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.
@article{arxiv.2304.03293,
title = {Prediction of Protein Aggregation Propensity via Data-driven Approaches},
author = {Seungpyo Kang and Minseon Kim and Jiwon Sun and Myeonghun Lee and Kyoungmin Min},
journal= {arXiv preprint arXiv:2304.03293},
year = {2023}
}