Protein Misfolding in rare diseases: Hereditary Transthyretin Amyloidosis

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• Ugo Lomoio, Magna Graecia University of Catanzaro
• Friday 17 October 2025, 16:00-17:00
• Online - Google Meet: https://meet.google.com/jfo-bifc-yjf.

If you have a question about this talk, please contact Pietro Lio.

Transthyretin amyloidosis (ATTR) is a genetically diverse disorder caused by destabilizing mutations in the transthyretin (TTR) protein, leading to pathological aggregation. Although stabilizers such as tafamidis and acoramidis are approved, their efficacy across different TTR variants remains unclear.

In this presentation, we report an in silico pipeline that integrates AlphaFold3 for variant structure prediction, ESM2 for variant classification, DiffDock-L and AutoDock Vina for molecular docking, GROMACS for molecular dynamics analysis, and Diff-SBDD for ligand optimization and generation.

Our results show that the binding affinities of approved ligands vary significantly across TTR variants, with some mutations (e.g., W61L , Y98F) reducing binding ability despite being distant from the T4 binding site of TTR . ESM2 embeddings, when projected into two dimensions using UMAP , demonstrate clear separation between benign and pathogenic variants. Projections of benign mutants cluster near wild-type TTR , while pathogenic variants are located farther away. Distances between all mutants and wild-type TTR were computed, and a threshold was applied to perform binary classification, achieving a ROC AUC of 0.9948.

Furthermore, customized ligand optimization can recover binding affinity in specific destabilizing mutations. For example, optimization of the tafamidis ligand against the Y98F mutation yielded a substantial improvement in both ligand drug-likeness and the overall mutational landscape.

We also generated new ligands designed to improve binding at the Y98F -TTR T4 site. Binding affinities obtained for the most effective generated ligand were compared with those of acoramidis, showing improvements across nearly all tested mutations.

This integrative approach provides a foundation for precision drug design in ATTR and may also be applied to other protein misfolding disorders, enabling the development of personalized stabilizers tailored to individual mutational profiles.

This talk is part of the Data Science and AI in Medicine series.

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