Machine Learning-Based Prediction of Transthyretin-Binding Activity and Its Application to Screening Food-Derived Compounds for ATTR Amyloidosis.

Background/

Objectives

Transthyretin amyloidosis (ATTR) is a progressive disease caused by the dissociation of the transthyretin (TTR) tetramer, leading to amyloid fibril formation. Although pharmacological stabilizers have been developed, preventive strategies for wild-type ATTR (ATTRwt) have not been established. This study developed a computational model to predict TTR-binding activity from chemical structures and applied it to the exploratory prioritization of food-derived compounds.

Methods

A machine learning model was constructed using TTR-8-anilino-1-naphthalenesulfonic acid displacement assay data from the Tox24 Challenge. An integrated dataset compiled from multiple literature sources was used to compare predicted TTR-binding activity with amyloid fibril formation inhibition, and the trained model was applied to the PhytoHub database.

Results

The model achieved a root mean square error of 21.34 and a mean absolute error of 15.9 percentage points on an external test dataset, a performance comparable to that of the top-scoring models in the Tox24 Challenge. The predicted TTR-binding activity showed positive correlations with inhibition of amyloid fibril formation, Pearson's r = 0.673 and Spearman's ρ = 0.602. The model prioritized 39 compounds with high predicted binding activity, predominantly polyphenols.

Conclusions

These results suggest that the proposed in silico method may be useful for the exploratory prioritization of food-derived compounds with potential relevance to TTR stabilization.