COMPUTATIONAL DESIGN OF PEPTIDES AND PEPTIDOMIMETICS AS POTENTIAL THERAPEUTIC AGENTS TO SATISFY PRESSING MEDICAL NEEDS

Over the past two decades, peptide drug discovery showed a dramatic increase of interest due to the recent scientific findings and achievements made on the pharmacokinetic profile and peptide delivery. In addition, it is possible to mimic the secondary structure and the pharmacological effects of the peptides by designing peptidomimetics, which do not contain the fleeting amide bonds. In this research project, I have applied advanced computational approaches in combination with biochemical and biophysical analysis, to different case studies, in order to discover, design, and optimize the structure of peptides and peptidomimetics capable of interacting with biological targets critically involved in several diseases. Accordingly, in the case study 1, many efforts have been dedicated at identifying potential inhibitors of HMGB1 protein, an emerging target for the development of anti-inflammatory drugs. Then, my attention was focused on the design of innovative potential drugs endowed with a dual inhibitory activity against PCSK9 and HMG-CoAR protein targets, both crucial for the treatment of hypercholesterolemia (case study 2). In the case study 3, several computational approaches have been applied to discover ligands with potential anticancer activity capable of interfering with the autophagy machinery in which GABARAP protein is involved. Finally, the machine learning weaponry was used in order to identify peptides able to inhibit WWP1 protein, a new promising target of anticancer drugs (case study 4) for which no drug candidates have been reported to date in literature.