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Discovery of new \({\varvec{Mycobacterium~tuberculosis}}\) proteasome inhibitors using a knowledge-based computational screening approach

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An Erratum to this article was published on 26 October 2015

Abstract

Mycobacterium tuberculosis bacteria are cause deadly infections in patients. The rise of multidrug resistance associated with tuberculosis further makes the situation worse in treating the disease. M. tuberculosis proteasome is necessary for the pathogenesis of the bacterium validated as an anti-tubercular target, thus making it an attractive enzyme for designing Mtb inhibitors. In this study, a computational screening approach was applied to identify new proteasome inhibitor candidates from a library of 50,000 compounds. This chemical library was procured from the ChemBridge (20,000 compounds) and the ChemDiv (30,000 compounds) databases. After a detailed analysis of the computational screening results, 50 in silico hits were retrieved and tested in vitro finding 15 compounds with \(\hbox {IC}_{50}\) values ranging from 35.32 to 64.15 \(\upmu \)M on lysate. A structural analysis of these hits revealed that 14 of these compounds probably have non-covalent mode of binding to the target and have not reported for anti-tubercular or anti-proteasome activity. The binding interactions of all the 14 protein-inhibitor complexes were analyzed using molecular docking studies. Further, molecular dynamics simulations of the protein in complex with the two most promising hits were carried out so as to identify the key interactions and validate the structural stability.

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References

  1. Global Tuberculosis Report (2014) http://www.who.int/tb/publications/global_report/en/. Last accessed 20 Mar 2015

  2. Cerda-Maira F, Darwin KH (2009) The Mycobacterium tuberculosis proteasome: more than just a barrel-shaped protease. Microbes Infect 11:1150–1155. doi:10.1016/j.micinf.2009.08.003

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  3. Gandotra S, Schnappinger D, Monteleone M, Hillen W, Ehrt S (2007) In vivo gene silencing identifies the Mycobacterium tuberculosis proteasome as essential for persistence in mice. Nat Med 13:1515–1520. doi:10.1038/nm1683

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Darwin KH, Ehrt S, Gutierrez-Ramos JC, Weich N, Nathan CF (2003) The proteasome of Mycobacterium tuberculosis is required for resistance to nitric oxide. Science 302:1963–1966. doi:10.1126/science.1091176

    Article  CAS  PubMed  Google Scholar 

  5. Sassetti CM, Boyd DH, Rubin EJ (2003) Genes required for mycobacterial growth defined by high density mutagenesis. Mol Microbiol 48:77–84. doi:10.1046/j.1365-2958.2003.03425.x

    Article  CAS  PubMed  Google Scholar 

  6. Lowe J, Stock D, Jap B, Zwickl P, Baumeister W, Huber R (1995) Crystal structure of the 20S proteasome from the archaeon T. acidophilum at 3.4 Å resolution. Science 268:533–539. doi:10.1126/science.7725097

    Article  CAS  PubMed  Google Scholar 

  7. Hu G, Lin G, Wang M, Dick L, Xu RM, Nathan C, Li H (2006) Structure of the Mycobacterium tuberculosis proteasome and mechanism of inhibition by a peptidyl boronate. Mol Microbiol 59:1417–1428. doi:10.1111/j.1365-2958.2005.05036.x

    Article  CAS  PubMed  Google Scholar 

  8. Lin G, Li D, Chidawanyika T, Nathan C, Li H (2010) Fellutamide B is a potent inhibitor of the Mycobacterium tuberculosis proteasome. Arch Biochem Biophys 501:214–220. doi:10.1016/j.abb.2010.06.009

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Lin G, Li DY, Carvalho LPS, Deng HT, Tao H, Vogt G, Wu KY, Schneider J, Chidawanyika T, Warren JD, Li HL, Nathan C (2009) Inhibitors selective for mycobacterial versus human proteasomes. Nature 461:621–626. doi:10.1038/nature08357

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Lin G, Tsu C, Dick L, Zhou XK, Nathan C (2008) Distinct specificities of Mycobacterium tuberculosis and mammalian proteasomes for \(N\)-acetyl tripeptide substrates. J Biol Chem 283:34423–34431. doi:10.1074/jbc.M805324200

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Lin G, Chidawanyika T, Tsu C, Warrier T, Vaubourgeix J, Blackburn C, Gigstad K, Sintchak M, Dick L, Nathan C (2013) N, C-capped dipeptides with selectivity for mycobacterial proteasome over human proteasomes: role of S3 and S1 binding pockets. J Am Chem Soc 135:9968–9971. doi:10.1021/ja400021x

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Yang J, Pi W, Xiong L, Ang W, Yang T, He J, Liu Y, Chang Y, Ye W, Wang Z, Luo Y, Wei Y (2013) 3H–1,2,4-dithiazol-3-one compounds as novel potential affordable antitubercular agents. Bioorg Med Chem Lett 23:1424–1427. doi:10.1016/j.bmcl.2012.12.065

    Article  CAS  PubMed  Google Scholar 

  13. Johnson AM, Maggiora GM (1990) Concepts and applications of molecular similarity. Wiley, New York

    Google Scholar 

  14. Mehra R, Sharma R, Khan IA, Nargotra A (2015) Identification and optimization of Escherichia coli GlmU inhibitors: an in silico approach with validation thereof. Eur J Med Chem 92:78–90. doi:10.1016/j.ejmech.2014.12.030

    Article  CAS  PubMed  Google Scholar 

  15. Martin YC, Kofron JL, Traphagen LM (2002) Do structurally similar molecules have similar biological activity? J Med Chem 45:4350–4358. doi:10.1021/jm020155c

    Article  CAS  PubMed  Google Scholar 

  16. Franke L, Schwarz O, Kuhrt LM, Hoernig C, Fischer L, George S, Tanrikulu Y, Schneider P, Werz O, Steinhilber D, Schneider G (2007) Identification of natural-product-derived inhibitors of 5-lipoxygenase activity by ligand-based virtual screening. J Med Chem 50:2640–2646. doi:10.1021/jm060655w

    Article  CAS  PubMed  Google Scholar 

  17. Betzi S, Restouin A, Opi S, Arold ST, Parrot I, Guerlesquin F, Morelli X, Collette Y (2007) Protein–protein interaction inhibition (2P2I) combining high throughput and virtual screening: application to the HIV-1 Nef protein. Proc Natl Acad Sci USA 104:19256–19261. doi:10.1073/pnas.0707130104

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Willett P (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11:1046–1053. doi:10.1016/j.drudis.2006.10.005

    Article  CAS  PubMed  Google Scholar 

  19. Amaro RE, Schnaufer A, Interthal H, Hol W, Stuart KD, McCammon JA (2008) Discovery of drug-like inhibitors of an essential RNA-editing ligase in Trypanosoma brucei. Proc Natl Acad Sci USA 105:17278–17283. doi:10.1073/pnas.0805820105

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Langdon SR, Westwood IM, van Montfort RLM, Brown N, Blagg J (2013) Scaffold-focused virtual screening: prospective application to the discovery of TTK inhibitors. J Chem Inf Model 53:1100–1112. doi:10.1021/ci400100c

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Instant JChem (2012) Version 5.9.4. ChemAxon. http://www.chemaxon.com. Last accessed 31 Mar 2015

  22. Yang SC, Chang SS, Chen HY, Chen CYC (2011) Identification of potent EGFR inhibitors from TCM database@Taiwan. PLoS Comput Biol 7:e1002189. doi:10.1371/journal.pcbi.1002189

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  23. Valasani KR, Vangavaragu JR, Day VW, Yan SS (2014) Structure-based design, synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for identification of novel cyclophilin D inhibitors. J Chem Inf Model 54:902–912. doi:10.1021/ci5000196

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  24. Baurin N, Mozziconacci JC, Arnoult E, Chavatte P, Marot C, Allory LM (2004) 2D QSAR consensus prediction for high-throughput virtual screening: an application to COX-2 inhibition modeling and screening of the NCI database. J Chem Inf Comput Sci 44:276–285. doi:10.1021/ci0341565

    Article  CAS  PubMed  Google Scholar 

  25. Hoffman BT, Kopajtic T, Katz JL, Newman AH (2000) 2D QSAR modeling and preliminary database searching for dopamine transporter inhibitors using genetic algorithm variable selection of Molconn Z descriptors. J Med Chem 43:4151–4159. doi:10.1021/jm990472s

    Article  CAS  PubMed  Google Scholar 

  26. Evers A, Hessler G, Matter H, Klabunde T (2005) Virtual screening of biogenic amine-binding G-protein coupled receptors: Comparative evaluation of protein and ligand-based virtual screening protocols. J Med Chem 48:5448–5465. doi:10.1021/jm050090o

    Article  CAS  PubMed  Google Scholar 

  27. BIOVIA. Discovery Studio modeling environment, Release 2.1. http://www.accelrys.com

  28. Golbraikh A, Tropsha A (2002) Beware of q\(^{2}\). J Mol Graph Model 20:269–276. doi:10.1016/S1093-3263(01)00123-1

    Article  CAS  PubMed  Google Scholar 

  29. Roy P, Roy K (2008) On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci 27:302–313. doi:10.1002/qsar.200710043

    Article  CAS  Google Scholar 

  30. Wermuth C, Ganellin C, Lindberg P, Mitscher L (1998) Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl Chem 70:1129–1143. doi:10.1351/pac199870051129 ISSN (print): 0033-4545

    Article  CAS  Google Scholar 

  31. Schrödinger, LLC (2015) New York, NY. www.schrodinger.com. Last accessed 3 Apr 2015

  32. Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006) PHASE: A new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening. 1. Methodology and preliminary results. J Comput Aided Mol Des 20:647–671. doi:10.1007/s10822-006-9087-6

    Article  CAS  PubMed  Google Scholar 

  33. Dixon SL, Smondyrev AM, Rao SN (2006) PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des 67:370–372. doi:10.1111/j.1747-0285.2006.00384.x

    Article  CAS  PubMed  Google Scholar 

  34. SciFinder (2015) https://scifinder.cas.org/scifinder. Last accessed 20 Mar 2015

  35. Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759. doi:10.1021/jm030644s

    Article  CAS  PubMed  Google Scholar 

  36. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shaw DE, Shelley M, Perry JK, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749. doi:10.1021/jm0306430

    Article  CAS  PubMed  Google Scholar 

  37. Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the ACM/IEEE conference on supercomputing (SC 2006), Tampa, FL, p 43, 11–17 November 2006. E-ISBN:0-7695-2700-0. doi:10.1109/SC.2006.54

  38. Desmond Molecular Dynamics System (2014) Version 3.8. D. E. Shaw Research, New York. https://www.deshawresearch.com/resources_desmond.html, 2015

  39. Deswal S, Roy N (2007) A novel range based QSAR study of human neuropeptide Y (NPY) Y5 receptor inhibitors. Eur J Med Chem 42:463–470. doi:10.1016/j.ejmech.2006.09.011

    Article  CAS  PubMed  Google Scholar 

  40. Nargotra A, Sharma S, Koul JL, Sangwan PL, Khan IA, Kumar A, Taneja SC, Koul S (2009) Quantitative structure activity relationship (QSAR) of piperine analogs for bacterial NorA efflux pump inhibitors. Eur J Med Chem 44:4128–4135. doi:10.1016/j.ejmech.2009.05.004

    Article  CAS  PubMed  Google Scholar 

  41. Mehra R, Nargotra A, Shah BA, Taneja SC, Vishwakarma RA, Koul S (2013) Pro-apoptotic properties of parthenin analogs: a quantitative structure-activity relationship study. Med Chem Res 22:2303–2311. doi:10.1007/s00044-012-0225-5

    Article  CAS  Google Scholar 

  42. Poongavanam V, Kongsted J (2013) Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition. PLoS ONE 8:e73478. doi:10.1371/journal.pone.0073478

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  43. Krishna S, Singh K, Meena S, Datta D, Siddiqi MI, Banerjee D (2014) Pharmacophore-based screening and identification of novel human ligase I inhibitors with potential anticancer activity. J Chem Inf Model 54:781–792. doi:10.1021/ci5000032

    Article  CAS  PubMed  Google Scholar 

  44. Vuorinen A, Engeli R, Meyer A, Bachmann F, Griesser UJ, Schuster D, Odermatt A (2014) Ligand-based pharmacophore modeling and virtual screening for the discovery of novel 17\(\beta \)- hydroxysteroid dehydrogenase 2 inhibitors. J Med Chem 57:5995–6007. doi:10.1021/jm5004914

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  45. Chiang YK, Kuo CC, Wu YS, Chen CT, Coumar MS, Wu JS, Hsieh HP, Chang CY, Jseng HY, Wu MH, Leou JS, Song JS, Chang JY, Lyu PC, Chao YS, Wu SY (2009) Generation of ligand-based pharmacophore model and virtual screening for identification of novel tubulin inhibitors with potent anticancer activity. J Med Chem 52:4221–4233. doi:10.1021/jm801649y

    Article  CAS  PubMed  Google Scholar 

  46. Shukla S, Kouanda A, Silverton L, Talele TT, Ambudkar SV (2014) Pharmacophore modeling of nilotinib as an inhibitor of ATP-binding cassette drug transporters and BCR-ABL kinase using a three-dimensional quantitative structure-activity relationship approach. Mol Pharm 11:2313–2322. doi:10.1021/mp400762h

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  47. Ritschel T, Hermans SMA, Schreurs M, Heuvel JJMW, Koenderink JB, Greupink R, Russel FGM (2014) In silico identification and in vitro validation of potential cholestatic compounds through 3D ligand-based pharmacophore modeling of BSEP inhibitors. Chem Res Toxicol 27:873–881. doi:10.1021/tx5000393

    Article  CAS  PubMed  Google Scholar 

  48. Yang SY (2010) Pharmacophore modeling and application in drug discovery: challenges and recent advances. Drug Discov Today 15:444–450. doi:10.1016/j.drudis.2010.03.013

    Article  CAS  PubMed  Google Scholar 

  49. Leach AR, Gillet VJ, Lewis RA, Taylor R (2010) Three-dimensional pharmacophore methods in drug discovery. J Med Chem 53:539–558. doi:10.1021/jm900817u

    Article  CAS  PubMed  Google Scholar 

  50. de Bettignies G, Coux O (2010) Proteasome inhibitors: dozens of molecules and still counting. Biochimie 92:1530–1545. doi:10.1016/j.biochi.2010.06.023

  51. Kisselev AF, van der Linden WA, Overkleeft H (2012) Proteasome inhibitors: an expanding army attacking a unique target. Chem Biol 19:99–115. doi:10.1016/j.chembiol.2012.01.003

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  52. Rentsch A, Landsberg D, Brodmann T, Bulow L, Girbig AK, Kalesse M (2013) Synthesis and pharmacology of proteasome inhibitors. Angew Chem Int Ed 52:5450–5488. doi:10.1002/anie.201207900

    Article  CAS  Google Scholar 

  53. Genin E, Reboud-Ravaux M, Vidal J (2010) Proteasome inhibitors: recent advances and new perspectives in medicinal chemistry. Curr Top Med Chem 10:232–256. doi:10.2174/156802610790725515

    Article  CAS  PubMed  Google Scholar 

  54. Beck P, Dubiella C, Groll M (2012) Covalent and non-covalent reversible proteasome inhibition. Biol Chem 393:1101–1120. doi:10.1515/hsz-2012-0212

    Article  CAS  PubMed  Google Scholar 

  55. Kaffy J, Bernadat G, Ongeri S (2013) Non-covalent proteasome inhibitors. Curr Pharm Des 19:4115–4130. doi:10.2174/1381612811319220016

    Article  CAS  PubMed  Google Scholar 

  56. Blackburn C, Gigstad KM, Hales P, Garcia K, Jones M, Bruzzese FJ, Barrett C, Liu JX, Soucy TA, Sappal DS, Bump N, Olhava EJ, Fleming P, Dick LR, Tsu C, Sintchak MD, Blank JL (2010) Characterization of a new series of non-covalent proteasome inhibitors with exquisite potency and selectivity for the 20S \(\beta \)5-subunit. Biochem J 430:461–476. doi:10.1042/BJ20100383

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  57. Flanagan ME, Abramite JA, Anderson DP, Aulabaugh A, Dahal UP, Gilbert AM, Li C, Montgomery J, Oppenheimer SR, Ryder T, Schuff BP, Uccello DP, Walker GS, Wu Y, Brown MF, Chen JM, Hayward MM, Noe MC, Obach RS, Philippe L, Shanmugasundaram V, Shapiro MJ, Starr J, Stroh J, Che Y (2014) Chemical and computational methods for the characterization of covalent reactive groups for the prospective design of irreversible inhibitors. J Med Chem 57:10072–10079. doi:10.1021/jm501412a

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

The authors acknowledge the Department of Biotechnology (DBT), India and Department of Science and Technology (DST), India for the financial support from the projects GAP-0141 (DBT) and GAP-1143 (DST), respectively.

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Authors and Affiliations

  1. Discovery Informatics Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, 180001, India

    Rukmankesh Mehra & Amit Nargotra

  2. Clinical Microbiology Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, 180001, India

    Reena Chib, Inshad Ali Khan & Farrah Gul Khan

  3. Medicinal Chemistry Division, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, 180001, India

    Gurunadham Munagala, Kushalava Reddy Yempalla & Parvinder Pal Singh

  4. Academy of Scientific and Innovative Research, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu, 180001, India

    Reena Chib, Gurunadham Munagala, Kushalava Reddy Yempalla, Inshad Ali Khan, Parvinder Pal Singh & Amit Nargotra

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  1. Rukmankesh Mehra
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Mehra, R., Chib, R., Munagala, G. et al. Discovery of new \({\varvec{Mycobacterium~tuberculosis}}\) proteasome inhibitors using a knowledge-based computational screening approach. Mol Divers 19, 1003–1019 (2015). https://doi.org/10.1007/s11030-015-9624-0

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