From PARP1 to TNKS2 Inhibition: A Structure-Based Approach

Discovering Dually Active Anti-cancer Compounds with a Hybrid AI-structure-based Approach

Cancer's persistent growth often relies on its ability to maintain telomere length and tolerate the accumulation of DNA damage. This study explores a computational approach to identify compounds that can simultaneously target both G-quadruplex (G4) structures and poly(ADP-ribose) polymerase (PARP)1 enzyme, offering a potential multipronged attack on cancer cells. We employed a hybrid virtual screening (VS) protocol, combining the power of machine learning with traditional structure-based methods. PyRMD, our AI-powered tool, was first used to analyze vast chemical libraries and to identify potential PARP1 inhibitors based on known bioactivity data. Subsequently, a structure-based VS approach selected compounds from these identified inhibitors for their G4 stabilization potential. This two-step process yielded 50 promising candidates, which were then experimentally validated for their ability to inhibit PARP1 and stabilize G4 structures. Ultimately, four lead compounds emerged as promising candidates with the desired dual activity and demonstrated antiproliferative effects against specific cancer cell lines. This study highlights the potential of combining Artificial Intelligence and structure-based methods for the discovery of multitarget anticancer compounds, offering a valuable approach for future drug development efforts.

Chain Extension of Piperazine in Ethanol: Synthesis of 2-(4-(2-(Phenylthio)ethyl)piperazinyl)acetonitriles and ACAT-1 Inhibitors

A base-induced synthesis of 2-(4-(2-(phenylthio)ethyl)piperazinyl) acetonitriles by reaction of disulfides, 1-(chloromethyl)-4-aza-1-azonia bicyclo[2.2.2]octane chloride and trimethylsilyl cyanide is reported. The scope of the method is demonstrated with 30 examples. The reaction mechanism research indicates that the three-component reaction would be a SN2 reaction. The products exhibit good activities towards advanced synthesis of aqueous soluble acyl-CoA: cholesterol O-acyltransferase-1 (ACAT-1) inhibitors. Our work is superior as it uses less-odor disulfides as carbon sources and EtOH as solvent in a water and dioxygen insensitive reaction system, followed by a simple purification process.

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors

PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.

Unravelling the Mechanistic Role of Quinazolinone Pharmacophore in the Inhibitory Activity of Bis-quinazolinone Derivative on Tankyrase-1 in the Treatment of Colorectal Cancer (CRC) and Non-small Cell Lung Cancer (NSCLC): A Computational Approach

In recent years, tankyrase inhibition has gained a great focus as an anti-cancer strategy due to their modulatory effect on WNT/β-catenin pathway implicated in many malignancies, including colorectal cancer (CRC) and non-small cell lung cancer (NSCLC). Based on the structural homology in the catalytic domain of PARP enzymes, bis-quinazolinone 5 (Cpd 5) was designed to be a potent selective tankyrase inhibitor. In this study, we employed molecular dynamics simulations and binding energy analysis to decipher the underlying mechanism of TNK-1 inhibition by Cpd 5 in comparison with a known selective tankyrase, IWR-1. The Cpd 5 had a relatively higher ΔG_bind than IWR-1 from the thermodynamics analysis, revealing the better inhibitory activity of Cpd 5 compared to IWR-1. High involvement of solvation energy (ΔG_sol) and the van der Waals energy (ΔE_vdW) potentiated the affinity of Cpd 5 at TNK-1 active site. Interestingly, the keto group and the N3 atom of the quinazolinone nucleus of Cpd 5, occupying the NAM subsite, was able to form H-bond with Gly1185, thereby favoring the better stability and higher inhibitory efficacy of Cpd 5 relative to IWR-1. Our analysis proved that the firm binding of Cpd 5 was achieved by the quinazolinone groups via the hydrophobic interactions with the side chains of key site residues at the two subsite regions: His1201, Phe1188, Ala1191, and Ile1192 at the AD subsite and Tyr1224, Tyr1213, and Ala1215 at the NAM subsite. Thus, Cpd 5 is dominantly bound through π-π stacked interactions and other hydrophobic interactions. We believe that findings from this study would provide an important rationale towards the structure-based design of improved selective tankyrase inhibitors in cancer therapy.