Investigation of Binding Characteristics of Phosphoinositide-dependent Kinase-1 (PDK1) Co-crystallized Ligands Through Virtual Pharmacophore Modeling Leading to Novel Anti-PDK1 Hits

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

Antifungal Activity of a Library of Aminothioxanthones

Fungal infections are one of the main causes of mortality and morbidity worldwide and taking into account the increasing incidence of strains resistant to classical antifungal drugs, the development of new agents has become an urgent clinical need. Considering that thioxanthones are bioisosteres of xanthones with known anti-infective actions, their scaffolds were selected for this study. A small library of synthesized aminothioxanthones (1-10) was evaluated for in vitro antifungal activity against Candida albicans, Aspergillus fumigatus, and Trichophyton rubrum; for the active compounds, the spectrum was further extended to other clinically relevant pathogenic fungi. The results showed that only compounds 1, 8, and 9 exhibited inhibitory and broad-spectrum antifungal effects. Given the greater antifungal potential presented, compound 1 was the subject of further investigations to study its anti-virulence activity and in an attempt to elucidate its mechanism of action; compound 1 seems to act predominantly on the cellular membrane of C. albicans ATCC 10231, altering its structural integrity, without binding to ergosterol, while inhibiting two important virulence factors-dimorphic transition and biofilm formation-frequently associated with C. albicans pathogenicity and resistance. In conclusion, the present work proved the usefulness of thioxanthones in antifungal therapy as new models for antifungal agents.

Exploring dual agonists for PPARα/γ receptors using pharmacophore modeling, docking analysis and molecule dynamics simulation

BACKGROUND

The peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors belonging to the nuclear receptor family. The roles of PPARα in fatty acid oxidation and PPARγ in adipocyte differentiation and lipid storage have been widely characterized. Compounds with dual PPARα/γ activity have been proposed, combining the benefits of insulin sensitization and lipid-lowering into one drug, allowing a single drug to reduce hyperglycemia and hyperlipidemia while preventing the development of cardiovascular complications.

METHODS

The new PPARα/γ agonists were screened through virtual screening of pharmacophores and molecular dynamics simulations. First, in the article, the constructed pharmacophore was used to screen the Ligand Expo Components-pub database to obtain the common structural characteristics of representative PPARα/γ agonist ligands. Then, the obtained ligand structure was modified and replaced to obtain 12 new compounds. Using molecular docking, ADMET and molecular dynamics simulation methods, the designed 12 ligands were screened, their docking scores were analyzed when they bound to the PPARα/γ dual targets, and also their stability and pharmacological properties were assessed when they were bound to the PPARα/γ dual targets.

RESULTS

We performed pharmacophore-based virtual screening for 22949 molecules in the Ligand Expo Components-pub database. Structural analysis and modification were performed on the compounds that were superior to the original ligand , and a series of compounds with novel structures were designed. Using precise docking, ADMET prediction and molecular dynamics methods, newly designed compounds were screened and verified, and the above compounds showed higher docking scores and lower side effects.

CONCLUSION

9 new PPARα/γ agonists were obtained by pharmacophore modeling, docking analysis and molecule dynamics simulation.

Structural Investigations of Aroylindole Derivatives through 3D-QSAR and Multiple Pharmacophore Modeling for the Search of Novel Colchicines Inhibitor

Background :

The ligand and structure based integrated strategies are being repeatedly and effectively employed for the precise search and design of novel ligands against various disease targets. Aroylindole derivative has a similar structural analogy as Combretastatin A-4, and exhibited potent anticancer activity on several cancer cell lines.

Objective:

To identify structural features of aroylindole derivatives through 3D-QSAR and multiple pharmacophore modelling for the search of novel colchicines inhibitor via virtual screening.

Method:

The present study utilizes ligand and structure based methodology for the establishment of structure activity correlation among trimethoxyaroylindole derivatives and the search of novel colchicines inhibitor via virtual screening. The 3D-QSAR studies were performed using Phase module and provided details of relationship between structure and biological activity. A single ligand based pharmacophore model was generated from Phase on compound 3 and compound 29 and three energetically optimized structure based pharmacophore models were generated from epharmacophore for co-crystallized ligand, compound 3 and compound 29 with protein PBD ID 1SA0, 5EYP and 5LYJ. These pharmacophoric features containing hit-like compounds were collected from commercially available ZINC database and screened using virtual screening workflow.

Results and Discussion:

The 3D-QSAR model studies with good PLSs statistics for factor four was characterized by the best prediction coefficient Q2 (0.8122), regression R2 (0.9405), SD (0.2581), F (102.7), P (1.56e-015), RMSE (0.402), Stability (0.5411) and Pearson-r (0.9397). The generated epharmacophores have GH scores over 0.5 and AUAC ≥ 0.7 indicated that all the pharmacophores were suitable for pharmacophore-based virtual screening. The virtual screened compounds ZINC12323179, ZINC01642724, and ZINC14238006 have showed similar structural alignment as co-crystallized ligand and showed the hydrogen bonding of ligand with ASN101, SER178, THR179, VAL238, CYS241 amino acid of protein.

Conclusion:

The study illustrates that the ligand and structure based pharmacophoric approach is beneficial for identification of structurally diverse hits, having better binding affinity on colchicines binding site as novel anticancer agents.