Computational Methods for AL Amyloidosis

During my Bachelor's studies in Biotechnology, I was lucky to fall in love with computational chemistry early.

During the first year, the regular exams are Animal and Vegetable Biology, Inorganic Chemistry, Algebra, Geometry, Statistics, etc. In the last lesson of our Organic Chemistry course, my future supervisor, Prof. Giorgio Colombo, gave a lecture about his research group's work in computational drug discovery.
I immediately loved that.

Studying biotechnology has always been a decision deeply connected with the silent presence of my teenage years. I was angry and wanted revenge against the cancer that brought my grandparents and my aunt away. Biotechnology was the perfect choice to learn the army to defeat it. However, the wet lab pace was slow for me, and I always preferred the molecular level rather than the cellular genomic one.

After a small summer school of "Computer Aided Drug Design” from Scuola Normale di Pisa in September, I reached out to Giorgio to ask him if there was something I could start helping with. There was!

I’m grateful for how much Giorgio and the entire lab taught me. After two years of research, I developed a new computational protocol to identify small ligands that could stabilize protein misfolding in Light Chain Amyloidosis. AL Amyloidosis is a systemic protein misfolding diseases where toxic fibrils accumulate because of aggregate due to incorrect folding that expose sticky residues.

My research supports a new therapeutic approach, proposing small-molecule drug candidates as alternatives to aggressive chemotherapy or palliative natural remedies. — Abstract Following, full research is available at bit.ly/AL-amyloidosis.

Figure 7. Superimposition of the original crystal deposited under PDB code 6MG5 7 in light-blue and redocking of Coumarin 1 with pre-treated protein structure, both in violet. A: Complete superimposition with both ligands and light-chains. B: Focus on Ligand Docked in pocket, without visible pocket surface C:Focus on Ligand Docked in pocket, with visible pocket surface

Abstract

To carry out their functions in cells, proteins must fold into a specific three-dimensional structure named the Native State. Failure to reach this state generally leads to aggregation, with severe pathogenic consequences in vivo.

Many degenerative diseases are indeed related to protein misfolding: one of these is Light Chain Amyloidosis (AL), caused by the misfolding of immunoglobulin light chains. Current therapeutics block only 30-40% of the clonal plasma cells that secrete unstable light chains and are hardly tolerated by AL patients with severe cardiac damage.

The introduction of small-molecule stabilizers of protein folding could provide novel therapeutic opportunities: by favoring the stability of the native state, these molecules could aptly suppress pathogenic misfolding and aggregation. Here, we developed a new computational approach for the selection of small drug-like molecules potentially able to act as Light Chain folding stabilizers.

Through a combination of pharmacophores design, ligands docking, protein modeling, and database screening, we identified a series of novel chemotypes that were notably shown to be highly similar to novel drug candidates found independently via experimental brute-force screening.
The new promising chemotypes will be tested on immunoglobulin light chains from AL patients to evaluate their ability to prevent pathologic misfolding.