Computing Molecular Devices in L.major through Transcriptome Analysis: Structured Simulation Approach.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 26901858.
- Also identified by DOI 10.1371/journal.pone.0148909 and PMC identifier 4768835.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
In the modern era of post genomics and transcriptomics, non-coding RNAs and non-coding regions of many RNAs are a big puzzle when we try deciphering their role in specific gene function. Gene function assessment is a main task wherein high throughput technologies provide an impressive body of data that enables the design of hypotheses linking genes to phenotypes. Gene knockdown technologies and RNA-dependent gene silencing are the most frequent approaches to assess the role of key effectors in a particular scenario. Ribozymes are effective modulators of gene expression because of their simple structure, site-specific cleavage activity, and catalytic potential. In our study, after an extensive transcriptomic search of Leishmania major transcriptome we found a Putative ATP dependent DNA helicase (Lmjf_09_0590) 3' UTR which has a structural signature similar to well-known HDV hammerhead ribozyme, even though they have variable sequence motifs. Henceforth, to determine their structural stability and sustainability we analyzed our predicted structural model of this 3'UTR with a 30ns MD simulation, further confirmed with 100ns MD simulation in presence of 5mM MgCl2 ionic environment. In this environment, structural stability was significantly improved by bonded interactions between a RNA backbone and Mg2+ ions. These predictions were further validated in silico using RNA normal mode analysis and anisotropic network modelling (ANM) studies. The study may be significantly imparted to know the functional importance of many such 3'UTRs to predict their role in a mechanistic manner.
Medical subject headings
- Computational Biology
- Gene Expression Profiling
- Leishmania major
- Transcriptome