Homology Modeling and Structural Docking Analysis on a Human BDNF Gene by Using Computational Algorithms

Abstract

Brain-derived neurotrophic factor (BDNF) is a neurotrophin that interacts with TrkB and p75NTR receptors, playing a crucial role in neuronal plasticity, differentiation, and neurotransmission. The BDNF gene regulates glutamatergic and GABAergic synaptic plasticity while influencing serotonergic and dopaminergic pathways. Despite its biological significance, the structural and functional properties of BDNF remain incompletely understood, posing a challenge in the development of therapeutic strategies. To address this, computational approaches were employed to analyze BDNF, predict its structure, and identify potential drug candidates. This study investigates the structural and functional properties of BDNF through sequence analysis and structural modeling. Our findings indicate that BDNF is negatively charged, non-polar, hydrophilic, and soluble, with a GRAVY score of -0.456; however, it is generally unstable due to its physicochemical properties. Structural analysis revealed a dominance of α-helices over β-type structures, which are critical for its functional elements. Additionally, interaction network analysis underscores the role of BDNF-related signaling pathways in cancer development. In-silico modeling was performed to assess BDNF as a potential target for protein-ligand docking. Docking studies using the PyRx tool identified IND24, Congo red, Neoamphimedine, Amphimedine, Deoxyamphimedine, and Emetine as the most stable binders with high docking scores. These findings suggest that BDNF could serve as a viable target for drug discovery, aiding in the identification of therapeutic candidates for neurological and neurodegenerative disorders.