Titolo della tesi: Application of Computer Aided Drug Design techniques for the identification of bioactive compounds
Molecular modeling is used in drug discovery in order to estimate molecular properties, to plan experiments and predict their outcomes, thus, to make decisions about them. Indeed, computational approaches are now key components in the drug discovery process and can help to speed up the release of new and improved active molecules.
The application of molecular modeling techniques in the early stage of drug discovery is one of the topics of this thesis. Here I present some works concerning virtual screening studies to identify compounds able to modulate some proteins involved in certain diseases.
AN-465-J137-985 shows an inhibiting binding capability and reduce the affinity of the C-SH3 domain for Gab2 (in s A549 and H1299 lung cancer cell line.)
(S)-RS4690 proved promising as new therapeutic agent against WNT-dependent colon cancer selectively binding to DVL1 PDZ (with an EC50 of 0.49 ± 0.11 μM and the growth of HCT116 cells that did not present the APC mutation with an EC50 value 7.1 ± 0.6 μM.)
RS6212 was found to have potent anticancer activity inhibiting lactate dehydrogenase in the micromolar range in several cancer cell lines, (such as the aerobic glycolysis-dependent Med1-MB cell line, the CRC HCT116 and SW620, the lung cancer A549, and the pancreatic PANC-1 cancer cells.)
All these inhibitors deserve to be further investigated as a starting point for the development of novel anticancer strategies.
Moreover, another computational technique used in drug discovery is molecular dynamics. It helps the knowledge about the stability of a ligand-protein complex and allowed us to simulate the conformational change after binding.
Co-solvent molecular dynamics was useful to identify a suitable binding site for AM-001, an allosteric inhibitor of EPAC1.
Accelerated molecular dynamics let us the recognition of the putative hot-spot residues involved in CCRL2-Chemerin binding.