Abstract
Background: Matrix metalloproteinase-9 (MMP-9) plays a crucial role in the development and progression of cancer. Therefore, identifying its inhibitors has enjoyed numerous attentions. In this report, a hybrid approach, including pharmacophore-based virtual screening, docking studies, and density functional theory (DFT) binding energy calculations followed by molecular dynamics simulations, was used to identify potential MMP-9 inhibitors.
Methods: Pharmacophore modeling based on ARP101, as a known MMP-9 inhibitor, was performed and followed by virtual screening of ZINC database and docking studies to introduce a set of new ligands as candidates for potent inhibitors of MMP-9. The binding energies of MMP-9 and the selected ligands as well as ARP101, were estimated via the DFT energy calculations. Subsequently, molecular dynamics simulations were applied to evaluate and compare the behavior of ARP101 and the selected ligand in a dynamic environment.
Results: (S,Z)-6-(((2,3-dihydro-1H-benzo[d]imidazol-2-yl)thio)methylene)-2-((4,6,7- trimethylquinazolin- 2-yl)amino)-1,4,5,6-tetrahydropyrimidin-4-ol, ZINC63611396, with the largest DFT binding energy, was selected as a proper potent MMP-9 inhibitor. Molecular dynamics simulations indicated that the new ligand was stable in the active site.
Conclusion: The results of this study revealed that compared to the binding energies achieved from the docking studies, the binding energies obtained from the DFT calculations were more consistent with the intermolecular interactions. Also, the interaction between the Zinc ion and ligand, in particular the Zn2+-ligand distance, played a profound role in the quantity of DFT binding energies.
Keywords: Drug discovery, matrix metalloproteinase-9 inhibitor, virtual screening, DFT energy calculation, molecular dynamics simulations. density functional theory.
Graphical Abstract
[1]
Ferlay, J.; Steliarova-Foucher, E.; Lortet-Tieulent, J.; Rosso, S.; Coebergh, J.W.W.; Comber, H.; Forman, D.; Bray, F. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries in 2012. Eur. J. Cancer, 2013, 49(6), 1374-1403.
[2]
Siegel, R.L. Miller. K.D.; Fedewa. S.A.; Ahnen. D.J.; Meester. R.G.S.; Barzi. A.; Jemal. A. Colorectal cancer statistics. CA Cancer J. Clin., 2017, 67(3), 177-193.
[3]
van Zijl, F.; Krupitza, G.; Mikulits, W. Initial steps of metastasis: Cell invasion and endothelial transmigration. Mutat. Res. Rev. Mutat. Res., 2011, 728(1), 23-34.
[4]
Jiang, W.G.; Sanders, A.J.; Katoh, M.; Ungefroren, H.; Gieseler, F.; Prince, M.; Thompson, S.; Zollo, M.; Spano, D.; Dhawan, P. Tissue invasion and metastasis: Molecular, biological and clinical perspectives. Semin. Cancer Biol., 2015, 35, S244-S275.
[5]
Mehner, C.; Hockla, A.; Miller, E.; Ran, S.; Radisky, D.C.; Radisky, E.S. Tumor cell-produced matrix metalloproteinase 9 (MMP-9) drives malignant progression and metastasis of basal-like triple negative breast cancer. Oncotarget, 2014, 5(9), 2736.
[6]
Bai, X.; Li, Y-y.; Zhang, H-y.; Wang, F.; He, H-l.; Yao, J-c.; Liu, L.; Li, S-S. Role of matrix metalloproteinase-9 in transforming growth factor-β1-induced epithelial–mesenchymal transition in esophageal squamous cell carcinoma. OncoTargets Ther., 2017, 10, 2837.
[7]
Liu, Z.; Li, L.; Yang, Z.; Luo, W.; Li, X.; Yang, H.; Yao, K.; Wu, B.; Fang, W. Increased expression of MMP9 is correlated with poor prognosis of nasopharyngeal carcinoma. BMC Cancer, 2010, 10(1), 270.
[8]
Marečko, I.; Cvejić, D.; Šelemetjev, S.; Paskaš, S.; Tatić, S.; Paunović, I.; Savin, S. Enhanced activation of matrix metalloproteinase-9 correlates with the degree of papillary thyroid carcinoma infiltration. Croat. Med. J., 2014, 55(2), 128-137.
[9]
Salehabadi, H.; Khajeh, K.; Dabirmanesh, B.; Biglar, M.; Mohseni, S.; Amanlou, M. Surface plasmon resonance based biosensor for discovery of new matrix metalloproteinase-9 inhibitors. Sens. Actuators B Chem., 2018, 263, 143-150.
[10]
Ndinguri, M.W.; Bhowmick, M.; Tokmina-Roszyk, D.; Robichaud, T.K.; Fields, G.B. Peptide-based selective inhibitors of matrix metalloproteinase-mediated activities. Molecules, 2012, 17(12), 14230-14248.
[11]
Srivastava, P.; Tiwari, A. Critical role of computer simulations in drug discovery and development. Curr. Top. Med. Chem., 2017, 17(21), 2422-2432.
[12]
Ou-Yang, S-s.; Lu, J-y.; Kong, X-q.; Liang, Z-j.; Luo, C.; Jiang, H. Computational drug discovery. Acta Pharmacol. Sin., 2012, 33(9), 1131.
[13]
Leelananda, S.P.; Lindert, S. Computational methods in drug discovery. Beilstein J. Org. Chem., 2016, 12, 2694-2718.
[14]
Meng, X-Y.; Zhang, H-X.; Mezei, M.; Cui, M. Molecular docking: A powerful approach for structure-based drug discovery. Curr. Comput. Aided Drug Des., 2011, 7(2), 146-157.
[15]
Ferreira, L.; dos Santos, R.; Oliva, G.; Andricopulo, A. Molecular docking and structure-based drug design strategies. Molecules, 2015, 20(7), 13384.
[16]
Guedes, I.A.; de Magalhães, C.S.; Dardenne, L.E. Receptor–ligand molecular docking. Biophys. Rev., 2014, 6(1), 75-87.
[17]
Klebe, G. Virtual ligand screening: Strategies, perspectives and limitations. Drug Discov. Today, 2006, 11(13-14), 580-594.
[18]
David, L.; Nielsen, P.A.; Hedstrom, M.; Norden, B. Scope and limitation of ligand docking: Methods, scoring functions and protein targets. Curr. Comput. Aided Drug Des., 2005, 1(3), 275-306.
[19]
Peters, M.B.; Merz, K.M. Semiempirical comparative binding energy analysis (SE-COMBINE) of a series of trypsin inhibitors. . J. Chem. Theory Comput., 2006, 2(2), 383-399.
[20]
Ara, A.; Kadoya, R.; Ishimura, H.; Shimamura, K.; Sylte, I.; Kurita, N. Specific interactions between zinc metalloproteinase and its inhibitors: Ab initio fragment molecular orbital calculations. J. Mol. Graph. Model., 2017, 75, 277-286.
[21]
Irwin, J.J.; Sterling, T.; Mysinger, M.M.; Bolstad, E.S.; Coleman, R.G. ZINC: A free tool to discover chemistry for biology. J. Chem. Inf. Model., 2012, 52(7), 1757-1768.
[22]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791.
[23]
Wang, J.; Wang, W.; Kollman, P.A.; Case, D.A. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graph. Model., 2006, 25(2), 247-260.
[24]
Abbasi, M.; Sadeghi-Aliabadi, H.; Hassanzadeh, F.; Amanlou, M. Prediction of dual agents as an activator of mutant p53 and inhibitor of Hsp90 by docking, molecular dynamic simulation and virtual screening. J. Mol. Graph. Model., 2015, 61(Suppl. C), 186-195.
[25]
Cosconati, S.; Forli, S.; Perryman, A.L.; Harris, R.; Goodsell, D.S.; Olson, A.J. Virtual screening with autodock: Theory and practice. Expert Opin. Drug Discov., 2010, 5(6), 597-607.
[26]
Morris, G.M.; Green, L.G.; Radić, Z.; Taylor, P.; Sharpless, K.B.; Olson, A.J.; Grynszpan, F. Automated docking with protein flexibility in the design of femtomolar “click chemistry” inhibitors of acetylcholinesterase. J. Chem. Inf. Model., 2013, 53(4), 898-906.
[27]
Shirgahi-Talari, F.; Bagherzadeh, K.; Golestanian, S.; Jarstfer, M.; Amanlou, M. Potent human telomerase inhibitors: molecular dynamic simulations, multiple pharmacophore-based virtual screening, and biochemical assays. J. Chem. Inf. Model., 2015, 55(12), 2596-2610.
[28]
Wolber, G.; Langer, T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model., 2005, 45(1), 160-169.
[29]
Pronk, S.; Páll, S.; Schulz, R.; Larsson, P.; Bjelkmar, P.; Apostolov, R.; Shirts, M.R.; Smith, J.C.; Kasson, P.M.; van der Spoel, D.; Hess, B.; Lindahl, E. GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 2013, 29(7), 845-854.
[30]
Schuttelkopf, A.W.; van Aalten, D.M.F. PRODRG: A tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr. D ., 2004, 60(8), 1355-1363.
[31]
Søndergaard, C.R.; Olsson, M.H.M.; Rostkowski, M.; Jensen, J.H. Improved treatment of ligands and coupling effects in empirical calculation and rationalization of pKa values. J. Chem. Theory Comput., 2011, 7(7), 2284-2295.
[32]
Frisch, M.J.; Trucks, G.W.; Schlegel, H.B.; Scuseria, G.E.; Robb, M.A.; Cheeseman, J.R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G.A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H.P.; Izmaylov, A.F.; Bloino, J.; Zheng, G.; Sonnenberg, J.L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, J.A.; Peralta, J.E.; Ogliaro, F.; Bearpark, M.; Heyd, J.J.; Brothers, E.; Kudin, K.N.; Staroverov, V.N.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A.; Burant, J.C.; Iyengar, S.S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, J.M.; Klene, M.; Knox, J.E.; Cross, J.B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R.E.; Yazyev, O.; Austin, A.J.; Cammi, R.; Pomelli, C.; Ochterski, J.W.; Martin, R.L.; Morokuma, K.; Zakrzewski, V.G.; Voth, G.A.; Salvador, P.; Dannenberg, J.J.; Dapprich, S.; Daniels, A.D. Farkas; Foresman, J.B.; Ortiz, J.V.; Cioslowski, J.; Fox; D.J. Gaussian, Inc.: Wallingford, CT, 2009.
[33]
Makarewicz, T.; Kaźmierkiewicz, R. Molecular dynamics simulation by GROMACS using GUI plugin for PyMOL. J. Chem. Inf. Model., 2013, 53(5), 1229-1234.
[35]
Rao, S.N.; Head, M.S.; Kulkarni, A.; LaLonde, J.M. Validation studies of the site-directed docking program libdock. J. Chem. Inf. Model., 2007, 47(6), 2159-2171.
[36]
Koes, D.R.; Camacho, C.J. ZINCPharmer: Pharmacophore search of the ZINC database. Nucleic Acids Res., 2012, 40, W409-W414.
[37]
Yamamoto, D.; Takai, S.; Miyazaki, M. Prediction of interaction mode between a typical ACE inhibitor and MMP-9 active site. Biochem. Biophys. Res. Commun., 2007, 354(4), 981-984.
[38]
Eckhard, U.; Huesgen, P.F.; Schilling, O.; Bellac, C.L.; Butler, G.S.; Cox, J.H.; Dufour, A.; Goebeler, V.; Kappelhoff, R.; Keller, U.A.D.; Klein, T.; Lange, P.F.; Marino, G.; Morrison, C.J.; Prudova, A.; Rodriguez, D.; Starr, A.E.; Wang, Y.; Overall, C.M. Active site specificity profiling of the matrix metalloproteinase family: Proteomic identification of 4300 cleavage sites by nine MMPs explored with structural and synthetic peptide cleavage analyses. Matrix Biol., 2016, 49(Suppl. C), 37-60.
Article Metrics
34
3