• Anukriti Verma Amity Institute of Biotechnology J-3 Block, Amity University Campus, Sector–125, Noida 201303 (U. P.)
  • Bhawna Rathi Amity Institute of Biotechnology J-3 Block, Amity University Campus, Sector–125, Noida 201303 (U. P.)
  • Shivani Sharda Amity Institute of Biotechnology J-3 Block, Amity University Campus, Sector–125, Noida 201303 (U. P.)


Objective: The aim of the present study is the structure identification of UDP-N-acetyl muramate dehydrogenase and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase for Campylobacter jejuni and designing their inhibitors using docking and simulation studies.

Methods: Uniprot, BLAST P, Discovery Studio, Verify 3D and Maestro Schrödinger suit have been used for structure identification, validation and docking studies.

Results: The structures of UDP-N-acetylmuramic dehydrogenase and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase were predicted and validated generating 87.80% and 85.82% score respectively. For 4-hydroxy-3-methylbut-2-enyl diphosphate reductase, HTVS resulted in 5801 compounds while SP and XP resulted in 5781 ligands. For UDP-N-acetylmuramate dehydrogenase, HTVS resulted in 5474 compounds whereas SP and XP resulted in 5359 ligands.

Conclusion: The structures of UDP-N-acetylmuramate dehydrogenase and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase were detected and verified. The list of top 10 inhibitors was acquired that can be considered as putative and potential drug targets.

Keywords: Campylobacter jejuni, Structure prediction, Active site, Docking, Inhibitor.



Download data is not yet available.


1. Nyati K, Nyati R. Role of Campylobacter jejuni infection in the pathogenesis of guillain-barré syndrome: an update. Biomed Res Int 2013;2013:1-13.
2. CDC. Foodborne diseases active surveillance network (Food Net); 2014.
3. Kim JC, OhE, Kim J, Jeon B. Regulation of oxidative stress resistance in Campylobacter jejuni, a microaerophilic foodborne pathogen. Front Microbiol 2015;6:1-12.
4. Parkhill J, Wren BW, Mungall K, Ketley JM, Churcher C, Basham D, et al. The genome sequence of the foodborne pathogen Campylobacter jejuni reveals hypervariable sequences. Nature 2000;403:665-8.
5. Grazziotin AL, Vidal NM, Venancio TM. Uncovering major genomic features of essential genes in bacteria and methanogenic archaea. FEBS J 2015;282:3395-411.
6. Kusum Mehla K, Ramana J. Novel drug targets for foodborne pathogen Campylobacter jejuni: an integrated subtractive genomics and comparative metabolic pathway study. OMICS 2015;19:393-406.
7. Metris A, Reuter M, Gaskin D, Baranyi J, Vliet A. In vivo and in silico determination of essential genes of Campylobacter jejuni. BMC Genomics 2011;12:1-14.
8. Jagyasi A, Choubey J, Patel A, Verma MK. Molecular modeling and docking analysis of novel drug-like compounds for NDM-1. Int J Chem Anal Sci 2013;1:47-54.
9. Dassault systèmes BIOVIA, Discovery studio modeling environment, Release 4.5, San Diego: Dassault Systèmes; 2015.
10. Lüthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature 1992;356:83-5.
11. Unger M, Eichhoff A, Schumacher L, Strysio M, Menzel S, Carsten S, et al. Selection of nanobodies that block the enzymatic and cytotoxic activities of the binary Clostridium Difficile toxin CDT. Sci Rep 2015;5:1-10.
12. Akl M, Foudah A, Ebrahim H, Meyer S, Sayed K. The marine-derived sipholenol A-4-O-3′,4′-dichlorobenzoate inhibits breast cancer growth and motility in vitro and in vivo through the suppression of brk and FAK signaling. Mar Drugs 2014;12:2282-304.
13. Graewert T, Rohdich F, Span I, Bacher A, Eisenreich W, Eppinger J, et al. Structure of active IspH enzyme from Escherichia coli provides mechanistic insights into substrate reduction. Angew Chem Int Ed 2009;48:5756-9.
14. Graewert T, Span I, Eisenreich W, Rohdich F, Eppinger J, Bacher A, et al. Probing the reaction mechanism of IspH protein by x-ray structure analysis. Proc Natl Acad Sci 2010;107:1077-81.
15. Babajan B, Anuradha CM, Chaitanya M, Gowsia D, Kumar C. In silico structural characterization of Mycobacterium tuberculosis H37Rv UDP-N-acetylmuramate dehydrogenase. Int J Integr Biol 2009;6:12-6.
16. Shen J, Zhang W, Fang H, Perkins R, Tong W, Hong H. Homology modeling, molecular docking, and molecular dynamics simulations elucidated a-fetoprotein binding modes. BMC Bioinf 2013;14:1-11.
17. Khaled M, Elokely K, Robert J, Doerksen R. Docking challenge: protein sampling and molecular docking performance. J Chem Inf Model 2013;53:1934-45.
18. Tamilvanan T, Hopper W. High-throughput virtual screening and docking studies of matrix protein vp40 of ebola virus. Bioinformation 2013;9:286-92.
19. Gani O, Narayanan D, Engh R. Evaluating the predictivity of virtual screening for Abl kinase inhibitors to hinder drug resistance. Chem Biol Drug Des 2013;82:506–19.
20. Herráez A. Biomolecules in the computer: Jmol to the rescue. Biochem Mol Biol Educ 2006;34:255–61.
21. Mohamed N, Mohamed R, Chong T. Homology modeling of coagulase in Staphylococcus aureus. Bioinformation 2012;8:412-4.
22. Bowie JU, Lüthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991;253:164-70.
23. Sirin S, Kumar R, Martinez C, Karmilowicz M, Ghosh P, Abramov Y, et al. A computational approach to enzyme design: predicting ω-aminotransferase catalytic activity using docking and MM-GBSA scoring. J Chem Inf Model 2014;54:2334–46.
24. Gannavaram S, Sirin S, Sherman W, Gadda G. Mechanistic and computational studies of the reductive half-reaction of tyrosine to phenylalanine active site variants of D-arginine dehydrogenase. Biochemistry 2014;53:6574-83.
25. Jamal S, Goyal S, Shanker A, Grover A. Checking the STEP-associated trafficking and internalization of glutamate receptors for reduced cognitive deficits: a machine learning approach based cheminformatics study and its application for drug repurposing. PloS One 2015;10:1-20.
26. Singh K, Muthusamy K. Molecular modeling, quantum polarized ligand docking and structure-based 3D-QSAR analysis of the imidazole series as dual AT1 and ETA receptor antagonists. Acta Pharmacol Sin 2013;34:1592–606.
27. Vilar S, Ferino G, Phatak S, Berk B, Cavasotto C, Costanzi S. Docking-based virtual screening for ligands of G protein coupled receptors: not only crystal structures but also in silico models. J Mol Graph Model 2011;29:614–23.
28. Rajeswari M, Santhi N, Bhuvaneswari V. Pharmacophore and virtual screening of JAK3 inhibitors. Bioinformation 2014; 10:157-63.
29. Vijayakumar B, Umamaheswari A, Puratchikody A, Velmurugan D. Selection of an improved HDAC8 inhibitor through structure-based drug design. Bioinformation 2011;7:134-41.
293 Views | 862 Downloads
How to Cite
Verma, A., B. Rathi, and S. Sharda. “IDENTIFICATION OF NOVEL INHIBITORS AGAINST POTENTIAL TARGETS OF CAMPYLOBACTER JEJUNI”. International Journal of Pharmacy and Pharmaceutical Sciences, Vol. 8, no. 2, Feb. 2016, pp. 312-6,
Original Article(s)