Virtual Screening for Potential Inhibitors of CTX-M-15 Protein of Klebsiella pneumoniae

  • Tayebeh Farhadi
  • Atefeh FakharianEmail author
  • Roman S. Ovchinnikov
Original Research Article


The Gram-negative bacterium Klebsiella pneumoniae, responsible for a wide variety of nosocomial infections in immuno-deficient patients, involves the respiratory, urinary and gastrointestinal tract infections and septicemia. Extended spectrum β-lactamases (ESBL) belong to β-lactamases capable of conferring antibiotic resistance in Gram-negative bacteria. CTX-M-15, a prevalent ESBL reported from Enterobacteriaceae including K. pneumoniae, was selected as a potent anti-bacterial target. To identify the novel drug-like compounds, structure-based screening procedure was employed against downloaded drug-like compounds from ZINC database. An acronym for “ZINC” is not commercial. The docking free energy values were investigated and compared to the known inhibitor Avibactam. Six best novel drug-like compounds were selected and their hydrogen bindings with the receptor were determined. Based on the binding efficiency mode, three among these six identified most potential inhibitors, ZINC21811621, ZINC93091917 and ZINC19488569, were predicted as potential competitive inhibitors against CTX-M-15 compared to Avibactam. These three inhibitors may provide a framework for the experimental studies to develop anti-Klebsiella novel drug candidates targeting CTX-M-15.


Klebsiella pneumoniae β-Lactamase CTX-M-15 Molecular docking Virtual screening 


Compliance with Ethical Standards

Conflict of interest

The authors declare no competing financial interests in the findings of this study.

Supplementary material

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  1. 1.
    Brisse S, Grimont F, Grimont PAD (2006) The genus Klebsiella. Prokaryotes 6:159–196Google Scholar
  2. 2.
    Podschun R, Ullmann U (1998) Klebsiella spp. as nosocomial pathogens: epidemiology, taxonomy, typing methods, and pathogenicity factors. Clin Microbiol Rev 11(4):589–603PubMedPubMedCentralGoogle Scholar
  3. 3.
    Dhingra KR (2008) A case of complicated urinary tract infection: Klebsiella pneumoniae emphysematous cystitis presenting as abdominal pain in the emergency department. West J Emerg Med 9(3):171–173PubMedPubMedCentralGoogle Scholar
  4. 4.
    Vieira AT, Rocha VM, Tavares L, Garcia CC, Teixeira MM, Oliveira SC et al (2016) Control of Klebsiella pneumoniae pulmonary infection and immunomodulation by oral treatment with the commensal probiotic Bifidobacterium longum 51A. Microbes Infect 18(3):180–189CrossRefGoogle Scholar
  5. 5.
    Wei YQ, Bi DX, Wei DQ et al (2016) Prediction of type II toxin–antitoxin loci in Klebsiella pneumoniae genome sequences. Interdiscip Sci Comput Life Sci 8:143. doi: 10.1007/s12539-015-0135-6 CrossRefGoogle Scholar
  6. 6.
    Bonnet R (2004) Growing group of extended-spectrum b-lactamases: the CTX-M enzymes. Antimicrob Agents Chemother 48:1–14CrossRefGoogle Scholar
  7. 7.
    Coque TM, Baquero F, Canton R (2008) Increasing prevalence of ESBL producing Enterobacteriaceae in Europe. Euro Surveill 13(47):pii_19044.
  8. 8.
    Hawkey PM, Jones AM (2009) The changing epidemiology of resistance. J Antimicrob Chemother 6:i3–i10CrossRefGoogle Scholar
  9. 9.
    Bethal CR, Taracila M, Shyr T, Thomson JM, Distler AM et al (2011) Exploring the inhibition of CTX-M-9 by b-lactamase inhibitors and carbapenems. Antimicrob Agents Chemother 55:3465–3475CrossRefGoogle Scholar
  10. 10.
    Karim A, Poirel L, Nagarajan S, Nordmann P (2001) Plasmid mediated extended-spectrum b-lactamase (CTX-M-3 like) from India and gene association with insertion sequence ISEcp1. FEMS Microbiol Lett 201:237–241PubMedGoogle Scholar
  11. 11.
    Bush K (2010) Bench-to-bedside review: the role of b-lactamases in antibioticresistant Gram-negative infections. Crit Care 14:224CrossRefGoogle Scholar
  12. 12.
    Faheem M, Rehman MT, Danishuddin M, Khan AU (2013) Biochemical characterization of CTX-M-15 from enterobacter cloacae and designing a novel non-b-lactam-b-lactamase inhibitor. PLoS One 8(2):e56926. doi: 10.1371/journal.pone.0056926 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Akta Z, Kayacana Z, Onculb O (2012) In vitro activity of avibactam (NXL104) in combination with β-lactams against Gram-negative bacteria, including OXA-48 β-lactamase-producing Klebsiella pneumoniae. Int J Antimicrob Agents 39:86–89CrossRefGoogle Scholar
  14. 14.
    van Duin D, Bonomo RA (2016) Ceftazidime/Avibactam and Ceftolozane/Tazobactam: second-generation β-lactam/β-lactamase Inhibitor combinations. Clin Infect Dis 63(2):234–241. doi: 10.1093/cid/ciw243 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Farhadi T, Ranjbar MM (2017) Designing and modeling of complex DNA vaccine based on MOMP of Chlamydia trachomatis: an in silico approach. Netw Model Anal Health Inf Bioinf 6(1). doi: 10.1007/s13721-016-0142-5
  16. 16.
    Farhadi T, Ovchinnikov RS, Ranjbar MM (2016) In silico designing of some agonists of toll-like receptor 5 as a novel vaccine adjuvant candidates. Netw Model Anal Health Inform Bioinforma 5(31). doi: 10.1007/s13721-016-0138-1
  17. 17.
    Farhadi T, Nezafat N, Ghasemi Y, Karimi Z, Hemmati S, Erfani N (2015) Designing of complex multi-epitope peptide vaccine based on Omps of Klebsiella pneumoniae: an in silico approach. Int J Pept Res Ther 21(3):325–341CrossRefGoogle Scholar
  18. 18.
    Farhadi T, Nezafat N, Ghasemi Y (2015) In silico phylogenetic analysis of Vibrio cholera isolates based on three housekeeping genes. Int J Comput Biol Drug Des 8(1):62–74CrossRefGoogle Scholar
  19. 19.
    Singh N, Sudandiradoss C, Abraham J (2016) Screening of furanone in Cucurbita melo and evaluation of its bioactive potential using in silico studies. Interdiscip Sci Comput Life Sci 8:395. doi: 10.1007/s12539-016-0161-z CrossRefGoogle Scholar
  20. 20.
    Padhi S, Das D, Panja S et al (2016) Molecular characterization and antimicrobial activity of an Endolichenic fungus, Aspergillus sp. isolated from Parmeliacaperata of similipal biosphere reserve, India. Interdiscip Sci Comput Life Sci. doi: 10.1007/s12539-016-0146-y CrossRefGoogle Scholar
  21. 21.
    Vijayalakshmi P, Nisha J, Rajalakshmi M (2014) Virtual screening of potential inhibitor against FtsZ protein from Staphylococcus aureus. Interdiscip Sci Comput Life Sci 6:331–339. doi: 10.1007/s12539-012-0229-3 CrossRefGoogle Scholar
  22. 22.
    Jorgensen WL (2004) The many roles of computation in drug discovery. Science 303:1813–1818CrossRefGoogle Scholar
  23. 23.
    Clark DE (2008) What has virtual screening ever done for drug discovery? Expert opin drug dis 3:841–851CrossRefGoogle Scholar
  24. 24.
    Vyas V, Jain A, Jain A, Gupta A (2008) Virtual screening: a fast tool for drug design. Sci Pharm 76:333–360. doi: 10.3797/scipharm.0803-03 CrossRefGoogle Scholar
  25. 25.
    Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26CrossRefGoogle Scholar
  26. 26.
    Ohtsuki S, Uchida Y, Kubo Y, Terasaki T (2011) Quantitative targeted absolute proteomics-based ADME research as a new path to drug discovery and development: methodology, advantages, strategy and prospects. J Pharm Sci 100:3547–3559CrossRefGoogle Scholar
  27. 27.
    Forli S (2015) Charting a path to success in virtual screening. Molecules 20(10):18732–18758CrossRefGoogle Scholar
  28. 28.
    Wolf LK (2009) New software and websites for the chemical enterprise. Chem Eng News 87:31Google Scholar
  29. 29.
    Irwin JJ, Brian K, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Model 45(1):177–182CrossRefGoogle Scholar
  30. 30.
    Rose PW, Prlic A, Altunkaya A, Bi C, Bradley AR, Christie CH et al (2017) The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res 45:D271–D281CrossRefGoogle Scholar
  31. 31.
    Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC et al (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612CrossRefGoogle Scholar
  32. 32.
    Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36:3219–3228CrossRefGoogle Scholar
  33. 33.
    Roy A, Yang J, Zhang Y (2012) COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res 40:W471–W477CrossRefGoogle Scholar
  34. 34.
    Yang J, Roy A, Zhang Y (2013) Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics 29:2588–2595CrossRefGoogle Scholar
  35. 35.
    Leach AR, Shoichet BK, Peishoff CE (2006) Prediction of protein-ligand interactions docking and scoring: successes and gaps. J Med Chem 49:5851–5855CrossRefGoogle Scholar
  36. 36.
    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK (2004) Glide: a new approach for rapid, accurate docking and scoring: method and assessment of docking accuracy. J Med Chem 47:1739–1749CrossRefGoogle Scholar
  37. 37.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461PubMedPubMedCentralGoogle Scholar
  38. 38.
    Kabsch W (1978) A discussion of the solution for the best rotation to relate two sets of vectors. Acta Cryst A 34:827–828CrossRefGoogle Scholar
  39. 39.
    Zhang Y, Skolnick J (2004) Template-based modeling and free modeling by I-TASSER in CASP7. Proc Natl Acad Sci USA 101:7594–7599CrossRefGoogle Scholar
  40. 40.
    Podlogar BL, Muegge I, Brice LJ (2001) Computational methods to estimate drug development parameters. Curr Opin Drug Discov Dev 4:102–109Google Scholar
  41. 41.
    Muegge I, Heald SL, Brittelli D (2001) Simple selection criteria for drug-like chemical matter. J Med Chem 44:1841–1846CrossRefGoogle Scholar
  42. 42.
    Kumar S, Jena L, Mohod K, Daf S, Varma AK (2014) Virtual screening for potential inhibitors of high-risk human papillomavirus 16 E6 protein. Interdiscip Sci Comput Life Sci. doi: 10.1007/s12539-015-0008-z CrossRefGoogle Scholar
  43. 43.
    Su AI, Lorber DM, Weston GS, Baase WA, Matthews BW, Shoichet BK (2001) Docking molecules by families to increase the diversity of hits in database screens: computational strategy and experimental evaluation. Proteins Struct Funct Bioinform 42:279–293CrossRefGoogle Scholar
  44. 44.
    Eyal E, Gerzon S, Potapov V, Edelman M, Sobolev V (2005) The limit of accuracy of protein modeling: influence of crystal packing on protein structure. J Mol Biol 351:431–442CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2017

Authors and Affiliations

  • Tayebeh Farhadi
    • 1
  • Atefeh Fakharian
    • 1
    Email author
  • Roman S. Ovchinnikov
    • 2
  1. 1.Chronic Respiratory Diseases Research Center (CRDRC), National Research Institute of Tuberculosis and Lung Diseases (NRITLD)Shahid Beheshti University of Medical SciencesTehranIran
  2. 2.Microbiology Group, Department of Bioactive NanostructuresFederal Research Centre for Microbiology and EpidemiologyMoscowRussia

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