Exploring Ruthenium‐Based Organometallic Inhibitors against Plasmodium falciparum Calcium Dependent Kinase 2 (PfCDPK2): A Combined Ensemble Docking, QM/MM and Molecular Dynamics Study

Abstract

Recent advances in the metal‐organic framework (MOF) have accelerated the discovery of novel metal‐based anticancer, antibacterial and antimalarial compounds. This is substantiated by many serendipitously discovered metals (Ru, Rh, and Ir) based inhibitors that established the importance of metal inserted into the known organic scaffold. Conversely, it is possible to design novel bioactive compounds by mimicking hypervalent carbon atoms by transition metals. This process can be facilitated by computational drug discovery by treating metal centres using optimized parameters that can be used for molecular docking and molecular dynamics simulations. Further, the method can be plugged with high computational power and refined algorithms to interpret chemical phenomena with atomic‐level insights. In the present work, we have demonstrated an approach for parameterizing three organometallic ligands (FLL, E52, and staurosporine) using MCPB.py. In particular, we report that E52 and FLL have a better shape complimentary and affinity compared to staurosporine identified inhibitor (staurosporine) against Calcium‐dependent protein kinases 2 (CDPK2). This study also revealed that a flexible approach (ensemble) outperforms the given target with dynamic movements. The calculated MM‐PBSA energies for staurosporine, FLL and E52 were −66.461±2.192, −67.182±1.971 and −91.339±2.745 kJ/mol, respectively.

Identification of New KRAS G12D Inhibitors through Computer-Aided Drug Discovery Methods

“…Additionally, apart from switch I and switch II RMSF fluctuations (Figure 2B) commonly seen in mutated KRAS systems [26,27], no significant RMSF fluctuations were observed. PCA is a powerful tool to gain knowledge on conformational changes in a structure, with many researchers now using PCA to study ligand binding induced protein conformation changes [29,30]. In line with the RMSD and RMSF profiles, the PCA results also displayed fewer correlated motions upon hit molecules binding in comparison to reference.…”

“…As expected, the resulting plot indicated that eigenvalues for the first few vectors were higher, with the most important motions in our case contained in the first, second, and third eigenvectors. Figure 7A also demonstrates that values for the first few vectors were higher for Protein-Reference as compared to Protein-Hits, indicating that the binding of Reference leads to greater conformational changes in proteins dynamics as compared to binding of Hits [29]. We generated a 2D projection plot of these components for both the Protein-Reference and Protein-Hits (Figure 7B).…”

“…PCA was conducted to assess the ligand binding induced correlated motions. The overall significant motion of the protein is always controlled by the first few eigenvectors [29,30]. The top 10 eigenvectors accounted for 70%, 72%, 55%, 60%, and 58% motions for Protein-Reference, Hit1, Hit2, Hit3, and Hit4, respectively.…”

“…PCA is a useful tool to extract the most useful and meaningful elements (or the principal components) from MD trajectories and would assist in assessing the protein conformational changes upon ligand binding [29]. This was accomplished by calculating eigenvalue and eigenvectors for the covariance matrix, where the eigenvalue represents the magnitude and the eigenvector represents the direction of the motion of the biomolecules during the simulation.…”

Drug repurposing against main protease and RNA-dependent RNA polymerase of SARS-CoV-2 using molecular docking, MM-GBSA calculations and molecular dynamics

“…Three repurposing approaches are used, which can be a computational approach, an experimental approach or mixed approaches [ 25 ]. Great advances in the development of computational drugs (CDDs) have led to more advanced algorithms that play a huge role in advances in drug discovery processes [ 6 , 29 , 30 ].…”