Prediction of Protein Aggregation Propensity via Data-Driven Approaches.
aggregation propensity
data-driven method
feature-based model
graph-based model
protein aggregation
Journal
ACS biomaterials science & engineering
ISSN: 2373-9878
Titre abrégé: ACS Biomater Sci Eng
Pays: United States
ID NLM: 101654670
Informations de publication
Date de publication:
13 11 2023
13 11 2023
Historique:
medline:
14
11
2023
pubmed:
16
10
2023
entrez:
16
10
2023
Statut:
ppublish
Résumé
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 the Protein Data Bank using Aggrescan3D 2.0. Moreover, feature- and graph-based models for predicting protein aggregation have been developed by using this database. The graph-based model outperformed the feature-based model, resulting in an
Identifiants
pubmed: 37844262
doi: 10.1021/acsbiomaterials.3c01001
doi:
Substances chimiques
Protein Aggregates
0
Proteins
0
Banques de données
figshare
['10.6084/m9.figshare.22492606.v1']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM