Computational discovery of AKT serine/threonine kinase 1 inhibitors through shape screening for rheumatoid arthritis intervention.

Rheumatoid Arthritis (RA) is a chronic, symmetrical inflammatory autoimmune disorder characterized by painful, swollen synovitis and joint erosions, which can cause damage to bone and cartilage and be associated with progressive disability. Despite expanded treatment options, some patients still experience inadequate response or intolerable adverse effects. Consequently, the treatment options for RA remain quite limited. The enzyme AKT1 is crucial in designing drugs for various human diseases, supporting cellular functions like proliferation, survival, metabolism, and angiogenesis in both normal and malignant cells. Therefore, AKT serine/threonine kinase 1 is considered crucial for targeting therapeutic strategies aimed at mitigating RA mechanisms. In this context, directing efforts toward AKT1 represents an innovative approach to developing new anti-arthritis medications. The primary objective of this research is to prioritize AKT1 inhibitors using computational techniques such as molecular modeling and dynamics simulation (MDS) and shape-based virtual screening (SBVS). A combined SBVS approach was employed to predict potent inhibitors against AKT1 by screening a pool of compounds sourced from the ChemDiv and IMPPAT databases. From the SBVS results, only the top three compounds, ChemDiv_7266, ChemDiv_2796, and ChemDiv_9468, were subjected to stability analysis based on their high binding affinity and favorable ADME/Tox properties. The SBVS findings have revealed that critical residues, including Glu17, Gly37, Glu85, and Arg273, significantly contribute to the successful binding of the highest-ranked lead compounds at the active site of AKT1. This insight helps to understand the specific binding mechanism of these leads in inhibiting RA, facilitating the rational design of more effective therapeutic agents.

Harnessing allosteric inhibition: prioritizing LIMK2 inhibitors for targeted cancer therapy through pharmacophore-based virtual screening and essential molecular dynamics.

The therapeutic potential of small molecule kinase inhibitors in cancer treatment is well recognized. However, achieving selectivity remains a formidable challenge, primarily due to the structural similarity of ATP binding pockets among kinases. Allosteric inhibition, which involves targeting binding pockets beyond the ATP-binding site, provides a promising alternative to overcome this challenge. In this study, a meticulous approach was implemented to prioritize type 3 inhibitors for LIMK2, employing a range of techniques including Molecular Dynamics (MD) simulations, e-pharmacophore-guided High Throughput Virtual Screening (HTVS), MM/GBSA and ADMETox analyses, Density Functional Theory (DFT) calculations, and MM/PBSA investigations. The e-pharmacophore model identifies a hypothesis featuring five essential pharmacophoric elements (RRRAH). Through virtual screening of the ZINC compound database, we identified only five compounds that align with all four pharmacophoric features: ZINC1044382792, ZINC1433610865, ZINC1044109145, ZINC952869440, and ZINC490621334. These compounds not only exhibit higher binding affinity but also demonstrate favorable ADME/Tox profiles. Molecular dynamics simulations underscore the stability of hydrogen bond interactions with critical cryptic LIMK2 pocket residues, Asp469 and Arg474, only for two compounds: ZINC143361086 and ZINC1044382792. These compounds also exhibit superior occupancy interactions, as indicated by HOMO-LUMO analysis. Additionally, binding free energy calculations highlight the significant affinities of these two compounds when complexed with LIMK2: -83.491 ± 1.230 kJ/mol and -90.122 ± 1.248 kJ/mol for ZINC1044382792 and ZINC1433610862, respectively. Hence, this comprehensive investigation identifies ZINC1433610862 and ZINC1044382792 as prospective hits, representing promising leads for targeting LIMK2 in cancer therapeutics.Communicated by Ramaswamy H. Sarma.

Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches.

Diabetes mellitus (DM) is one of the major health problems worldwide. WHO have estimated that 439 million people may have DM by the year 2030. Several classes of drugs such as sulfonylureas, meglitinides, thiazolidinediones etc. are available to manage this disease, however, there is no cure for this disease. Salt inducible kinase 2 (SIK2) is expressed several folds in adipose tissue than in normal tissues and thus SIK2 is one of the attractive targets for DM treatment. SIK2 inhibition improves glucose homeostasis. Several analogues have been reported and experimentally proven against SIK for DM treatment. But, identifying potential SIK2 inhibitors with improved efficacy and good pharmacokinetic profiles will be helpful for the effective treatment of DM. The objective of the present study is to identify selective SIK2 inhibitors with good pharmacokinetic profiles. Due to the unavailability of SIK2 structure, the modeled structure of SIK2 will be an important to understand the atomic level of SIK2 inhibitors in the binding site pocket. In this study, different molecular modeling studies such as Homology Modeling, Molecular Docking, Pharmacophore-based virtual screening, MD simulations, Density Functional Theory calculations and WaterMap analysis were performed to identify potential SIK2 inhibitors. Five molecules from different databases such as Binding_4067, TosLab_837067, NCI_349155, Life chemicals_ F2565-0113, Enamine_7623111186 molecules were identified as possible SIK2 inhibitors.

WaterMap and Molecular Dynamic Simulation-Guided Discovery of Potential PAK1 Inhibitors Using Repurposing Approaches.

p21-Activated kinase 1 (PAK1) is positioned at the nexus of several oncogenic signaling pathways. Currently, there are no approved inhibitors for disabling the transfer of phosphate in the active site directly, as they are limited by lower affinity, and poor kinase selectivity. In this work, a repurposing study utilizing FDA-approved drugs from the DrugBank database was pursued with an initial selection of 27 molecules out of ∼2162 drug molecules, based on their docking energies and molecular interaction patterns. From the molecules that were considered for WaterMap analysis, seven molecules, namely, Mitoxantrone, Labetalol, Acalabrutinib, Sacubitril, Flubendazole, Trazodone, and Niraparib, ascertained the ability to overlap with high-energy hydration sites. Considering many other displaced unfavorable water molecules, only Acalabrutinib, Flubendazole, and Trazodone molecules highlighted their prominence in terms of binding affinity gains through ΔΔG that ranges between 6.44 and 2.59 kcal/mol. Even if Mitoxantrone exhibited the highest docking score and greater interaction strength, it did not comply with the WaterMap and molecular dynamics simulation results. Moreover, detailed MD simulation trajectory analyses suggested that the drug molecules Flubendazole, Niraparib, and Acalabrutinib were highly stable, observed from their RMSD values and consistent interaction pattern with Glu315, Glu345, Leu347, and Asp407 including the hydrophobic interactions maintained in the three replicates. However, the drug molecule Trazodone displayed a loss of crucial interaction with Leu347, which was essential to inhibit the kinase activity of PAK1. The molecular orbital and electrostatic potential analyses elucidated the reactivity and strong complementarity potentials of the drug molecules in the binding pocket of PAK1. Therefore, the CADD-based reposition efforts, reported in this work, helped in the successful identification of new PAK1 inhibitors that requires further investigation by in vitro analysis.

Novel huperzine A based NMDA antagonists: insights from molecular docking, ADME/T and molecular dynamics simulation studies.

Huperzine A (HupA) is an alkaloidal natural product and drug isolated from Chinese herb Huperzia serrata, which is a potent selective anticholinesterase inhibitor. HupA has symptomatic, cognitive-enhancing and protective effect on neurons against amyloid beta-induced oxidative injury and antagonizing N-methyl-d-aspartate receptors by blocking the ion channels. The present study aimed to identify the docking, ADME/T and molecular dynamics simulation parameters of a library of 40 analogues which can correlate the binding affinity, conformational stability and selectivity of the ligands towards NMDA receptor through in silico approach. Glide molecular docking analysis was performed for the designed analogues to understand the binding mode and interactions. MD simulations were performed to explain the conformational stability and natural dynamics of the interaction in physiological environmental condition of protein-ligand complex affording a better understanding of chemical-scale interactions between HupA and its analogues with NMDA channel that could potentially benefit the development of new drugs for neurodegenerative diseases involving NMDA receptors.