Targeting PARP14: An in silico framework for identifying novel Competitive inhibitors via 3D-QSAR pharmacophore modeling and molecular dynamics.

Post-translational modifications fine-tune protein function and regulate key signalling pathways in eukaryotic cells. ADP-ribosylation, which is catalyzed by the poly(ADP‒ribose) polymerase (PARP) family of enzymes, governs processes such as transcription, DNA repair, and inflammation. PARP14, a mono-ADP-ribosyltransferase, has emerged as a key player in cancer, with its overexpression linked to aggressive B-cell lymphomas and metastatic prostate cancer, positioning it as a promising therapeutic target. This study aimed to identify novel PARP14 inhibitors by repurposing existing compounds for anticancer applications via a ligand-based computational strategy. Using advanced techniques for 3D quantitative structure-activity relationship and pharmacophore modeling, we created a reliable pharmacophore model (Hypo1) via a varied dataset of 60 confirmed PARP14 inhibitors for accuracy. The evaluation of more than 71,540 compounds from the DrugBank and IBScreen libraries through virtual screening, followed by molecular docking studies, resulted in the assessment of these compounds against Veber's and Lipinski's drug-like criteria and optimal ADMET properties. This process identified four promising candidates: Furosemide, Vilazodone, STOCK1N-42868, and STOCK1N-92908. Molecular dynamics simulations and MM-PBSA analysis provided additional evidence of the stability and positive interactions of these ligands with PARP14. Furosemide and Vilazodone exhibited significant binding affinity and anticancer properties, whereas STOCK1N-42868 emerged as a novel candidate with promising in silico results. These findings suggest that Furosemide and Vilazodone could be effectively repurposed as PARP14 inhibitors, offering a strategic approach to enhance the efficacy of cancer treatment, whereas STOCK1N-42868 represents an exciting avenue for further research. This study emphasizes the possible applications of computational methods for finding new drugs and stresses the importance of pre-clinical research to examine how these inhibitors work in cancer treatment.