Pharmacophore modeling coupled with scaffold hopping to identify novel and potent ribosomal S6 kinase (RSK2) protein antagonists as anti-cancer agents

3-deazaadenosine: A promising novel p38γ antagonist with potential as a breast cancer therapeutic agent

Human p38γ protein kinase, or MAPK12, is a crucial signaling protein that is important in channelizing membrane signals to the nucleus in the MAPK cascade pathway, associated with breast and colorectal cancer, besides other forms of malignancies and atherosclerotic lesions too. P38γ has a significant contribution to the progression of breast carcinoma due to its multifaceted functions. Targeting p38γ for defining potent antagonists against p38γ can turn out to be an attractive and novel means of breast cancer therapeutics. Novel and potent lead molecules were designed utilizing computational drug design methodologies. Using high-throughput virtual screening, 1909 geometrically similar analogs of known inhibitors were generated, primarily using BIRB796, SB202190, ANP, CHEBI: 620708, and CHEBI: 524699. Chemical correctness was ensured using LigPrep for the standalone library, and Prep Wizard for p38γ using Maestro v.11.5. Using the Glide v5.5 flexible docking procedure on a standalone library of p38γ binding sites, we defined 18 potential leads and assessed their ADMET properties. Lead "1", among the proposed four p38γ antagonists with high-scoring and favorable interactions, was considered for 100 ns molecular dynamics simulations. Among the four proposed leads, Lead '1' displayed consistent and stable bonding interactions with p38γ throughout the 100 ns molecular dynamics (MD) simulations. Additionally, it formed water bridges, contributing to its strong association with the protein. Notably, Lead '1' (3-deazaadenosine) exhibited favorable root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) within the acceptable range of pharmacological properties. Thus, 3-deazaadenosine and its mimetic might be promising new directions for developing a novel class of antagonists for breast cancer treatment.

Pharmacophore- based virtual screening, 3D- QSAR, molecular docking approach for identification of potential dipeptidyl peptidase IV inhibitors

Pharmacophore modeling, molecular docking, and in silico ADME studies have been carried out to determine the binding mode and drug likeliness profile of Pyrrolidine derivatives as Dipeptidyl peptidase IV inhibitors. A five point pharmacophore model (AAADH_1) was generated using 96 compounds with IC₅₀ values ranging from 1.8 to 13000 nM. A statistically significant 3D-QSAR model was generated from the pharmacophore hypothesis. The model had a high correlation coefficient (R² - 0.92), cross validation coefficient (Q² - 0.776) and F value (F - 144) at 6 component Partial least square factor. Pearson r of 0.7124 indicated greater degree of confidence on the model. The accuracy and predictability of the generated model were checked by Enrichment factor, Receiver operating characteristic curves, area under curve, Boltzmann-enhanced discrimination of Receiver operating characteristic and the Robust initial enhancement. To identify novel and potent Dipeptidyl peptidase IV inhibitors, virtual screening was performed using the ligand and database screening. Considering the potent hit molecules on the basis of pharmacophore virtual screening, we have designed new molecules and further subjected to see the interaction with protein. The catalytic domain of Dipeptidyl peptidase IV enzyme in complex with Vildagliptin (PDB Code: 6B1E) was obtained from protein data bank with resolution 1.77 A°. Compound 75 showed the better binding (dock score -7.966) with protein than standard drug vildagliptin (Dock Score -6.554). The hits obtained on virtual screening of the database have provided new chemical starting points for design and development of novel Dipeptidyl peptidase IV inhibitory agents.Communicated by Ramaswamy H. Sarma.