Parkinson's disease (PD) is a disorder of the central nervous system that is caused due to the death of the dopaminergic neurons in the region of the brain called substantia nigra. Mutations in LRRK2 genes are associated with disease condition and it's been reported as crucial factor for drug resistance. Identification of deleterious mutations and studying the structural and functional impact of such mutations may lead to the identification of potential selective inhibitors. In this study, we analyzed 52 PD associated mutations, among that 20 were identified as highly deleterious. The deleterious mutations G2019S and I2020T in the kinase domain were playing a key role in causing resistance to drug levedopa. Molecular docking analyses have been performed to understand the binding affinity of levodapa with LRRK2 in wild and mutant condition. Molecular docking results show that levedopa binds differentially and obtained less number of hydrogen bonds in compared with wild type LRRK2. In addition, molecular dynamics simulations were performed to study the efficacy of docked complexes and it was observed that the efficacy of the mutant complexes (G2019S-Levodopa and I2020T-Levodopa) affected in the presence of mutation. Finally, through virtual screening approach specific inhibitors SCHEMBL6473053 and SCHEMBL1278779 have been identified that could potentially inhibit LLRK2 mutants G2019S and I2020T respectively. Over all this computational investigation correlates the impact of genotypic modulation in structure and function of drug target which enhanced in the identification of precision medicine to treat PD.
Objective: This works aims at analyzing the potential of Naringenin (NAR) as an antidepressant drug by computational methods.
Methods: The database Protein Data Bank and PubChem were used to retrieve the three-dimensional structures of the protein and the compound. The software Discovery Studio was used to study the interactions between the protein and the ligand.
Results: NAR, one of the flavanone, which has strong anti-inflammatory and antioxidant activities, is studied for its antidepressant and neuroprotective effects through in silico approach. Interaction study and pharmacophore analysis using Discovery Studio show that the molecule NAR interacts with the protein at different sites. The interaction has a maximum dock score at position B.
Conclusion: The compound NAR shows to have antidepressant quality from the computational study. The docking studies show promising result that NAR can be explored further as a potential drug.
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