Docking analysis targeted to the whole enzyme: an application to the prediction of inhibition of PTP1B by thiomorpholine and thiazolyl derivativesFootnote^$

^$Presented at the 9th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources, CMTPI-2017, 27–30 October 2017, Goa, India.

References

• M. Stumvoll, B.J. Goldstein, and T.W. van Haeften, Type 2 diabetes: Principles of pathogenesis and therapy, Lancet 365 (2005), pp. 1333–1346.
   PubMed Web of Science ®Google Scholar
• M. Bodmer, C. Meier, S. Krähenbühl, S.S. Jick, and C.R. Meier, Metformin, sulfonylureas, or other antidiabetes drugs and the risk of lactic acidosis or hypoglycemia a nested case-control analysis, Diabetes Care 31 (2008), pp. 2086–2091.
   PubMed Web of Science ®Google Scholar
• UK Prospective Diabetes Study (UKPDS) Group, Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33), Lancet 352 (1998), pp. 837–853.
   PubMed Web of Science ®Google Scholar
• J. Girard, The incretins: From the concept to their use in the treatment of type 2 diabetes. Part A: Incretins: Concept and physiological functions, Diabetes Metabol. 34 (2008), pp. 550–559.
   PubMed Web of Science ®Google Scholar
• D. Kirpichnikov, S.I. McFarlane, and J.R. Sowers, Metformin: An update, Ann. Intern. Med. 137 (2002), pp. 25–33.
   PubMed Web of Science ®Google Scholar
• H. Hauner, The mode of action of thiazolidinediones, Diab. Metab. Res. Rev. 18 (2002), pp. S10–S15.
   PubMed Web of Science ®Google Scholar
• Y. Lin and Z. Sun, Current views on type 2 diabetes, J. Endocrinol. 204 (2010), pp. 1–11.
   PubMed Web of Science ®Google Scholar
• J.R. Zierath, A. Krook, and H. Wallberg-Henriksson, Insulin action and insulin resistance in human skeletal muscle, Diabetologia 43 (2000), pp. 821–835.
   PubMed Web of Science ®Google Scholar
• M.F. White, Insulin signaling in health and disease, Science 302 (2003), pp. 1710–1711.
   PubMed Web of Science ®Google Scholar
• D.J. Burks and M.F. White, IRS proteins and beta-cell function, Diabetes 50 (2001), p. S140.
   PubMed Web of Science ®Google Scholar
• P. Eleftheriou, The protein tyrosine phosphatase 1b as a drug target for the treatment of diabetes type II. Developing effective and selective PTP1B inhibitors, ChemXpress 2 (2013), pp. 71–84.
   Google Scholar
• M. Rask-Andersen, M.S. Almén, and H.B. Schiöth, Trends in the exploitation of novel drug targets, Nat. Rev. Drug Discov. 10 (2011), pp. 579–590.
   PubMed Web of Science ®Google Scholar
• P. Eleftheriou, A. Geronikaki, D. Hadjipavlou-Litina, P. Vicini, O. Filz, D. Filimonov, V. Poroikov, S. Chaudhaery, K. Roy, and A. Saxena, Fragment-based design, docking, synthesis, biological evaluation and structure-activity relationships of 2-benzo/benzisothiazolimino-5-aryliden-4-thiazolidinones as cycloxygenase/lipoxygenase inhibitors, Eur. J. Med. Chem. 47 (2012), pp. 111–124.
   PubMed Web of Science ®Google Scholar
• Y. Cao, T. Jiang, and T. Girke, A maximum common substructure-based algorithm for searching and predicting drug-like compounds, Bioinformatics 24 (2008), pp. 366–374.
   Web of Science ®Google Scholar
• J. Masciocchi, G. Frau, M. Fanton, M. Sturlese, M. Floris, L. Pireddu, P. Palla, F. Cedrati, P. Rodriguez-Tome, and S. Moro, MMsINC: A large-scale chemoinformatics database, Nucleic Acids Res. 37 (2009), pp. 284–290.
   PubMed Web of Science ®Google Scholar
• A. Geronikaki, A. Lagunin, D. Hadjipavlou-Litina, P. Elefteriou, D. Filimonov, V. Poroikov, I. Alam, and A. Saxena, Computer-aided discovery of anti- inflammatory thiazolidinones with dual cyclooxygenase/lipoxygenase inhibition, J. Med. Chem. 51 (2008), pp. 1601–1609.
   PubMed Web of Science ®Google Scholar
• A. Lagunin, O. Gomazkov, D. Filimonov, T. Gureeva, E. Dilakyan, E. Kugaevskaya, Y. Elisseeva, N. Solovyeva, and V. Poroikov, Computer-aided selection of potential antihypertensive compounds with dual mechanisms of action, J. Med. Chem. 46 (2003), pp. 3326–3332.
   PubMed Web of Science ®Google Scholar
• P. Eleftheriou, A. Petrou, A. Geronikaki, K. Liaras, S. Dirnali, and M. Anna, Prediction of enzyme inhibition and mode of inhibitory action based on calculation of distances between hydrogen bond donor/acceptor groups of the molecule and docking analysis: An application on the discovery of novel effective PTP1B inhibitors, SAR QSAR Environ. Res. 26 (2015), pp. 557–576.
   PubMed Web of Science ®Google Scholar
• S.Y. Huang and X. Zou, Advances and challenges in protein-ligand docking, Int. J. Mol. Sci. 11 (2010), pp. 3016–3034.
   PubMed Web of Science ®Google Scholar
• M. Bello, M. Martínez-Archundia, and J. Correa-Basurto, Automated docking for novel drug discovery, Expert Opin. Drug Discov. 8 (2013), pp. 821–834.
   PubMed Web of Science ®Google Scholar
• A.P. Sarkate, P.R. Murumkar, D.K. Lokwani, A.D. Kandhare, S.L. Bodhankar, D.B. Shinde, and K.G. Bothara, Design of selective TACE inhibitors using molecular docking studies: Synthesis and preliminary evaluation of anti-inflammatory and TACE inhibitory activity, SAR QSAR Environ. Res. 26 (2015), pp. 905–923.
   PubMed Web of Science ®Google Scholar
• P.W. Feinstein and M. Brylinski, Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets, J. Cheminform. 7 (2015), p. 18.
   PubMed Web of Science ®Google Scholar
• Z. Bikadi and E. Hazai, Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock, J. Cheminf. 11 (2009), pp. 1–15.
   Google Scholar
• G.M. Morris, D.S. Goodsell, R. Halliday, R. Huey, W.E. Hart, R.K. Belew, and A.J. Olson, Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function, J. Comp. Chem. 19 (1998), pp. 1639–1662.
   Web of Science ®Google Scholar
• F.J. Solis and R.J.B. Wets, Minimization by random search techniques, Math. Operat. Res. 6 (1998), pp. 19–30.
   Google Scholar
• P. Theodosis-Nobelos, P.Ν. Kourounakis, and E.A. Rekka, Anti-inflammatory and hypolipidemic effect of novel conjugates with trolox and other antioxidant acids, Med. Chem. 13 (2017), pp. 214–225.
   PubMed Web of Science ®Google Scholar
• P. Theodosis-Nobelos, M. Kourti, A. Gavalas, and E.A. Rekka, Amides of non-steroidal anti-inflammatory drugs with thiomorpholine can yield hypolipidemic agents with improved anti-inflammatory activity, Bioorg. Med. Chem. Lett. 26 (2016), pp. 910–913.
   PubMed Web of Science ®Google Scholar
• G.N. Ziakas, E.A. Rekka, A.M. Gavalas, P.T. Eleftheriou, and P.N. Kourounakis, New analogues of butylated hydroxytoluene as anti-inflammatory and antioxidant agents, Bioorg. Med. Chem. Lett. 14 (2006), pp. 5616–5624.
   Google Scholar
• E.A. Correa, E.D. Högestätt, O. Sterner, F. Echeverri, and P.M. Zygmunt, In vitro TRPV1 activity of piperine derived amide, Bioorg. Med. Chem. 18 (2010), pp. 3299–3306.
   PubMed Web of Science ®Google Scholar
• A. Petrou, A. Geronikaki, E. Terzi, O.O. Guler, T. Tuccinardi, and C.T. Supuran, Inhibition of carbonic anhydrase isoforms I, II, IX and XII with secondary sulfonamides incorporating benzothiazole scaffolds, J. Enzyme Inhib. Med. Chem. 31 (2016), pp. 1306–1311.
   PubMed Web of Science ®Google Scholar
• A. Geronikaki, P. Eleftheriou, P. Vicini, I. Alam, A. Dixit, and A.K. Saxena, 2-thiazolylimino/heteroarylimino-5-arylidene-4-thiazolidinones as new agents with shp-2 inhibitory action, J. Med. Chem. 51 (2008), pp. 5221–5228.
   PubMed Web of Science ®Google Scholar
• R. Cer, U. Mudunuri, R. Stephens, and F. Lebeda, IC50-to-Ki: A web-based tool for converting IC50 to Ki values for inhibitors of enzyme activity and ligand binding, Nucleic Acids Res. 37 (2009) Web Server issue W441–W445.
   PubMed Web of Science ®Google Scholar