• Baki Vijaya Bhaskar Division of Molecular Biology, Department of Zoology, Sri Venkateswara University, Tirupati 517502
  • Tirumalasetty Muni Chandra Babu Division of Molecular Biology, Department of Zoology, Sri Venkateswara University, Tirupati 517502
  • Wudayagiri Rajendra Division of Molecular Biology, Department of Zoology, Sri Venkateswara University, Tirupati 517502


Objective: In order to development of novel, potent and selective inhibitors of Dihydropiconilate reductase (KpDHDPR) of multidrug resistant Klebsiella pneumonia.

Methods: Protein sequence of KpDHDPR was retrieved from the UNIPROT and the primary and secondary structure was analyzed using Prot Param, SOPMA, GOR4 and Chou and Fasman. Afterword's, 3D structure of KpDHDPR was built by using MODELLEER9.14. The Molecular dynamics simulation was carried out using NAMD2.9 with CHARMM27 force field for 10 picoseconds and production run with for 400 picoseconds time period covered with water box. Molecular docking and virtual screening was carried out using Auto Dock Vina4.0 with PyRx interface. Bond angles, bond lengths, bond distances and binding interactions were analyzed using PyMol. Toxicity assessment and Lipinski rule of five of ligand were assessed using MOLINSPIRATION and OSIRIS Server.

Results: 3D structure of KpDHDPR was resolved on the basis of EcDHDPR that revealed N-terminal nucleotide domain and C-terminal substrate binding domain which are connected by a short hinge region. Nucleotide binding domain is formed with seven α-helices and the Substrate binding domain is composed with three α-helices and Rossman fold is observed with four α-helices and seven β-strands. Molecular docking analysis revealed that NADPH has exhibited more binding affinity to KpDHDPR than NADH. As results of virtual screening and docking, six compounds viz. ZINC04280533, ZINC04280532, ZINC04280468, ZINC33378709, ZINC05280538 and ZINC25694354 were identified. Bioavailability of these inhibitors are comply with the Lipinski rule of five, good pharmacokinetic and drug likeness properties.

Conclusion: In conclusion, in silico studies revealed that these lead scaffolds could helpful in the development of KpDHDP R inhibitors. Hence, these drug candidates might be promoted as promising antibacterial agents for the treatment of drug resistant gram negative bacterial infections.

Keywords: Dihydropiconilate reductase, NADH and NADPH, Homology Modeling, Docking, Virtual screening


1. Weigel LM, Steward CD, Tenover FC. gyrA mutations associated with fluoroquinolone resistance in eight species of enterobacteriaceae. Antimicrob Agents Chemother 1998;10:2661-7.
2. Cox RJ, Sutherland A, Vederas JC. Bacterial diaminopimelate metabolism as a target for antibiotic design. Bioorg Med Chem 2000;8:843-71.
3. Shedlarski JG, Gilvarg C. The pyruvate-aspartic semialdehyde condensing enzyme of Escherichia coli. J Biol Chem 1970;245:1362–73.
4. Tamir H, Gilvarg C. Density gradient centrifugation for the separation of sporulating forms of bacteria. J Biol Chem 1974;249:3034–40.
5. Schrumpf B, Schwarzer A, Kalinowski J, Puhler A, Eggeling L, Sahm H. A functionally split pathway for lysine synthesis in corynebacterium glutamicum. J Bacteriol 1991;173:4510-6.
6. Scapin G, Reddy SG, Zheng R, Blanchard JS. Three-dimensional structure of Escherichia coli dihydrodipicolinate reductase in complex with NADH and the inhibitor 2, 6-pyridinedicarboxylate. Biochemistry 1997;36:15081–8.
7. Cirilli M, Zheng R, Scapin G, Blanchard JS. The three-dimensional structures of the Mycobacterium tuberculosis dihydrodipicolinate reductase-NADH-2, 6-PDC and-NADPH-2, 6-PDC complexes. Structural and mutagenic analysis of relaxed nucleotide specificity. Biochemistry 2003;42:10644–50.
8. Pearce FG, Sprissler C, Gerrard JA. Characterization of dihydrodipicolinate reductase from the rmotogamaritima reveals the evolution of substrate binding kinetics. J Biochem 2008;143:617–23.
9. Coulter CV, Juliet A, Gerrard, James A E, Kraunsoe, Pratt AJ. Escherichia coli dihydrodipicolinate synthase and dihydrodipicolinate reductase: kinetic and inhibition studies of two putative herbicide targets. Pestic Sci 1999;55:887–95.
10. Couper L, McKendrick JE, Robins DJ. Pyridine and piperidine derivatives as inhibitors of dihydrodipicolinic acid synthase, a key enzyme in the diaminopimelate pathway to L-lysine. Bioorg Med Chem Lett 1994;4:2267-72.
11. Giovanna S, Sreelatha GR, Renjian Z, John SB. Three-dimensional structure of escherichia coli dihydrodipicolinate reductase in complex with NADH and the inhibitor 2, 6-Pyridinedicarboxylate. Biochemistry 1997;36:15081-8.
12. Geourjon C, Deleage G. SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction. CABIOS Comput Appl Biosci 1995;11:681-4.
13. Garnier J, Gibrat JF, Robson B. GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol 1996;266:540-53.
14. Chou PY, Fasman GD. Prediction of protein conformation. Biochemistry 1974;13:222-45.
15. Thompson JD, Higgins DG, Gibson TJ, Clustal W. Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 1994;22:4673-80.
16. Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 1993;5234:779-815.
17. Kale L, Skeel R, Bhandarkar M, Brunner R, Gursoy A, Krawetz N, et al. Schulten KNAMD: Greater scalability for parallel molecular dynamics. J Comput Phys 1999;151:283–12.
18. Schlick T, Skeel R, Brunger A, Kale L, Board JA, Hermans J. Algorithmic challenges in computational molecular biophysics. J Comput Phys 1999;151;9–48.
19. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML. Comparison of simple potential functions for simulating liquid water. J Chem Phys 1983;79;926–34.
20. Grubmuller H, Heller H, Windemuth A, Schulten K. Generalized verlet algorithm for efficient molecular dynamics simulations with long-range interactions. Mol Simul 1991;6:121–42.
21. Darden TA, Pedersen LG. Molecular modeling: an experimental tool. Environ Health Perspect 1993;101:410-2.
22. Essmann U, Berkowitz ML. Dynamical properties of phospholipid bilayers from computer simulation. Biophys J 1999;76:2081-9.
23. Ryckaert JP, Ciccotti G, Berendsen HJC. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comp Physiol 1977;23:327-41.
24. Andersen HC. Rattle: a velocity” version of the shake algorithm for molecular dynamics calculations. J Comput Phys 1983;52:24-34.
25. Laskoswki RA, MacArthur MW, Moss DS. PROCHECK: A program to check the stereo chemical quality of protein structures. J Appl Crystallogr 1993;26:283-91.
26. Eisenberg D, Luthyand R, Bowie JU. VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol 1997;277:396-404.
27. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 1993;2:1511-9.
28. Hooft RW, Vriend G, Sander C. Errors in protein structures. Nature 1996;381:272.
29. Guex N, Peitsh MC. SWISS-MODEL and the swiss-Pdb viewer: an environment for comparative protein modeling. Electrophoresis 1997;15:2714-23.
30. Wolf LK. Chemical engineering news. PyRx Website 2009;87:31.
31. Trott O, Olson AJ. Auto dock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455–61.
32. DeLano WL. The Py MOL molecular graphics system DeLano scientific, San Carlos, CA, USA; 2012.
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How to Cite
Bhaskar, B. V., T. M. C. Babu, and W. Rajendra. “HOMOLOGY MODELING AND DEVELOPMENT OF DIHYDRODIPICONILATE REDUCTASE INHIBITORS OF KLEBSIELLA PNEUMONIA: A COMPUTATIONAL APPROACH”. International Journal of Current Pharmaceutical Research, Vol. 8, no. 3, July 2016, pp. 71-76, https://innovareacademics.in/journals/index.php/ijcpr/article/view/13899.
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