Refining proteins interactions to tackle aggregation in AL amyloidosis via the computational design of inhibitory peptides

Proteins are biomolecules at the base of most fundamental processes in living organisms, ranging from cellular architecture to signal transduction, biosynthesis, regulatory pathways, immune response, etc. Although they can stand in an unbound configuration within the cell, the importance of their role emerges when they interact with ligands like other proteins. Therefore, understanding the mechanisms underpinning protein structural and interaction stability can provide insight into the functioning of the cellular machinery. This project first undertakes the study of such properties by comparing the role of non-bonded interactions (i.e. Coulombic and van der Waals potentials) with respect to the melting temperature and the binding affinity, two widely referenced experimental descriptors of protein folding and association stability, respectively. The protocols developed and the conclusions drawn in this phase were then probed in the investigation of two cases in which protein-protein interactions result in pathological scenarios, namely SARS-CoV-2 and AL amyloidosis. As for SARS-Cov-2, my contribution encompassed the calculation of non-bonded interactions and investigating their role in determining binding stability in the Spike-ACE2 and Spike-antibody complexes in relation to the physicochemical features of the mutated residues in viral variants and the immunogenicity of the Spike protein surface. Coming to AL amyloidosis, this is a highly patient-specific condition associated with the aggregation and deposition of immunoglobulin light chains resulting in organ failure. We focused on a mutated patient-derived aggregation-prone light chain and its non-aggregating germ-line counterpart and performed extensive molecular dynamics simulations, highlighting misfolding in the pathogenic monomer. Applying docking procedures to the misfolded configuration, we proposed the structure of a putative pathogenic dimer. We then moved to look for a peptide candidate for binding hindrance using a Monte Carlo-based mutagenesis protocol. Affinity calculations confirmed that the optimised peptide thus obtained could be a good competitor against pathogenic dimerisation.