Download PDF
Drug Res (Stuttg) 2020; 70(05): 226-232
DOI: 10.1055/a-1138-8725
Original Article
© Georg Thieme Verlag KG Stuttgart · New York

Use of Graph Based Descriptors for Determination of Structural Features Causing Modulation of Fructose-1,6-Bisphosphatase

Ashwani Kumar
1   Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science & Technology, Hisar, India
,
Manisha,
Kiran Bagri
1   Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science & Technology, Hisar, India
,
Parvin Kumar
2   Department of Chemistry, Kurukshetra University, Kurukshetra, India
› Author Affiliations
Further Information

Publication History

received 22 January 2020

accepted 09 March 2020

Publication Date:
03 April 2020 (online)

Also available at
    Permissions and Reprints

Abstract

Fructose-1,6-bisphosphatase performs a significant function in regulating the blood glucose level in type 2 diabetes by controlling the process gluconeogenesis. In this research work optimal descriptor (graph) based quantitative structural activity relationship studies of a set of 203 fructose-1,6-bisphosphatase has been performed with the help of Monte Carlo optimization. Distribution of compounds into different sets such as training set, invisible training set, calibration set and validation sets resulted in formation of splits. Statistical parameters obtained from quantitative structural activity relationship modeling were good for various designed splits. The statistical parameters such as R2 and Q2 for calibration and validation sets of best split developed were found to be 0.8338, 0.7908 & 0.7920 and 0.7036 respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoters and demoters of the endpoint. Further these structural attributes were used in designing of new fructose-1,6-bisphosphatase inhibitors and molecular docking study was accomplished for the determination of interactions of designed molecules with the enzyme.

Key words

Fructose-1,6-bisphosphatase inhibitors - QSAR - Monte Carlo method - Docking.

Supporting Information

 

  • References

  • 1 Blaslov K. Curcumin-a polyphenol with molecular targets for diabetes control. Endocr Oncol Metab 2017; 3: 43-48
    PubMedGoogle Scholar
  • 2 Poelje PDV, Dang Q, Erion MD. Fructose-1,6-bisphosphatase as a therapeutic target for type 2 diabetes. Drug Discov. Today: Therapeutic Strategies 2007; 4: 103-109
    CrossrefPubMedGoogle Scholar
  • 3 Tiwari N, Thakur AK, Kumar V. et al. Therapeutic targets for diabetes mellitus: An update. Clin Pharmacol Biopharm 2014; 3: 1-10
    PubMedGoogle Scholar
  • 4 Kaushik P, Khokra SL, Rana AC. et al. Pharmacophore Modeling and Molecular Docking Studies on Pinus roxburghii as a Target for Diabetes Mellitus. Adv Bioinf. 2014 http://dx.doi.org/10.1155/2014/903246
    PubMedGoogle Scholar
  • 5 Kaur R, Dahiya L, Kumar M. Fructose-1,6-bisphosphatase inhibitors: A new valid approach for management of type 2 diabetes mellitus. Eur J Med Chem 2017; 141: 473-505
    CrossrefPubMedGoogle Scholar
  • 6 Havale SH, Pal M. Medicinal chemistry approaches to the inhibition of dipeptidyl peptidase-4 for the treatment of type 2 diabetes. Bioorg Med Chem 2009; 17: 1783-1802
    CrossrefPubMedGoogle Scholar
  • 7 Naim MJ, Alam O, Nawaz F. Recent target based discovery of anti-diabetic agents. IJPSR 2015; 6: 4544-4554
    PubMedGoogle Scholar
  • 8 Kitas E, Mohr P, Kuhn B. et al. Sulfonylureido thiazoles as fructose-1,6-bisphosphatase inhibitors for the treatment of Type-2 diabetes. Bioorg Med Chem Lett 2010; 20: 594-599
    CrossrefPubMedGoogle Scholar
  • 9 Hines JK, Fromm HJ, Honzatko RB. Novel allosteric activation site in escherichiacolifructose-1,6-bisphosphatase. ASBMB. 2006 http://www.jbcorg/cgi /doi/10.1074 /jbc
    PubMedGoogle Scholar
  • 10 Golubovi’c M, Lazarevi’c M, Zlatanovi’c D. et al. The anesthetic action of some polyhalogenated ethers−Monte Carlo method based QSAR study. Comput Biol Med 2018; 75: 32-38
    PubMedGoogle Scholar
  • 11 Dearden JC. The history and development of Quantitative Structure-Activity Relationships (QSARs). IJQSPR. 2016; 1: 1-44
    PubMedGoogle Scholar
  • 12 Toropov AA, Toropova AP. CORAL. software available at: http://www.insilico.eu/coral
    PubMedGoogle Scholar
  • 13 Prachayasittikul V, Worachartcheewan A, Toropova AP. et al. Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors. SAR QSAR Environ Res 2017; 28: 1-16
    CrossrefPubMedGoogle Scholar
  • 14 Toropova AP, Toropov AA, Martyanov SE. et al. CORAL: Monte Carlo Method as a tool for the prediction of the bioconcentration factor of industrial pollutants. Mol Inf 2013; 32: 145-154
    CrossrefPubMedGoogle Scholar
  • 15 Toropov AA, Toropova AP, Benfenati E. et al. CORAL: QSPR model of water solubility based on local and global SMILES attributes. Chemosphere 2013; 90: 877-880
    CrossrefPubMedGoogle Scholar
  • 16 Dang Q, Brown BS, Liu Y. et al. Fructose-1,6-bisphosphatase Inhibitors. 1. purine phosphonic acids as novel AMP mimics. J Med Chem 2009; 52: 2880-2898
    CrossrefPubMedGoogle Scholar
  • 17 Dang Q, Reddy KR, Kasibthatla SR. et al. Discovery of Phosphonic acid-containing desaminobenzimidazoles as Fructose 1,6-Bisphosphatase Inhibitors that are Suitable for oral delivery via Prodrugs. J Diabetes Metab. 2010 doi:10.4172/2155-6156.1000105
    PubMedGoogle Scholar
  • 18 Dang Q, Kasibhatla SR, Xiao W. et al. Fructose-1,6-bisphosphatase Inhibitors. 2. design, synthesis, and structure-activity relationship of a series of phosphonic acid containing benzimidazoles that function as 5’-Adenosinemonophosphate (AMP) Mimics. J Med Chem 2010; 53: 441-451
    CrossrefPubMedGoogle Scholar
  • 19 Dang Q, Kasibthatla SR, Jiang T. et al. Oxazole phosphonic acids as fructose 1,6-bisphosphatase inhibitors with potent glucose-lowering activity. Med Chem Commun 2011; 2: 287-290
    CrossrefPubMedGoogle Scholar
  • 20 Dang Q, Liu Y, Cashion DK. et al. Discovery of a series of phosphonic acid-containing thiazoles and orally bioavailable diamide prodrugs that lower glucose in diabetic animals through inhibition of fructose-1,6-bisphosphatase. J Med Chem 2011; 54: 153-165
    CrossrefPubMedGoogle Scholar
  • 21 Toropova AP, Toropov AA, Veselinovic JB. et al. QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method. Eur J Med Chem 2014; 77: 298-305
    CrossrefPubMedGoogle Scholar
  • 22 Chadha N, Jasuja H, Kaur M. et al. Imidazo[1,2- a]pyrazine inhibitors of phosphoinositide 3-kinase alpha (PI3Kα): 3D-QSAR analysis utilizing the Hybrid Monte Carlo algorithm to refine receptor-ligand complexes for molecular alignment. SAR QSAR Environ Res 2014; 25: 221-247
    CrossrefPubMedGoogle Scholar
  • 23 Marvin Sketch v.14.11.17.0 chemAxon, xhemAxon kft. Budapest, Hungary 2014, https://chemaxon.com/products/marvin
    PubMed
  • 24 O’Boyle N, Banck M, James CA. et al. Open Babel: an open chemical toolbox. J Cheminform 2011; 3: 33
    CrossrefPubMedGoogle Scholar
  • 25 Toropov AA, Toropova AP, Como F. et al. Quantitative structure–activity relationship models for bee toxicity. Toxicol Environ Chem. 2016 DOI: 10.1080/02772248.2016.1242006
    PubMedGoogle Scholar
  • 26 Toropov AA, Toropova AP, Martyanov SE. et al. CORAL: Predictions of rate constants of hydroxyl radical reaction using representation of the molecular structure obtained by combination of SMILES and Graph approaches. Chemom Intell Lab Sys 2012; 112: 65-70
    CrossrefPubMedGoogle Scholar
  • 27 Kumar A, Chauhan S. QSAR Differential Model for Prediction of SIRT1 Modulation using Monte Carlo Method. Drug Res 2016; 67: 156-162
    Article in Thieme ConnectPubMedGoogle Scholar
  • 28 Toropov AA, Benfenati E. Correlation weighting of valence shells in QSAR analysis of toxicity. Bioorg Med Chem 2006; 14: 3923-3928
    CrossrefPubMedGoogle Scholar
  • 29 Stoičkov V, Šarić S, Golubović M. et al. Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach. SAR QSAR Environ Res 2018; 29: 503-515
    CrossrefPubMedGoogle Scholar
  • 30 Acharya PGR. Simplified molecular input line entry system-based optimal descrip-tors: QSAR modelling for voltage-gated potassium channel subunit Kv7.2. SAR QSAR Environ Res 2014; 25: 73-90
    CrossrefPubMedGoogle Scholar
  • 31 Kumar A, Chauhan S. Use of Simplified Molecular Input Line Entry System and molecular graph based descriptors in prediction and design of pancreatic lipase inhibitors. Future Med Chem 2018; 10: 1603-1622
    CrossrefPubMedGoogle Scholar
  • 32 Roy K. MLRPlusValidation. software available at: http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/
    PubMedGoogle Scholar
  • 33 Roy K, Kar S, Ambure P. On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst 2015; 145: 22-29
    CrossrefPubMedGoogle Scholar
  • 34 Kumar A, Chauhan S. Monte Carlo method based QSAR modeling of natural lipase inhibitors using hybrid optimal descriptors. SAR QSAR Environ Res 2017; 28: 179-197
    CrossrefPubMedGoogle Scholar
  • 35 Kumar A, Chauhan S. Use of the Monte Carlo Method for OECD Principles-Guided QSAR Modeling of SIRT1 Inhibitors. Arch Pharm Chem Life Sci 2017; 349: 1-9
    PubMedGoogle Scholar
  • 36 Duchowicz PR, Comelli NC, Ortiz EV. et al. QSAR Study for Carcinogenicity in a Large Set of Organic Compounds. Curr Drug Saf 2012; 7: 282-288
    CrossrefPubMedGoogle Scholar
  • 37 Sokolović D, Aleksić D, Milenković V. et al. QSAR modeling of bis-quinolinium and bis-isoquinolinium compounds as acetylcholine esterase inhibitors based on the Monte Carlo method-the implication for Myasthenia gravis treatment. Med Chem Res 2016; 25: 2989-2998
    CrossrefPubMedGoogle Scholar
  • 38 Kumar P, Kumar A, Sindhu J. et al. QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method. Drug Res. 2018 DOI https://doi.org/10.1055/a-0652-5290
    PubMedGoogle Scholar
  • 39 Manisha Chauhan S, Kumar A. et al. Development of prediction model for fructose-1,6-bisphosphatase inhibitors using the Monte Carlo method. SAR and QSAR in Environ Res 2019; 30: 145-159
    CrossrefPubMedGoogle Scholar
  • 40 Nimbhal M, Bagri K, Kumar A. et al. The index of ideality of correlation: A statistical yardstick for better QSAR modeling of glucokinase activators. Struct Chem. 2019 https://doi.org/10.1007/s11224-019-01468-w
    PubMedGoogle Scholar
  • 41 Trott O, Olson AJ. AutoDockVina: Improving the speed and accuracy of docking with a newscoring function, efficient optimization, and multithreading. J Comput Chem 2010; 31: 455-461
    PubMedGoogle Scholar
  • 42 Pedretti A, Villa L, Vistoli G. “VEGA: a versatile program to convert, handle and visualize molecular structure on windows-based pcs”. J Mol Graph 2002; 21: 47-49
    PubMedGoogle Scholar
  • 43 Tsukada T, Takahashi M, Takemoto T. et al. Structure-based drug design of tricyclic 8H-indeno[1,2-d][1,3]thiazoles as potent FBPase inhibitors. Bioorg Med Chem Lett 2010; 20: 1004-1007
    CrossrefPubMedGoogle Scholar
  • 44 Lan P, Wu Z, Chen W. et al. Molecular modeling studies on phosphonic acid-containing thiazole derivatives: Design for fructose-1, 6-bisphosphatase inhibitors. J Mol Model 2011; 18: 973-990
    PubMedGoogle Scholar
  • 45 Discovery Studio Visualizer v19.1.0.18287, Dassault Systemes Biovia Corp, 2018
    PubMed