Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches.

DNA methyl transferases (DNMTs) are one of the crucial epigenetic modulators associated with a wide variety of cancer conditions. Among the DNMT isoforms, DNMT1 is correlated with bladder, pancreatic, and breast cancer, as well as acute myeloid leukemia and esophagus squamous cell carcinoma. Therefore, the inhibition of DNMT1 could be an attractive target for combating cancers and other metabolic disorders. The disadvantages of the existing nucleoside and non-nucleoside DNMT1 inhibitors are the main motive for the discovery of novel promising inhibitors. Here, pharmacophore modeling, 3D-QSAR, and e-pharmacophore modeling of DNMT1 inhibitors were performed for the large fragment database screening. The resulting fragments with high dock scores were combined into molecules. The current study revealed several constitutional pharmacophoric features that can be essential for selective DNMT1 inhibition. The fragment docking and virtual screening identified 10 final hit molecules that exhibited good binding affinities in terms of docking score, binding free energies, and acceptable ADME properties. Also, the modified lead molecules (GL1b and GL2b) designed in this study showed effective binding with DNMT1 confirmed by their docking scores, binding free energies, 3D-QSAR predicted activities and acceptable drug-like properties. The MD simulation studies also suggested that leads (GL1b and GL2b) formed stable complexes with DNMT1. Therefore, the findings of this study can provide effective information for the development/identification of novel DNMT1 inhibitors as effective anticancer agents.

Fragment-based investigation of thiourea derivatives as VEGFR-2 inhibitors: a cross-validated approach of ligand-based and structure-based molecular modeling studies.

Angiogenesis is mediated by the vascular endothelial growth factor (VEGF) that plays a key role in the modulation of progression, invasion and metastasis, related to solid tumors and hematological malignancies. Several small-molecule VEGFR-2 inhibitors are marketed, but their usage is restricted to specific cancers due to severe toxicities. Therefore, cost-effective novel small molecule VEGFR-2 inhibitors may be an alternative to overcome these adverse effects. Here, a set of thiourea-based VEGFR-2 inhibitors were considered for a combined fragment-based QSAR technique, structure-based molecular docking followed by molecular dynamics simulation studies to acquire insights into the key structural attributes and the binding pattern of enzyme-ligand interactions. Noticeably, amine-substituted quinazoline phenyl ring and a higher number of nitrogen atoms, and the hydrazide function in the molecular structure are crucial for VEGFR-2 inhibition whereas methoxy groups are detrimental to VEGFR-2 inhibition. The MD simulation study of sorafenib and thiourea derivatives explored the significance of urea and thiourea moiety binding at VEGFR-2 active site that can be utilized further in the future to design molecules for greater binding stability and better VEGFR-2 selectivity. Therefore, such findings can be beneficial for the development of newer VEGFR-2 inhibitors for further refinement to acquire better therapeutic efficacy.Communicated by Ramaswamy H. Sarma.

Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors.

Histone deacetylase 3 (HDAC3) is an epigenetic regulator that involves gene expression, apoptosis, and cell cycle progression, and the overexpression of HDAC3 is accountable for several cancers, neurodegeneracy, and many other diseases. Therefore, HDAC3 emerged as a promising drug target for the novel drug design. Here, we carried out the pharmacophore modeling using 50 benzamide-based HDAC3 selective inhibitors and utilized it for PHASE ligand screening to retrieve the hits with similar pharmacophore features. The dataset inhibitors of best hypotheses used to build the 3D QSAR model and the generated 3D QSAR model resulted in good PLS statistics with a regression coefficient (R2) of 0.89, predictive coefficient (Q2) of 0.88, and Pearson-R factor of 0.94 indicating its excellent predictive ability. The hits retrieved from pharmacophore-based virtual screening were subjected to docking against HDAC3 for the identification of potential inhibitors. A total of 10 hitsM1 to M10 were ranked using their scoring functions and further subject to lead optimization. The Prime MM/GBSA, AutoDock binding free energies, and ADMET studies were implemented for the selection of lead candidates. The four ligand molecules M1, M2, M3, and M4 were identified as potential leads against HDAC3 after lead optimization. The top two leads M1 and M2 were subjected to MD simulations for their stability evaluation with HDAC3. The newly designed leads M11 and M12 were identified as HDAC3 potential inhibitors from MD simulations studies. Therefore, the outcomes of the present study could provide insights into the discovery of new potential HDAC3 inhibitors with improved selectivity and activity against a variety of cancers and neurodegenerative diseases.

Fragment-based structural exploration and chemico-biological interaction study of HDAC3 inhibitors through non-linear pattern recognition, chemical space, and binding mode of interaction analysis.

HDAC3 is an emerging target for the identification and discovery of novel drug candidates against several disease conditions including cancer. Here, a fragment-based non-linear machine learning (ML) method along with chemical space exploration followed by a structure-based binding mode of interaction analysis study was carried out on some HDAC3 inhibitors to obtain the key structural features modulating HDAC3 inhibition. Both the ML and chemical space analysis identified several physicochemical and structural properties namely lipophilicity, polar and relative polar surface area, arylcarboxamide moiety, bulky fused aromatic group, n-alkyl, and cinnamoyl moieties, the higher number of oxygen atoms, π-electrons for the substituted tetrahydrofuronaphthodioxolone moiety favorable for higher HDAC3 inhibition. Moreover, hydrogen bond forming capabilities, the length and substitution position of the linker moiety, the importance of phenyl ring in the linker motif, the contribution of heterocyclic cap moieties for effective inhibitor binding at the HDAC3 catalytic site that correspondingly affects the HDAC3 inhibitory potency. Again, macrocyclic ring structure and cyclohexyl cap moiety are responsible for lower HDAC3 inhibition. The MD simulation study of selected compounds explained strong binding patterns at the HDAC3 active site as evidenced by the lower RMSD and RMSF values. Nevertheless, it also explained the importance of the crucial structural fragments derived from the fragment-based analysis during ligand-enzyme interactions. Therefore, the outcomes of this current structural analysis will be a useful tool for fragment-based drug discovery of effective HDAC3 inhibitors for clinical therapeutics in the future.Communicated by Ramaswamy H. Sarma.

Identification of potential Aurora kinase-C protein inhibitors: an amalgamation of energy minimization, virtual screening, prime MMGBSA and AutoDock.

Genomic instability is a trademark of cancer evolution, there is still a wide necessity to discover safe and effective anti-cancer drugs. Aurora kinase-C protein is a member of the Aurora kinase family involved in regulating mitosis phases of the cell cycle. Aurora kinase-C protein has an aberrant expression at spindle assembly checkpoint leading to human cervical cancer and colorectal cancer. Knowledge of 3D structure of the protein is vital for identify possible inhibitors. In the present study, 3D structure prediction of Aurora kinase-C protein is taken up by using comparative homology modeling techniques and minimized by NAMD-VMD Software. The quality of modeled protein was validated using ProSA and Ramachandran plot. Probable binding site in protein was identified from SiteMap and molecular docking based virtual screening was carried out using Asinex database to identify lead molecules. The obtained lead molecules were further optimized using PrimeMM-GBSA and AutoDock. ADME properties of the leads were calculated to ascertain the drug-like properties at lead molecules.Communicated by Ramaswamy H. Sarma.

Structure-based identification of potential novel inhibitors targeting FAM3B (PANDER) causing type 2 diabetes mellitus through virtual screening.

Type 2 diabetes mellitus is a metabolic disorder that requires potent therapeutic approaches. The FAM3B is a cytokine-like protein also referred to as PANcreatic-DERrived factor (PANDER) which mainly exists in pancreatic islets. In the process of identifying potential inhibitors with the aid of structure-based method PANDER protein is identified as a novel therapeutic target against type 2 diabetes mellitus as it involved in the development of type 2 diabetes by negatively regulating the pancreatic ß-cell function and insulin sensitivity in the liver. In the present study, the 3d model of target protein FAM3B was generated by homology modeling technique using the MODELLER9.9 program. The assessment of the structural stability of the 3d model was established by energy minimization technique. The structural quality was evaluated with standard validating protocols. Binding regions of the target protein has been determined by literature and SiteMap tool. In the current study of research, the FAM3B model was subjected to molecular screening with the Asinex-elite database of 14849 output molecules using the Glide virtual screening module in the Schrodinger suite. The final XP descriptor output of 14 molecules was analyzed and prioritized based on molecular interactions at the FAM3B active site. The docking score, binding free energies (Prime MM/GBSA) and bioavailability were undertaken into the consideration to identify lead inhibitors. The identified lead compounds were checked for ADME properties all falling within the permeable ranges. The analysis of results gave the insight to develop the novel therapeutic strategies against type 2 diabetes mellitus.