Molecular modelling and docking studies on heat shock protein 90 (Hsp90) inhibitors

Novel tropane analogues as Hsp90 inhibitors targeting colon cancer: Synthesis, biological estimation, and molecular docking study

New derivatives of tropane scaffold were prepared from the reaction of their thione or thioamide derivatives with α-halocarbonyl compounds. The structures of all new derivatives were assured and proved with their spectral data. The novel tropane derivatives were examined for their cytotoxicity on two colon tumor cell lines; Caco2 and HCT116 cells. The most active compounds 3, 4, 5, 9d and 14a displayed significant antitumor activities with IC50 range of 9.50 - 30.15 μM compared to doxorubicin. Moreover, they revealed reduced cytotoxic effect on WI-38 normal ones, signifying their great safety. With the aim of better understanding the inhibitory potential of such compounds on heat-shock protein 90 (Hsp90), there activities were assessed against such enzyme demonstrating high inhibitory activities with IC50 range of 56.58-78.85 nM. Western blotting was carried out to ensure the inhibitory activity on Hsp90, results showed that 3 markedly suppressed Hsp90 expression on Caco2 cell line. Additionally, a molecular docking analysis of the most potent derivatives at the Hsp90 binding site was carried out in order to approve the performed in vitro assays.

Deciphering the Inhibition Mechanism of under Trial Hsp90 Inhibitors and Their Analogues: A Comparative Molecular Dynamics Simulation

Heat shock protein 90 (Hsp90) performs functions in cellular activities together with other signaling pathways. Hsp90 is evolutionarily conserved and universally articulated as a human cancer-causing agent involved in lung cancer and breast cancer followed by colon and rectum cancers. It has emerged as an effective drug candidate, and inhibition may affect several signaling pathways associated with cancer spread. Therefore, in-silico approaches, molecular docking, molecular dynamics simulation, and binding free energy calculations were applied to create insights into the inhibition mechanism against Hsp90 to identify new cancer therapeutic drugs. Top-docked Hsp90-inhibitor complexes with their analogues were selected as the best complexes based on the GOLD fitness score and orientation. The significant interaction of Hsp90 inhibitors and their analogues were observed to be bound with active site residues as well as residing within the same cavity region. System stability factors RMSD, RMSF, beta-factor, and radius of gyration were analyzed for top-docked complexes and ensure strong binding interaction between inhibitors and the Hsp90 cavity. Cavity bound inhibitors were found to retain consistent hydrogen bonding during the simulation. The radial distribution function (RDF) illustrated that interacting active site residues drive the binding and stability of the inhibitors. Similarly, the axial frequency distribution, which is an indigenously developed analytical tool, produced noteworthy knowledge of the hydrogen-bonding pattern. Results yielded new insights into the design of cancer therapeutic drugs against Hsp90. This finding suggests that under trial Hsp90 inhibitors MPC-3100 could be a potential starting point into the development of potential anticancer agents with the possibility of future directions for the improvement of early existing Hsp90 inhibitors CNF-2024 and SNX-5422 as an anticancer agent.

Hierarchical virtual screening: identification of potential high-affinity and selective β(3)-adrenergic receptor agonists

The hierarchical virtual screening (HVS) study, consisting of pharmacophore modelling, docking and VS of the generated focussed virtual library, has been carried out to identify novel high-affinity and selective β(3)-adrenergic receptor (β-AR) agonists. The best pharmacophore model, comprising one H-bond donor, two hydrophobes, one positive ionizable and one negative ionizable feature, was developed based on a training set of 51 β(3)-AR agonists using the pharmacophore generation protocol implemented in Discovery Studio. The model was further validated with the test set, external set and ability of the pharmacophoric features to complement the active site amino acids of the homology modelled β(3)-AR developed using MODELLER software. The focussed virtual library was generated using the structure-based insights gained from our earlier reported comprehensive study focussing on the structural basis of β-AR subtype selectivity of representative agonists and antagonists. The HVS with the sequential use of the best pharmacophore model and homology modelled β(3)-AR in the screening of the generated focussed library has led to the identification of potential virtual leads as novel high-affinity and selective β(3)-AR agonists.

A web server for predicting inhibitors against bacterial target GlmU protein

Background

The emergence of drug resistant tuberculosis poses a serious concern globally and researchers are in rigorous search for new drugs to fight against these dreadful bacteria. Recently, the bacterial GlmU protein, involved in peptidoglycan, lipopolysaccharide and techoic acid synthesis, has been identified as an important drug target. A unique C-terminal disordered tail, essential for survival and the absence of gene in host makes GlmU a suitable target for inhibitor design.

Results

This study describes the models developed for predicting inhibitory activity (IC₅₀) of chemical compounds against GlmU protein using QSAR and docking techniques. These models were trained on 84 diverse compounds (GlmU inhibitors) taken from PubChem BioAssay (AID 1376). These inhibitors were docked in the active site of the C-terminal domain of GlmU protein (2OI6) using the AutoDock. A QSAR model was developed using docking energies as descriptors and achieved maximum correlation of 0.35/0.12 (r/r²) between actual and predicted pIC₅₀. Secondly, QSAR models were developed using molecular descriptors calculated using various software packages and achieved maximum correlation of 0.77/0.60 (r/r²). Finally, hybrid models were developed using various types of descriptors and achieved high correlation of 0.83/0.70 (r/r²) between predicted and actual pIC₅₀. It was observed that some molecular descriptors used in this study had high correlation with pIC₅₀. We screened chemical libraries using models developed in this study and predicted 40 potential GlmU inhibitors. These inhibitors could be used to develop drugs against Mycobacterium tuberculosis.

Conclusion

These results demonstrate that docking energies can be used as descriptors for developing QSAR models. The current work suggests that docking energies based descriptors could be used along with commonly used molecular descriptors for predicting inhibitory activity (IC₅₀) of molecules against GlmU. Based on this study an open source platform, http://crdd.osdd.net/raghava/gdoq, has been developed for predicting inhibitors GlmU.

Pharmacophore-based virtual screening and docking studies on Hsp90 inhibitors

Hsp90 (Heat shock protein 90) is an important therapeutic target for the treatment of cancer. To identify important chemical features for Hsp90 inhibitory activity, a 3D-QSAR pharmacophore model was developed using a set of 61 inhibitors (a training set of 31 and a test set of 30 compounds) belonging to a series of 2-amino-6-halopurine and 7'-substituted benzothiazolothio- and pyridinothiazolothio-purines. The best HypoGen model consisted of five pharmacophoric features: one hydrogen bond acceptor (HBA), one hydrogen bond donor (HBD) and three hydrophobic (HY) groups. It showed a high correlation coefficient (r = 0.943) and low root mean square deviation (RMSD = 0.751). This model was validated against 30 known Hsp90 inhibitors, where it showed a high predictive value for R(2)pred = 0.805], thus confirming that HY, HBA and HBD features are essential for Hsp90 inhibition. The validated pharmacophore model (Hypo-1) was used as a 3D query for virtual screening to retrieve potential inhibitors from the Maybridge and National Cancer Institute (NCI) databases. The hit compounds were subsequently subjected to molecular docking studies and, finally, five hits were prioritized as potential leads based on GoldScore function.