  Protein aggregation occurs when misfolded or unfolded proteins physically
bind together, and can promote the development of various amyloid diseases.
This study aimed to construct surrogate models for predicting protein
aggregation via data-driven methods using two types of databases. First, an
aggregation propensity score database was constructed by calculating the scores
for protein structures in Protein Data Bank using Aggrescan3D 2.0. Moreover,
feature- and graph-based models for predicting protein aggregation have been
developed using this database. The graph-based regression model outperformed
the feature-based model, resulting in R2 of 0.95, although it intrinsically
required protein structures. Second, for the experimental data, a feature-based
model was built using Curated Protein Aggregation Database 2.0, to predict the
aggregated intensity curves. In summary, this study suggests the approaches
that are more effective in predicting protein aggregation, depending on the
type of descriptor and the database.
