Homology Modeling and Molecular Docking Studies of Glutamate Dehydrogenase (GDH) from Cyanobacterium Synechocystis sp. PCC 6803

  • Omid Haghighi
  • Soheila Davaeifar
  • Hossein Shahbani ZahiriEmail author
  • Hadi MalekiEmail author
  • Kambiz Akbari NoghabiEmail author


Glutamate dehydrogenase (GDH), which is present in most bacteria and eukaryotes’ mitochondria, plays an important role in amino acid metabolism. In general, GDH converts 2-oxoglutarate to l-glutamate using NAD(P)H as a cofactor, and vice versa. Acquiring more structural information about the GDH of Synechocystis sp. PCC 6803 could be helpful in many studies related to amino acid metabolism in cyanobacteria. In this study, homology modeling studies were conducted to achieve an acceptable structure of the GDH using recognized templates. To this end, a computational approach was used to demonstrate the coenzyme specificity of GDH for NADPH and NADH. The present study involved homology modeling of GDH and docking analyses of NADPH, NADH, 2-oxoglutarate, and l-glutamate into the predictive model of GDH. The results of this study suggest that GDH has similar coenzyme specificity for NADH and NADPH, while NADH has a better binding affinity than NADPH. Furthermore, the binding sites of 2-oxoglutarate and l-glutamate are similar to each other with differences in binding affinity.


Glutamate dehydrogenase Cyanobacteria Homology modeling Molecular docking 



This investigation was financially supported by National Institute of Genetic Engineering and Biotechnology (NIGEB), Ministry of Science, Research and Technology, Tehran, Iran.


  1. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402CrossRefGoogle Scholar
  2. Berman HM et al. (2006) The protein data bank, 1999. In: International tables for crystallography volume F: crystallography of biological macromolecules. Springer, New York, pp 675–684Google Scholar
  3. Chavez S, Candau P (1991) An NAD-specific glutamate dehydrogenase from cyanobacteria identification and properties. FEBS Lett 285:35–38CrossRefGoogle Scholar
  4. Chávez S, Reyes JC, Chauvat F, Florencio FJ, Candau P (1995) The NADP-glutamate dehydrogenase of the cyanobacterium Synechocystis 6803: cloning, transcriptional analysis and disruption of the gdhA gene. Plant Mol Biol 28:173–188CrossRefGoogle Scholar
  5. Chávez S, Lucena J, Reyes J, Florencio F, Candau P (1999) The presence of glutamate dehydrogenase is a selective advantage for the cyanobacterium Synechocystis sp. strain PCC 6803 under nonexponential growth conditions. J Bacteriol 181:808–813Google Scholar
  6. Florencio F, Marqués S, Candau P (1987) Identification and characterization of a glutamate dehydrogenase in the unicellular cyanobacterium Synechocystis PCC 6803. FEBS Lett 223:37–41CrossRefGoogle Scholar
  7. Frisch M et al. (2008) Gaussian 03, revision C. 02Google Scholar
  8. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38CrossRefGoogle Scholar
  9. Kolb P, Irwin JJ (2009) Docking screens: right for the right reasons? Curr Top Med Chem 9:755–770CrossRefGoogle Scholar
  10. Krishnamoorthy E, Hassan S, Hanna LE, Padmalayam I, Rajaram R, Viswanathan V (2017) Homology modeling of Homo sapiens lipoic acid synthase: substrate docking and insights on its binding mode. J Theor Biol 420:259–266CrossRefGoogle Scholar
  11. Larsson C, Snoep JL, Norbeck J, Albers E (2011) Flux balance analysis for ethylene formation in genetically engineered Saccharomyces cerevisiae. IET Syst Biol 5:245–251CrossRefGoogle Scholar
  12. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291CrossRefGoogle Scholar
  13. Laskowski R, MacArthur M, Thornton J (2001) PROCHECK: validation of protein structure coordinates international tables of crystallography. Crystallogr Biol Macromol F:722–725Google Scholar
  14. Lovell SC et al (2003) Structure validation by Cα geometry: ϕ, ψ and Cβ deviation. Proteins 50:437–450CrossRefGoogle Scholar
  15. McWilliam H et al (2013) Analysis tool web services from the EMBL-EBI. Nucleic Acids Res 41:W597–W600CrossRefGoogle Scholar
  16. Meng H, Liu P, Sun H, Cai Z, Zhou J, Lin J, Li Y (2016) Engineering a d-lactate dehydrogenase that can super-efficiently utilize NADPH and NADH as cofactors. Sci Rep 6:24887CrossRefGoogle Scholar
  17. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791CrossRefGoogle Scholar
  18. Park J, Choi Y (2017) Cofactor engineering in cyanobacteria to overcome imbalance between NADPH and NADH: a mini review. Front Chem Sci Eng 11:66–71CrossRefGoogle Scholar
  19. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612CrossRefGoogle Scholar
  20. Ribas J, Cubero E, Luque FJ, Orozco M (2002) Theoretical study of alkyl-π and aryl-π interactions. Reconciling theory and experiment. J Org Chem 67:7057–7065CrossRefGoogle Scholar
  21. 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
  22. Schaeffer L (2008) The role of functional groups in drug–receptor interactions. In: The practice of medicinal chemistry. Elsevier, Amsterdam, pp 464–480Google Scholar
  23. Sefid F, Rasooli I, Payandeh Z (2016) Homology modeling of a Camelid antibody fragment against a conserved region of Acinetobacter baumannii biofilm associated protein (Bap). J Theor Biol 397:43–51CrossRefGoogle Scholar
  24. Seyedi SS, Shukri M, Hassandarvish P, Oo A, Shankar EM, Abubakar S, Zandi K (2016) Computational approach towards exploring potential anti-Chikungunya activity of selected flavonoids. Sci Rep 6:24027CrossRefGoogle Scholar
  25. 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 31:455–461Google Scholar
  26. Webb B, Sali A (2014) Protein structure modeling with MODELLER protein structure prediction. Springer, New York, pp 1–15Google Scholar
  27. Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:W407–W410CrossRefGoogle Scholar
  28. Xu J, Zhang Y (2010) How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics 26:889–895CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Energy and Environmental BiotechnologyNational Institute of Genetic Engineering and Biotechnology (NIGEB)TehranIran
  2. 2.Department of Microbiology and Microbial BiotechnologyShahid Beheshti UniversityTehranIran

Personalised recommendations