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
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

Pagination

6451-6463

Auteurs

Seungpyo Kang (S)

School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu 06978, Seoul, Republic of Korea.

Minseon Kim (M)

School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu 06978, Seoul, Republic of Korea.

Jiwon Sun (J)

School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu 06978, Seoul, Republic of Korea.

Myeonghun Lee (M)

School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu 06978, Seoul, Republic of Korea.

Kyoungmin Min (K)

School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu 06978, Seoul, Republic of Korea.

Articles similaires

Databases, Protein Protein Domains Protein Folding Proteins Deep Learning
Humans Computational Biology ROC Curve Algorithms Proteins

Strain learning in protein-based mechanical metamaterials.

Naroa Sadaba, Eva Sanchez-Rexach, Curt Waltmann et al.
1.00
Serum Albumin, Bovine Stress, Mechanical Animals Polymers Materials Testing
Proteins Protein Binding Ligands Internet Databases, Protein

Classifications MeSH