Advances in the Prediction of Protein Aggregation Propensity.
Amyloid
biocomputational
approaches
bioinformatics
protein aggregation
protein structure
therapeutic proteins.
Journal
Current medicinal chemistry
ISSN: 1875-533X
Titre abrégé: Curr Med Chem
Pays: United Arab Emirates
ID NLM: 9440157
Informations de publication
Date de publication:
2019
2019
Historique:
received:
10
03
2017
revised:
14
04
2017
accepted:
20
04
2017
pubmed:
8
7
2017
medline:
2
11
2019
entrez:
8
7
2017
Statut:
ppublish
Résumé
Protein aggregation into β-sheet-enriched insoluble assemblies is being found to be associated with an increasing number of debilitating human pathologies, such as Alzheimer's disease or type 2 diabetes, but also with premature aging. Furthermore, protein aggregation represents a major bottleneck in the production and marketing of proteinbased therapeutics. Thus, the development of methods to accurately forecast the aggregation propensity of a certain protein is of much value. A myriad of in vitro and in vivo aggregation studies have shown that the aggregation propensity of a certain polypeptide sequence is highly dependent on its intrinsic properties and, in most cases, driven by specific short regions of high aggregation propensity. These observations have fostered the development of a first generation of algorithms aimed to predict protein aggregation propensities from the protein sequence. A second generation of programs able to map protein aggregation on protein structures is emerging. Herein, we review the most representative online accessible predictive tools, emphasizing their main distinctive features and the range of applications. In this review, we describe representative biocomputational approaches to evaluate the aggregation properties of protein sequences and structures, while illustrating how they can become very useful tools to target protein aggregation in biomedicine and biotechnology.
Sections du résumé
BACKGROUND
BACKGROUND
Protein aggregation into β-sheet-enriched insoluble assemblies is being found to be associated with an increasing number of debilitating human pathologies, such as Alzheimer's disease or type 2 diabetes, but also with premature aging. Furthermore, protein aggregation represents a major bottleneck in the production and marketing of proteinbased therapeutics. Thus, the development of methods to accurately forecast the aggregation propensity of a certain protein is of much value.
METHODS/RESULTS
RESULTS
A myriad of in vitro and in vivo aggregation studies have shown that the aggregation propensity of a certain polypeptide sequence is highly dependent on its intrinsic properties and, in most cases, driven by specific short regions of high aggregation propensity. These observations have fostered the development of a first generation of algorithms aimed to predict protein aggregation propensities from the protein sequence. A second generation of programs able to map protein aggregation on protein structures is emerging. Herein, we review the most representative online accessible predictive tools, emphasizing their main distinctive features and the range of applications.
CONCLUSION
CONCLUSIONS
In this review, we describe representative biocomputational approaches to evaluate the aggregation properties of protein sequences and structures, while illustrating how they can become very useful tools to target protein aggregation in biomedicine and biotechnology.
Identifiants
pubmed: 28685682
pii: CMC-EPUB-84519
doi: 10.2174/0929867324666170705121754
doi:
Substances chimiques
Protein Aggregates
0
Proteins
0
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
3911-3920Informations de copyright
Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.