A data-driven computational study of protein-protein and protein-glycan interactions

This PhD thesis focuses on the employment of computational approaches for the study of the interactions between biomolecules, a broad term that accounts for different molecular species ranging from proteins to small ligands. Understanding how biomolecules recognize each other, thus giving rise to complexes or assemblies, is a key point for the comprehension of biological mechanisms in living organisms and for application purposes in a variety of fields, among which drug design. These difficult and multidisciplinary issues strongly exploit in silico approaches, which, in the last decades, have become increasingly efficient and essential for supporting and guiding the experiments. The research activity carried out during my PhD work has mainly dealt with two projects. The first one revolves around protein-protein interactions and concerns a specific use-case, namely the necessity to predict how two affitins bind the human epidermal growth factor receptor 2 (HER2). This project has been carried out in collaboration with Dr. Alessandro Maiocchi (Bracco S.p.A – owner of two patents that cover the use of the two affitins as molecular probes targeting HER2), and Dr. Elisabetta Moroni (SCITEC, Italian National Research Council). The second project has been conducted at the Computational Structural Biology group (Bijvoet Centre for Biomolecular Research, Universiteit Utrecht) under the supervision of Prof. Alexandre Bonvin and Dr. Marco Giulini. It aims at building a reliable protocol, based on the software HADDOCK3, which is developed at the CSB group, for the prediction of protein-glycan complexes.