Molecular dynamic investigation for Roco4 kinase inhibitor as treatment options for parkinsonism.

Abstract

Parkinson's disease is a neurodegenerative condition characterized by the degeneration of dopaminergic neurons, resulting in motor disabilities such as rigidity, bradykinesia, postural instability, and resting tremors. While the exact cause of Parkinson's remains uncertain, both familial and sporadic forms are often associated with the G2019S mutation found in the kinase domain of LRRK2. Roco4 is an analogue of LRRK2 protein in Dictyostelium discoideum which is an established model organism to investigate LRRK2 inhibitors. In this study, the potential treatment of Parkinson's was explored by inhibiting the activity of the mutated LRRK2 protein using Roco4 as the base protein structure. Mongolicain-A and Bacoside-A exhibited significant selectivity towards the G2019S mutation, displaying a binding affinity of - 12.3 Kcal/mol and - 11.4 Kcal/mol respectively. Mongolicain-A demonstrated increased specificity towards Roco4, while Bacoside-A demonstrated significant binding affinity to all 34 kinases proteins alike. The Molecular Dynamics Studies (MDS) results strongly suggests that Mongolicain-A is a significant inhibitor of Roco4 kinase. ADMET and drugability analysis also suggests that among the two best ligands, Mongolicain-A demonstrates significant physicochemical properties to be suitable for best drug like molecule. Based on the in-silico molecular docking, molecular dynamic simulation, ADMET and drugability analyses, it is strongly suggested that, Mongolicain-A could be a potential candidate for treatment and management of Parkinson's disease via inhibition of LRRK2 protein. Further in-vitro and in-vivo investigations are in demand to validate these findings. To identify potential inhibitors, 3069 phytochemicals were screened using molecular docking via AutoDock Vina. Molecular Dynamics Simulation was carried out using GROMACS 2022.2 for a duration of 100ns per complex to study the stability and inhibition potential of the protein ligand complexes. ADMET analysis was carriedout using Molinspiration and preADMET web tool.