Enhancing protein aggregation prediction: a unified analysis leveraging graph convolutional networks and active learning.

ABSTRACT: Protein aggregation (PA) is a critical phenomenon associated with Alzheimer's and Parkinson's disease. Recent studies have suggested that factors like aggregation-prone regions (APRs) and β-strand interactions are crucial in understanding such behavior. While experimental methods have provided valuable insights, there has been a shift towards computational strategies, particularly machine learning, for their efficacy and speed. The challenge, however, lies in effectively incorporating structural information into these models. This study constructs a Graph Convolutional Network (GCN) to predict PA scores with the expanded and refined Protein Data Bank (PDB) and AlphaFold2.0 dataset. We employed AGGRESCAN3D 2.0 to calculate PA propensity and to enhance the dataset, we systematically separate

SUBMITTER: Sun J 

PROVIDER: S-EPMC11447823 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature