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Bioinformation. 2019 May 15;15(5):351-357. doi: 10.6026/97320630015351. eCollection 2019.

Computational analysis of non-coding RNAs in Alzheimer's disease.

Author information

1
King Fahd Medical Research Center, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia.
2
Department of Biology, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
3
Novel Global Community Educational Foundation, 7 Peterlee Place, Hebersham, NSW 2770, Australia.
4
AFNP Med, Austria.

Abstract

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.

KEYWORDS:

17A; Alzheimer's disease; BACE1-AS; NAT-Rad18; RAD18; hnRNP Q; long noncoding RNAs; secondary structure prediction; strict β-turns; structural alignment

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