti-cataract effects of Chromolaena odorata Linn., in streptozotocin-induced diabetic rats. , 2013, Journal of ethnopharmacology.

[46]  Sunil Kumar Tripathi,et al.  Insights into the structural basis of 3,5-diaminoindazoles as CDK2 inhibitors: prediction of binding modes and potency by QM-MM interaction, MESP and MD simulation. , 2014, Molecular bioSystems.

[47]  Ioannis Xenarios,et al.  The Human Diabetes Proteome Project (HDPP): The 2014 update , 2015 .

[48]  B. Schmidt,et al.  Small-Molecule Inhibitors of GSK-3: Structural Insights and Their Application to Alzheimer's Disease Models , 2012, International journal of Alzheimer's disease.

[49]  W. V. Gunsteren,et al.  Validation of the 53A6 GROMOS force field , 2005, European Biophysics Journal.

[50]  Huijun Sun,et al.  Molecular modeling study of checkpoint kinase 1 inhibitors by multiple docking strategies and prime/MM–GBSA calculation , 2011, J. Comput. Chem..

[51]  Luiz Angelo Steffenel,et al.  Parallel strategies for an inverse docking method , 2013, EuroMPI.

[52]  Wang,et al.  Proteomics in drug discovery. , 1999, Drug discovery today.

[53]  Thierry Langer,et al.  Molecule-pharmacophore superpositioning and pattern matching in computational drug design. , 2008, Drug discovery today.

[54]  Xicheng Wang,et al.  Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation , 2009, BMC Bioinformatics.

[55]  R. Friesner,et al.  Ab initio quantum chemical and mixed quantum mechanics/molecular mechanics (QM/MM) methods for studying enzymatic catalysis. , 2005, Annual review of physical chemistry.

[56]  Michael Williams,et al.  Drug Discovery and Development , 1987, Humana Press.

[57]  E. Asante-Appiah,et al.  Residues Distant from the Active Site Influence Protein-tyrosine Phosphatase 1B Inhibitor Binding* , 2006, Journal of Biological Chemistry.

[58]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[59]  P. Clemons,et al.  Target identification and mechanism of action in chemical biology and drug discovery. , 2013, Nature chemical biology.

[60]  T. França,et al.  Homology modeling: an important tool for the drug discovery , 2015, Journal of biomolecular structure & dynamics.

[61]  E. Jaeger,et al.  Docking: successes and challenges. , 2005, Current pharmaceutical design.

[62]  P. Kollman,et al.  Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models , 1992 .

[63]  Sean Ekins,et al.  Troubleshooting computational methods in drug discovery. , 2010, Journal of pharmacological and toxicological methods.

[64]  H. Michel,et al.  Crystal structure of human cytosolic phosphoenolpyruvate carboxykinase reveals a new GTP-binding site. , 2002, Journal of molecular biology.

[65]  Xiaomin Luo,et al.  PDTD: a web-accessible protein database for drug target identification , 2008, BMC Bioinformatics.

[66]  Philip E. Bourne,et al.  Predicting the Polypharmacology of Drugs: Identifying New Uses through Chemoinformatics, Structural Informatics, and Molecular Modeling‐Based Approaches , 2012 .

[67]  Chris Oostenbrink,et al.  A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force‐field parameter sets 53A5 and 53A6 , 2004, J. Comput. Chem..

[68]  Chenglong Li,et al.  Comparative Docking Assessment of Glucokinase Interactions with its Allosteric Activators , 2008, Current chemical genomics.

[69]  J. Kinser.  Basis Sets , 2018, Image Operators.

[70]  Paul D Lyne,et al.  Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. , 2006, Journal of medicinal chemistry.

[71]  Vince I. Grolmusz,et al.  Identifying diabetes-related important protein targets with few interacting partners with the PageRank algorithm , 2015, Royal Society Open Science.

[72]  Jing Zhang,et al.  Jaguar: A high-performance quantum chemistry software program with strengths in life and materials sciences , 2013 .

[73]  Sona Warrier,et al.  Reverse docking: a powerful tool for drug repositioning and drug rescue. , 2014, Future medicinal chemistry.

[74]  Masaaki Kawata,et al.  Particle mesh Ewald method for three-dimensional systems with two-dimensional periodicity , 2001 .

[75]  Berk Hess,et al.  LINCS: A linear constraint solver for molecular simulations , 1997, J. Comput. Chem..

[76]  Alexander E. Ivliev,et al.  Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach , 2013, PloS one.

[77]  S. Singh,et al.  Molecular modeling studies and comparative analysis on structurally similar HTLV and HIV protease using HIV-PR inhibitors , 2014, Journal of receptor and signal transduction research.

[78]  David J Newman,et al.  Natural products as sources of new drugs over the 30 years from 1981 to 2010. , 2012, Journal of natural products.

[79]  A. Becke.  Density-functional thermochemistry. III. The role of exact exchange , 1993 .