Open AccessMOLECULAR DOCKING STUDIES FOR THE COMPARATIVE ANALYSIS OF DIFFERENT BIOMOLECULES TO TARGET HYPOXIA INDUCIBLE FACTOR-1α
Author(s) -
Niraj Kumar Jha,
Pravir Kumar
Publication year - 2017
Publication title -
international journal of applied pharmaceutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.238
H-Index - 15
ISSN - 0975-7058
DOI - 10.22159/ijap.2017v9i4.19505
Subject(s) - lipinski's rule of five , docking (animal) , in silico , chemistry , small molecule , hypoxia (environmental) , pharmacology , computational biology , microbiology and biotechnology , biochemistry , biology , neuroscience , gene , medicine , oxygen , nursing , organic chemistry
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-1α under hypoxic insult could serve as an indication of the existence of tumors and neuronal dysfunction as well. For instance, under hypoxic stress, amyloid-β protein precursor (AβPP) cleavage is triggered due to the higher expression of HIF-1α and thus leads to synaptic loss. The objective of this research is to perform comparative studies of biomolecules in regulating HIF-1α activity based on in silico approaches that could establish a potential therapeutic window for the treatment of different abnormalities associated with impaired HIF-1α.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-1α 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-1α protein. Further, initial screenings of the molecules were done based on Lipinski’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-1α 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-1α activity for the treatment of different abnormalities associated with impaired HIF-1α.