Identification of Non-Zinc Binding Inhibitors of MMP-2 Through Virtual Screening and Subsequent Rescoring

Abstract

MMP-2 belongs to a large family of proteases called matrix metalloproteinases (MMPs) that degrades type IV collagen, the main structural component of basement membranes and gelatin. The main pathologic role of MMP-2 overexpression is to contribute to the development of cancer through the progression of metastasis and angiogenesis. A structure-based virtual screening was employed to find new inhibitors with possible selectivity for MMP-2. The inhibitory activities of 3 inhibitors (one was not a suitable drug-like hit) among 19 purchased compounds were approved by enzyme inhibition assay. 5 hits were non-zinc-binding inhibitors of MMP-2. The results demonstrated that a computer-aided drug design could be successfully applied for discovering new MMP-2 inhibitors. We found inhibitors with new scaffolds for the inhibition of MMP-2 with some selectivity features that could be used for future lead optimization processes. According to the docked pose and MD simulation, compound 13 was expected to interact with the S1′ specificity loop of MMP-2 and had 2 π–π interactions and a stable hydrogen bond with the MMP-2 active site. The key feature of compound 13 could be used to guide the design of new non-zinc-binding inhibitors of MMP-2.

Key words

cancer - drug research - proteases

• References

• 1 Hadizadeh F, Shamsara J. Receptor-based 3D-QSAR approach to find selectivity features of flexible similar binding sites: Case study on MMP-12/MMP-13. Int J Bioinform Res Appl 2015; 11: 326-346
• 2 Pirard B. Insight into the structural determinants for selective inhibition of matrix metalloproteinases. Drug Discov Today 2007; 12: 640-646
• 3 Shamsara J, Shahir-Sadr A. A Predictive HQSAR Model for a Series of Tricycle Core Containing MMP-12 Inhibitors with Dibenzofuran Ring. Int J Med Chem 2014; 2014:
• 4 Turk B.. Targeting proteases: Successes, failures and future prospects. Nat Rev Drug Discov 2006; 5: 785-799
• 5 Vargova V, Pytliak M, Mechirova V. Matrix metalloproteinases. EXS 2012; 103: 1-33
• 6 Ramezani M, Shamsara J. An integrated structure- and pharmacophore-based MMP-12 virtual screening. Mol Divers 2017 In Press
• 7 Ramezani M, Shamsara J. Virtual Screening on MMP-13 Led to discovering new inhibitors including a non-zinc binding and a micro molar one: A successful example of receptor selection according to cross-docking results for a flexible enzyme. Comb Chem High Throughput Screen 2017; 20: 719-725
• 8 Chaudhary AK, Pandya S, Ghosh K. et al. Matrix metalloproteinase and its drug targets therapy in solid and hematological malignancies: an overview. Mutation research 2013; 753: 7-23
• 9 Foda HD, Zucker S. Matrix metalloproteinases in cancer invasion, metastasis and angiogenesis. Drug Discov Today 2001; 6: 478-482
• 10 Klein G, Vellenga E, Fraaije MW. et al. The possible role of matrix metalloproteinase (MMP)-2 and MMP-9 in cancer, e. g. acute leukemia. Crit Rev Oncol Hematol 2004; 50: 87-100
• 11 Ren F, Tang R, Zhang X. et al. Overexpression of MMP family members functions as prognostic biomarker for breast cancer patients: A systematic review and meta-analysis. PloS one 2015; 10: e0135544
• 12 Shen W, Xi H, Wei B. et al. The prognostic role of matrix metalloproteinase 2 in gastric cancer: A systematic review with meta-analysis. Journal of cancer research and clinical oncology 2014; 140: 1003-1009
• 13 Fabre B, Ramos A, de Pascual-Teresa B. Targeting matrix metalloproteinases: Exploring the dynamics of the s1' pocket in the design of selective, small molecule inhibitors. Journal of medicinal chemistry 2014; 57: 10205-10219
• 14 Ramezani M, Shamsara J. A cross-docking study on matrix metalloproteinase family. Antiinflamm Antiallergy Agents Med Chem 2015; 14: 164-171
• 15 Shamsara J. Considering rotatability of hydroxyl groups for the active site residues of MMP-13 in retrospective virtual screening campaigns. Open Med Chem J 2016; 10: 1-6
• 16 Shamsara J. CrossDocker: A tool for performing cross-docking using Autodock Vina. SpringerPlus 2016; 5: 1
• 17 Puranen JS, Vainio MJ, Johnson MS. Accurate conformation-dependent molecular electrostatic potentials for high-throughput in silico drug discovery. J Comput Chem 2010; 31: 1722-1732
• 18 Vainio MJ, Johnson MS. Generating Conformer Ensembles Using a Multiobjective Genetic Algorithm. J Chem Inf Model 2007; 47: 2462-2474
• 19 Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry 2010; 31: 455-461
• 20 Eldridge MD, Murray CW, Auton TR. et al. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 1997; 11: 425-445
• 21 Neudert G, Klebe G. DSX: A knowledge-based scoring function for the assessment of protein-ligand complexes. J Chem Inf Model 2011; 51: 2731-2745
• 22 Morris GM, Huey R, Lindstrom W. et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009; 30: 2785-2791
• 23 Shamsara J. Evaluation of 11 Scoring Functions Performance on Matrix Metalloproteinases. Int J Med Chem 2014; 2014:
• 24 Gaulton A, Bellis LJ, Bento AP. et al. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res 2012; 40 Database issue D1100-1107
• 25 Wallach I, Lilien R. Virtual decoy sets for molecular docking benchmarks. J Chem Inf Model 2011; 51: 196-202
• 26 O'Boyle NM, Banck M, James CA. et al. Open babel: An open chemical toolbox. J Cheminform 2011; 3: 33
• 27 Wang R, Wang S. How does consensus scoring work for virtual library screening? an idealized computer experiment. J Chem Inf Comput Sci 2001; 41: 1422-1426
• 28 Ammazzalorso A, De Filippis B, Campestre C. et al. Seeking for non-zinc-binding mmp-2 inhibitors: Synthesis, biological evaluation and molecular modelling studies. International journal of molecular sciences 2016; 17: 1768
• 29 Pizio AD, Laghezza A, Tortorella P et al. Probing the S1' Site for the Identification of Non-Zinc-Binding MMP-2 Inhibitors. Chem Med Chem 2013
• 30 Santamaria S, Nuti E, Cercignani G. et al. Kinetic characterization of 4,4'-biphenylsulfonamides as selective non-zinc binding MMP inhibitors. Journal of enzyme inhibition and medicinal chemistry 2015; 30: 947-954
• 31 Song J, Peng P, Chang J. et al. Selective non-zinc binding MMP-2 inhibitors: Novel benzamide Ilomastat analogs with anti-tumor metastasis. Bioorganic & medicinal chemistry letters 2016; 26: 2174-2178
• 32 Nanjan P, Nambiar J, Nair BG. et al. Synthesis and discovery of (I-3,II-3)-biacacetin as a novel non-zinc binding inhibitor of MMP-2 and MMP-9. Bioorganic & medicinal chemistry 2015; 23: 3781-3787
• 33 Phillips JC, Braun R, Wang W. et al. Scalable molecular dynamics with NAMD. J Comput Chem 2005; 26: 1781-1802
• 34 Jayaram B, Singh T, Mukherjee G. et al. Sanjeevini: A freely accessible web-server for target directed lead molecule discovery. BMC bioinformatics 2012; 13: 1-13
• 35 Lipinski CA. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov Today Technol 2004; 1: 337-341
• 36 Nara H, Sato K, Naito T. et al. Discovery of novel, highly potent, and selective quinazoline-2-carboxamide-based matrix metalloproteinase (MMP)-13 inhibitors without a zinc binding group using a structure-based design approach. J Med Chem 2014; 57: 8886-8902
• 37 Yadav RK, Gupta SP, Sharma PK. et al. Recent advances in studies on hydroxamates as matrix metalloproteinase inhibitors: A review. Curr Med Chem 2011; 18: 1704-1722
• 38 Tallant C, Marrero A, Gomis-Ruth FX. Matrix metalloproteinases: fold and function of their catalytic domains. Biochim Biophys Acta 2010; 1803: 20-28
• 39 Gupta SP, Patil VM. Specificity of binding with matrix metalloproteinases. EXS 2012; 103: 35-56
• 40 Jacobsen JA, Major Jourden JL, Miller MT. et al. To bind zinc or not to bind zinc: An examination of innovative approaches to improved metalloproteinase inhibition. Biochim Biophys Acta 2010; 1803: 72-94
• 41 Dorman G, Cseh S, Hajdu I. et al. Matrix metalloproteinase inhibitors: A critical appraisal of design principles and proposed therapeutic utility. Drugs 2010; 70: 949-964
• 42 Scior T, Bender A, Tresadern G. et al. Recognizing pitfalls in virtual screening: A critical review. J Chem Inf Model 2012; 52: 867-881
• 43 Pettersen EF, Goddard TD, Huang CC. et al. UCSF Chimera – a visualization system for exploratory research and analysis. J Comput Chem 2004; 25: 1605-1612