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Title

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

Authors

Ghulam Md Ashraf 1,*, Magdah Ganash2, Alexiou Athanasios3,4,*

 

Affiliation

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

 

Email

Ghulam Md Ashraf – E-mail: ashraf.gm@gmail.com; gashraf@kau.edu.sa;
*Corresponding authors

 

Article Type

Research Article

 

Date

Received March 27, 2019; Accepted April 1, 2019; Published May 15, 2019

 

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

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

 

Citation

Ashraf et al. Bioinformation 15(5): 351-357 (2019)

 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.