Abstract
RNA molecules are essential players in many fundamental biological processes. Prokaryotes and eukaryotes have distinct RNA classes with specific structural features and functional roles. Computational prediction of protein structures is a research field in which high confidence three-dimensional protein models can be proposed based on the sequence alignment between target and templates. However, to date, only a few approaches have been developed for the computational prediction of RNA structures. Similar to proteins, RNA structures may be altered due to the interaction with various ligands, including proteins, other RNAs, and metabolites. A riboswitch is a molecular mechanism, found in the three kingdoms of life, in which the RNA structure is modified by the binding of a metabolite. It can regulate multiple gene expression mechanisms, such as transcription, translation initiation, and mRNA splicing and processing. Due to their nature, these entities also act on the regulation of gene expression and detection of small metabolites and have the potential to helping in the discovery of new classes of antimicrobial agents. In this review, we describe software and web servers currently available for riboswitch aptamer identification and secondary and tertiary structure prediction, including applications.
Highlights
Fifty years ago, the central dogma of molecular biology proposed a preferential flow of information, stating that DNA is transcribed into RNA, which in turn is translated into proteins with structural or catalytic functions (Crick, 1970; Albert et al, 2011)
The application of energy minimization methods for secondary structure prediction of the riboswitch expression platform domain is still limited as it involves conformational change
Barash and Gabdank (2010) predicted a single point mutation positioned in the nonconserved thiamine pyrophosphate (TPP) riboswitch region responsible for transforming the terminator to an anti-terminator state
Summary
The central dogma of molecular biology proposed a preferential flow of information, stating that DNA is transcribed into RNA, which in turn is translated into proteins with structural or catalytic functions (Crick, 1970; Albert et al, 2011). There are several methods for predicting RNA motifs, such as using an algorithm for predicting the secondary structure and compare the conserved stem-loops (like RiboSW, Chang et al, 2009), searching for riboswitch specific sequence motives followed by the comparison of the secondary structures (riboswitch Finder, Bengert and Dandekar, 2004; RibEx, AbreuGoodger and Merino, 2005; and DRD, Havill et al, 2014) and the usage of probabilistic models such as HMM and CM (HMMER, Mistry et al, 2013; Infernal, Nawrocki and Eddy, 2013b). The software utilizes a set of reliable sequence alignments, along with a common secondary structure annotation (Stockholm format), to create the CM model specific for that target RNA family.
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