Efficient Empirical Valence Bond Simulations with GROMACS

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

CONTEXT

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.

METHODS

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.

Why Do Empirical Valence Bond Simulations Yield Accurate Arrhenius Plots?

Computer simulations of the temperature dependence of enzyme reactions using the empirical valence bond (EVB) method have proven to give very accurate results in terms of the thermodynamic activation parameters. Here, we analyze the reasons for why such simulations are able to correctly capture activation enthalpies and entropies and how sensitive these quantities are to parametrization of the reactive potential energy function. We examine first the solution reference reaction for the enzyme ketosteroid isomerase, which corresponds to the acetate catalyzed deprotonation of the steroid in water. The experimentally determined activation parameters for this reaction turn out to be remarkably well reproduced by the calculations. By modifying the EVB potential so that the activation and reaction free energies become significantly shifted, we show that the activation entropy is basically invariant to such changes and that ΔS⧧ is instead determined by the specific mixture of the underlying force fields in the transition state region. The coefficients of this mixture do not change appreciably when the EVB potential is modified within reasonable limits, and hence, the estimate of ΔS⧧ becomes very robust. This is further verified by examining a more complex concerted hydride and proton transfer reaction in the enzyme hydroxybutyrate dehydrogenase.

DP/MM: A Hybrid Model for Zinc-Protein Interactions in Molecular Dynamics

Zinc-containing proteins are vital for many biological processes, yet accurately modeling them using classical force fields is hindered by complicated polarization and charge transfer effects. This study introduces DP/MM, a hybrid force field scheme that utilizes a deep potential model to correct the atomic forces of zinc ions and their coordinated atoms, elevating them from MM to QM levels of accuracy. Trained on the difference between MM and QM atomic forces across diverse zinc coordination groups, the DP/MM model faithfully reproduces structural characteristics of zinc coordination during simulations, such as the tetrahedral coordination of Cys4 and Cys3His1 groups. Furthermore, DP/MM allows water exchange in the zinc coordination environment. With its unique blend of accuracy, efficiency, flexibility, and transferability, DP/MM serves as a valuable tool for studying structures and dynamics of zinc-containing proteins and also represents a pioneering approach in the evolving landscape of machine learning potentials for molecular modeling.