The glyoxalase system, which is composed of two zinc metalloenzymes, glyoxalase I (GLO-I) and glyoxalase II (GLO-II) and a catalytic amount of GSH, has a pivotal role in the detoxification of cytotoxic methylglyoxal (MG), a glycolytic side product. Recent studies have revealed the overexpression of GLO-I in various cancer types such as breast carcinoma, invasive bladder, colon, prostate, and lung cancers. Consequently, GLO-I has become a validated target for the development of novel anticancer agents. In this study we were aiming to identify potent GLO-I inhibitors as potential candidates for the development of effective anticancer therapeutics using an integrated ligand- and structure-based drug design approach. A set of selective pharmacophore models was generated using an in-house tested set of flavonoids and used in virtual screening of Maybridge and Aldrich databases, collectively containing more than 64,000 compounds. Filtration of retained hits resulted in 362 compounds that were docked into the active site of the GLO-I enzyme. Then, the top 30% of docked compounds were visually inspected and 32 compounds were purchased and biologically evaluated. Five compounds showed good to excellent inhibitory activities with the most active one (compound 14, ID number ST018515) showing an IC50 of 336 nM. A high hit rate of actives implies the success of our approach. The five active compounds with considerable structural diversity were identified as novel leads that can be further optimized towards designing potent GLO-I inhibitors.
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GraphPad Prism 6 (2012) Calculation and plotting of the % enzyme inhibition and inhibitors IC50’s. GraphPad Prism, La Jolla, CA
Al-Balas Q, Hassan M, Al-Oudat B, Alzoubi H, Mhaidat N, Almaaytah A (2012) Generation of the first structure-based pharmacophore model containing a selective “zinc binding group” feature to identify potential glyoxalase-1 inhibitors. Molecules 17:13740–13758
Al-Balas QA, Hassan MA, Al-Shar’i NA, El-Elimat T, Almaaytah AM (2018) Computational and experimental exploration of the structure−activity relationships of flavonoids as potent glyoxalase-I inhibitors. Drug Dev Res 79:58–69
Al-Balas QA, Hassan MA, Al-Shar’i NA, Mhaidat NM, Almaaytah AM, Al-Mahasneh FM, Isawi IH (2016) Novel glyoxalase-i inhibitors possessing a “zinc-binding feature” as potential anticancer agents. Drug Des Devel Ther 10:2623
Al-Balas QA, Hassan MA, AlJabal GA, Al-Shar’i NA, Almaaytah AM, El-Elimat T (2017) Novel thiazole carboxylic acid derivatives possessing a “zinc binding feature” as potential human glyoxalase-I inhibitors. Lett Drug Des Discov 14:1324–1334
Al-Sha’er MA, Al-Balas QA, Hassan MA, Al Jabal GA, Almaaytah AM (2019) Combination of pharmacophore modeling and 3D-QSAR analysis of potential glyoxalase-I inhibitors as anticancer agents. Comput Biol Chem 80:102–110
Al-Shar’i NA, Al-Balas QA, Al-Waqfi RA, Hassan MA, Alkhalifa AE, Ayoub NM (2019) Discovery of a nanomolar inhibitor of the human glyoxalase-I enzyme using structure-based poly-pharmacophore modelling and molecular docking. J Comput Aid Mol Des 33:799–815
Barnum D, Greene J, Smellie A, Sprague P (1996) Identification of common functional configurations among molecules. J Chem Inf Comput Sci 36:563–571
Biovia DS (2017) Discovery Studio Modeling Environment, Pipeline Pilot Client. Dassault Systèmes, San Diego
Böhm H-J, Klebe G (1996) What can we learn from molecular recognition in protein–ligand complexes for the design of new drugs? Angew Chem Int Ed Engl 35:2588–2614
Brooks BR, Brooks III CL, Mackerell Jr AD, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545–1614
Cameron AD, Olin B, Ridderström M, Mannervik B, Jones TA (1997) Crystal structure of human glyoxalase I-evidence for gene duplication and 3D domain swapping. EMBO J 16:3386–3395
Cameron AD, Ridderström M, Olin B, Kavarana MJ, Creighton DJ, Mannervik B (1999) Reaction mechanism of glyoxalase I explored by an X-ray crystallographic analysis of the human enzyme in complex with a transition state analogue. Biochemistry 38:13480–13490
Chiba T, Ohwada J, Sakamoto H, Kobayashi T, Fukami TA, Irie M, Miura T, Ohara K, Koyano H (2012) Design and evaluation of azaindole-substituted N-hydroxypyridones as glyoxalase I inhibitors. Bioorg Med Chem Lett 22:7486–7489
Davidson SD, Cherry JP, Choudhury MS, Tazaki H, Mallouh C, Konno S (1999) Glyoxalase I activity in human prostate cancer: a potential marker and importance in chemotherapy. J Urol 161:690–691
DeLano WL (2002) The PyMOL Molecular Graphics System. DeLano Scientific, San Carlos, CA
Eckert H, Bajorath J (2007) Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Disco Today 12:225–233
Guner OF (2000) Pharmacophore perception, development, and use in drug design. International University Line, La Jolla, CA
Khedkar SA, Malde AK, Coutinho EC, Srivastava S (2007) Pharmacophore modeling in drug discovery and development: an overview. Med Chem 3:187–197
Liu M, Yuan M, Luo M, Bu X, Luo H-B, Hu X (2010) Binding of curcumin with glyoxalase I: molecular docking, molecular dynamics simulations, and kinetics analysis. Biophys Chem 147:28–34
Loving K, Alberts I, Sherman W (2010) Computational approaches for fragment-based and de novo design. Curr Top Med Chem 10:14–32
Maybridge (2017) Maybridge Screening Collection. https://www.maybridge.com.
Mearini E, Romani R, Mearini L, Antognelli C, Zucchi A, Baroni T, Porena M, Talesa V (2002) Differing expression of enzymes of the glyoxalase system in superficial and invasive bladder carcinomas. Eur J Cancer 38:1946–1950
Poptodorov K, Luu T, Hoffmann R (2006) Pharmacophore model generation software tools. In: Mannhold R, Kubinyi H, Folkers G, Langer T, Hoffmann R (eds) Pharmacophores and pharmacophore searches. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 17–44
Purushottamachar P, Patel JB, Gediya LK, Clement OO, Njar VC (2012) First chemical feature-based pharmacophore modeling of potent retinoidal retinoic acid metabolism blocking agents (RAMBAs): identification of novel RAMBA scaffolds. Eur J Med Chem 47:412–423
Ranganathan S, Tew KD (1993) Analysis of glyoxalase-I from normal and tumor tissue. Biochim Biophys Acta, Mol Basis Dis 1182:311–316
Rees DC, Congreve M, Murray CW, Carr R (2004) Fragment-based lead discovery.Nat Rev Drug Discov 3:660
Rulli A, Carli L, Romani R, Baroni T, Giovannini E, Rosi G, Talesa V (2001) Expression of glyoxalase I and II in normal and breast cancer tissues. Breast Cancer Res Treat 66:67–72
Sakamoto H, Mashima T, Kizaki A, Dan S, Hashimoto Y, Naito M, Tsuruo T (2000) Glyoxalase I is involved in resistance of human leukemia cells to antitumor agent-induced apoptosis. Blood 95:3214–3218
Sakamoto H, Mashima T, Sato S, Hashimoto Y, Yamori T, Tsuruo T (2001) Selective activation of apoptosis program by Sp-bromobenzylglutathione cyclopentyl diester in glyoxalase I-overexpressing human lung cancer cells. Clin Cancer Res 7:2513–2518
Sakkiah S, Thangapandian S, John S, Lee KW (2011) Pharmacophore based virtual screening, molecular docking studies to design potent heat shock protein 90 inhibitors. Eur J Med Chem 46:2937–2947
Sigma-Aldrich. Screening Compounds: MyriaScreen Diversity Collection. https://www.sigmaaldrich.com/chemistry/chemistry-services/high-throughput-screening/screening-compounds.html
Sousa Silva M, Gomes Ricardo A, Ferreira Antonio EN, Ponces Freire A, Cordeiro C (2013) The glyoxalase pathway: the first hundred years… and beyond. Biochem J 453:1–15
Spassov VZ, Flook PK, Yan L (2008) LOOPER: a molecular mechanics-based algorithm for protein loop prediction. Protein Eng Des Sel 21:91–100
Spassov VZ, Yan L (2008) A fast and accurate computational approach to protein ionization. Protein Sci 17:1955–1970
Takasawa R, Takahashi S, Saeki K, Sunaga S, Yoshimori A, Tanuma S-i (2008) Structure-activity relationship of human GLO I inhibitory natural flavonoids and their growth inhibitory effects. Bioorg Med Chem 16:3969–3975
Takasawa R, Tao A, Saeki K, Shionozaki N, Tanaka R, Uchiro H, Takahashi S, Yoshimori A, Tanuma S-i (2011) Discovery of a new type inhibitor of human glyoxalase I by myricetin-based 4-point pharmacophore. Bioorg Med Chem Lett 21:4337–4342
Thangapandian S, John S, Sakkiah S, Lee KW (2010) Ligand and structure based pharmacophore modeling to facilitate novel histone deacetylase 8 inhibitor design. Eur J Med Chem 45:4409–4417
Thornalley PJ (1990) The glyoxalase system: new developments towards functional characterization of a metabolic pathway fundamental to biological life. Biochem J 269:1–11
Thornalley PJ (1993) The glyoxalase system in health and disease. Mol Asp Med 14:287–371
Triballeau N, Acher F, Brabet I, Pin J-P, Bertrand H-O (2005) Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48:2534–2547
Vince R, Daluge S, Wadd WB (1971) Inhibition of glyoxalase I by S-substituted glutathiones. J Med Chem 14:402–404
Wilson GL, Lill MA (2011) Integrating structure-based and ligand-based approaches for computational drug design. Future Med Chem 3:735–750
Wu G, Robertson DH, Brooks CL, Vieth M (2003) Detailed analysis of grid-based molecular docking: a case study of CDOCKER—A CHARMm-based MD docking algorithm. J Comput Chem 24:1549–1562
Xue M, Rabbani N, Thornalley PJ (2011) Glyoxalase in ageing. Semin Cell Dev Biol 22:293–301
Yang S-Y (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Disco Today 15:444–450
Yao T-T, Xie J-F, Liu X-G, Cheng J-L, Zhu C-Y, Zhao J-H, Dong X-W (2017) Integration of pharmacophore mapping and molecular docking in sequential virtual screening: towards the discovery of novel JAK2 inhibitors. RSC Adv 7:10353–10360
The authors wish to thank the Deanship of Scientific Research at Jordan University of Science and Technology for financial support (grant number 20170276).
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Al-Shar’i, N.A., Al-Rousan, E.K., Fakhouri, L.I. et al. Discovery of a nanomolar glyoxalase-I inhibitor using integrated ligand-based pharmacophore modeling and molecular docking. Med Chem Res 29, 356–376 (2020). https://doi.org/10.1007/s00044-019-02486-3
- Zinc Binding
- Ligand-based pharmacophore modeling
- Molecular docking