Design, synthesis, biological evaluation,and molecular modeling of inhibitors of enzymes involved in tumor and viral pathologies

The aim of my Ph.D. project was the design, synthesis, biological evaluation,and molecular modeling of enzymeinhibitorsinvolved in tumor and viral pathologies.Specifically, this thesis manuscript is divided into three main parts, presentingsome of the papers published during my doctoral work. These studieswere carried out in cooperation with the University of Palermounder the supervisionof Prof. Marco Tutone-Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF).The first part concerns the study of proteases. Proteases represent one of the most relevant groups of enzymes which catalyze the hydrolysis of peptide bonds. The research group with whom I worked during my Ph.D. hasactively been involved in the development of novel proteasome/immunoproteasome inhibitors. In this scenario, during my Ph.D., molecular modeling studies such as MD Binding (MDB), Binding Pose MetaDynamics (BPMD),and Induced Fit Docking (IFD) were carried out on the previously identified non-covalent compound 1thatwasshown to inhibit the β1i subunitof the immunoproteasome. The outcomes provided a dynamic point of view for the definition of the pharmacophore features. This dynamic pharmacophore modeling approach was used for the scaffold-hopping of new non-covalent inhibitors.Additionally, thesemolecular modeling studies were used to find new inhibitors of the β5i subunit. Thirty-six molecules from three commercial databases were selected to carry out the enzymatic assay for β1i/β5i subunits. Most of the molecules showed activityin the micromolar range, among them, RIM312 inhibitsboth subunits. The virtual screening campaign wascarried out in collaboration with the Fondazione Ri. Med -Molecular Informaticgroup-under the supervisionof Dr.Ugo Perricone.Moreover, novel proteasome inhibitors were synthesized.Theconjugation of these compounds with nano systemsbased on graphene quantum dots (GQDs) will improve their dispersibility in water and cellular uptake.The second part concerns molecular modeling studiesapplied todifferent anticancer targets.The first study concerns the design, synthesis,and biological evaluation of arylsulfonamides as telomerase inhibitors. This study was carried out in collaboration with the research group of prof. Tutone.A structure-based approach was carried out to design potential inhibitors of the telomerase active site. The MYSHAPE (Molecular dYnamics SHared PharmacophorE) approach and docking were used to screen an in-houselibrary of 126 arylsulfonamide derivatives. Promising hit compounds were synthesized using classical and green methods. Compound 2C was the most active(IC50=33 ± 4 μM) against the K-562 cell line compared with the known telomerase inhibitor BIBR1532 (IC50=208 ± 11 μM). In this study, the biological assayswere carried out in collaboration with the University of Palermo under Prof. Mario Allegra-Department of Biological, Chemical,and Pharmaceutical Sciences and Technologies (STEBICEF).The second study concerns the comparison of MD-derived pharmacophore models with docking on CDK-2 inhibitors.In this study,the performance of MD pharmacophore modeling approaches, the Common Hit Approach (CHA), and the Molecular dYnamics SHAred PharmacophorE (MYSHAPE) approach, were compared with semi-flexible constrained/unconstrained docking. This work aimedto enrich the hit list of a virtual screening on CDK-2 known inhibitors as a case study. The results highlighted that the MYSHAPE approach performs betterwhen multiple target-ligand complexes are available (ROC5%= 0.99). Moreover, using short MD simulations improves the screening performance(ROC5%= 0.98–0.99) with respect to docking (ROC5%= 0.89–0.94).The third study concerns the evaluation of the IKKβ Binding of Indicaxanthin against the active and inactive form, and the allosteric binding site of hIKKβby Induced-Fit Docking, Binding Pose Metadynamics, and. MD.The outcomes of this study showed that Indicaxanthin inhibits prevalently the active form of the hIKKβ.The last studyconcernsthe exploration ofthe SARS-CoV-2 proteome in the search for potential inhibitors via a structure-based pharmacophore modeling/docking approach.Due to the fact thePh.D. period took place during the pandemic, in the first months ofthis one, a computational drug repositioning campaign on the DrugBank databasewas developed. The final selection of the potential inhibitors was made considering the best binding energy for each compound obtained utilizing MM-GBSA calculation. Molecular recognition analysis showed that these compounds interact with the residues found as crucial for each targetof the SARS-CoV2 virus.In conclusion, during this doctoral project, it was demonstrated how the use of in silicotools could be effectivein the drug discovery process. The computational approachesallowed the identification of promising compounds,and the information obtained could be exploited to optimize the identified inhibitors.