Computational analysis of non-coding RNAs in Alzheimer's disease.
17A
Alzheimer's disease
BACE1-AS
NAT-Rad18
RAD18
hnRNP Q
long noncoding RNAs
secondary structure prediction
strict β-turns
structural alignment
Journal
Bioinformation
ISSN: 0973-2063
Titre abrégé: Bioinformation
Pays: Singapore
ID NLM: 101258255
Informations de publication
Date de publication:
2019
2019
Historique:
received:
27
03
2019
accepted:
01
04
2019
entrez:
29
6
2019
pubmed:
30
6
2019
medline:
30
6
2019
Statut:
epublish
Résumé
Latest studies have shown that Long Noncoding RNAs corresponds to a crucial factor in neurodegenerative diseases and next-generation therapeutic targets. A wide range of advanced computational methods for the analysis of Noncoding RNAs mainly includes the prediction of RNA and miRNA structures. The problems that concern representations of specific biological structures such as secondary structures are either characterized as NP-complete or with high complexity. Numerous algorithms and techniques related to the enumeration of sequential terms of biological structures and mainly with exponential complexity have been constructed until now. While BACE1-AS, NATRad18, 17A, and hnRNP Q lnRNAs have been found to be associated with Alzheimer's disease, in this research study the significance of the most known β-turn-forming residues between these proteins is computationally identified and discussed, as a potentially crucial factor on the regulation of folding, aggregation and other intermolecular interactions.
Identifiants
pubmed: 31249438
doi: 10.6026/97320630015351
pii: 97320630015351
pmc: PMC6589468
doi:
Types de publication
Journal Article
Langues
eng
Pagination
351-357Références
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