Protein aggregation: in silico algorithms and applications.
Aggregation kinetics
Aggregation propensity
Algorithm
Molecular dynamics
Peptide assembly
Prediction
Protein aggregation
Journal
Biophysical reviews
ISSN: 1867-2450
Titre abrégé: Biophys Rev
Pays: Germany
ID NLM: 101498573
Informations de publication
Date de publication:
Feb 2021
Feb 2021
Historique:
received:
06
11
2020
accepted:
01
01
2021
entrez:
22
3
2021
pubmed:
23
3
2021
medline:
23
3
2021
Statut:
epublish
Résumé
Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.
Identifiants
pubmed: 33747245
doi: 10.1007/s12551-021-00778-w
pii: 778
pmc: PMC7930180
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
71-89Informations de copyright
© International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2021.
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