MOLECULAR DOCKING STUDIES FOR THE COMPARATIVE ANALYSIS OF DIFFERENT BIOMOLECULES TO TARGET HYPOXIA INDUCIBLE FACTOR-1Î±
Objective: Hypoxia plays a significant role in governing many vital signalling molecules in the central nervous system (CNS). Hypoxic exposure has also been depicted as a stimulus for oxidative stress, increase in lipid peroxidation, DNA damage, blood-brain dysfunction, impaired calcium (Ca2+) homoeostasis and agglomeration of oxidized biomolecules in neurons, which act as a novel signature in diverse neurodegenerative and oncogenic processes. On the contrary, the presence of abnormally impaired expression of HIF-1Î± under hypoxic insult could serve as an indication of the existence of tumors and neuronal dysfunction as well. For instance, under hypoxic stress, amyloid-Î² protein precursor (AÎ²PP) cleavage is triggered due to the higher expression of HIF-1Î± and thus leads to synaptic loss. The objective of this research is to perform comparative studies of biomolecules in regulating HIF-1Î± activity based on in silico approaches that could establish a potential therapeutic window for the treatment of different abnormalities associated with impaired HIF-1Î±.
Methods: We employed various in silico methods such as drug-likeness parameters namely Lipinski filter analysis, Muscle tool, SWISS-MODEL, active site prediction, Auto Dock 4.2.1 and LigPlot1.4.5for molecular docking studies.
Results: 3D structure of HIF-1Î± was generated and Ramachandran plot obtained for quality assessment. RAMPAGE displayed 99.5% of residues in the most favoured regions. 0% residues in additionally allowed and 0.5% disallowed regions of the HIF-1Î± protein. Further, initial screenings of the molecules were done based on Lipinskiâ€™s rule of five. Cast P server used to predict the ligand binding site suggests that this protein can be utilised as a potential drug target. Finally, we have found Naringenin to be most effective amongst three biomolecules in modulating HIF-1Î± based on minimum inhibition constant, Ki and highest negative free energy of binding with the maximum interacting surface area during docking studies.
Conclusion: The present study outlines the novel potential of Biomolecules in regulating HIF-1Î± activity for the treatment of different abnormalities associated with impaired HIF-1Î±.
2. Keith B, Johnson RS, Simon MC. HIF1Î± and HIF2Î±: sibling rivalry in hypoxic tumour growth and progression. Nat Rev Cancer 2011;12:9-22.
3. Correia SC, Carvalho C, Cardoso S. Defective HIF signaling pathway and brain response to hypoxia in neurodegenerative diseases: not an "iffy" question. Curr Pharm Des 2013;19:6809-22.
4. Ziello JE, Jovin IS, Huang Y. Hypoxia-inducible factor (HIF)-1 regulatory pathway and its potential for therapeutic intervention in malignancy and ischemia. Yale J Biol Med 2007;80:51-60.
5. Mukandala G, Tynan R, Lanigan S. The effects of hypoxia and inflammation on synaptic signaling in the CNS. Brain Sci 2016;6:E6.
6. Oh YS. Bioactive compounds and their neuroprotective effects in diabetic complications. Nutrients 2016;8:E472.
7. Mahendra Kumar C, Singh SA. Bioactive lignans from sesame (Sesamumindicum L.): evaluation of their antioxidant and antibacterial effects for food applications. J Food SciTechnol 2015;52:2934-41.
8. Zhang L, Lokeshwar BL. Medicinal properties of the Jamaican pepper plant Pimentadioica and Allspice. Curr Drug Targets 2012;13:1900-6.
9. Sonia Angeline M, Sarkar A, Anand K. Sesamol and naringenin reverse the effect of rotenone-induced PD rat model. Neuroscience 2013;254:379-94.
10. Sarkar A, Angeline MS, Anand K. Naringenin and quercetin reverse the effect of hypobaric hypoxia and elicit a neuroprotective response in the murine model. Brain Res 2012;1481:59-70.
11. Altschul SF, Gish W, Miller W. Basic local alignment search tool. J Mol Biol 1990;215:403-10.
12. Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 2004;5:113.
13. Arnold K, Bordoli L, Kopp J. The SWISS-MODEL workspace: a web-based environment for protein structure homology modeling. Bioinformatics 2006;22:195-201.
14. Yang Z, Lasker K, Schneidman-Duhovny D. UCSF Chimera, MODELLER, and IMP: an integrated modeling system. J Struct Biol 2012;179:269-78.
15. Dundas J, Ouyang Z, Tseng J. CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 2006;34:W116-118.
16. Lipinski CA. Lead-and drug-like compounds: the rule-of-five revolution. Drug Discovery Today Technol 2004;1:337-41.
17. Pradeepkiran JA, Kumar KK, Kumar YN. Modeling, molecular dynamics and docking assessment of transcription factor rho: a potential drug target in Brucellamelitensis 16M. Drug Des Dev Ther 2015;9:1897-912.
18. Park H, Lee J, Lee S. Critical assessment of the automated AutoDock as a new docking tool for virtual screening. Proteins 2006;65:549-54.
19. Pranjaligupta, Nishant Rai, Pankaj Gautam. Anticancer drugs as potential inhibitors of acrab-tolc of multidrug-resistant Escherichia coli: an in silico molecular modeling and docking study. Asian J Pharm Clin Res. 2015;8:351-8.
20. Sri dharani R, Ranjitha R, Sripathi R, Ali muhammad KS, Ravi S. Docking studies in target proteins involved in antibacterial action mechanisms: alkaloids isolated from Scutellariagenus. Asian J Pharm Clin Res 2016;9:121-5.
21. Manjula J, Maheswari R. Biological and docking studies of novel aroylhydrazones. Int J Pharm Pharm Sci 2017;9:81-5.
22. Sarath S, Anjali T. In silico design and molecular docking studies of some 1,2-benzisoxazole derivatives for their analgesic and anti-inflammatory activity. Int J Curr Pharm Res 2017;9:38-41.