Riboswitch Detection Using Profile Hidden Markov Models

Payal Singh,Supratim Sengupta,Sudha Bhattacharya,Pradipta Bandyopadhyay,A Krishnamachari

doi:10.1186/1471-2105-10-325

Abstract

BackgroundRiboswitches are a type of noncoding RNA that regulate gene expression by switching from one structural conformation to another on ligand binding. The various classes of riboswitches discovered so far are differentiated by the ligand, which on binding induces a conformational switch. Every class of riboswitch is characterized by an aptamer domain, which provides the site for ligand binding, and an expression platform that undergoes conformational change on ligand binding. The sequence and structure of the aptamer domain is highly conserved in riboswitches belonging to the same class. We propose a method for fast and accurate identification of riboswitches using profile Hidden Markov Models (pHMM). Our method exploits the high degree of sequence conservation that characterizes the aptamer domain.ResultsOur method can detect riboswitches in genomic databases rapidly and accurately. Its sensitivity is comparable to the method based on the Covariance Model (CM). For six out of ten riboswitch classes, our method detects more than 99.5% of the candidates identified by the much slower CM method while being several hundred times faster. For three riboswitch classes, our method detects 97-99% of the candidates relative to the CM method. Our method works very well for those classes of riboswitches that are characterized by distinct and conserved sequence motifs.ConclusionRiboswitches play a crucial role in controlling the expression of several prokaryotic genes involved in metabolism and transport processes. As more and more new classes of riboswitches are being discovered, it is important to understand the patterns of their intra and inter genomic distribution. Understanding such patterns will enable us to better understand the evolutionary history of these genetic regulatory elements. However, a complete picture of the distribution pattern of riboswitches will emerge only after accurate identification of riboswitches across genomes. We believe that the riboswitch detection method developed in this paper will aid in that process. The significant advantage in terms of speed, of our pHMM-based approach over the method based on CM allows us to scan entire databases (rather than 5'UTRs only) in a relatively short period of time in order to accurately identify riboswitch candidates.

Highlights

Riboswitches are a type of noncoding RNA that regulate gene expression by switching from one structural conformation to another on ligand binding
We find that our profile Hidden Markov Models (pHMM)-based method is able to detect riboswitches belonging to eight of the ten families with high sensitivity and specificity while being more than a hundred times faster than the Covariance Model (CM)
A profile Hidden Markov Models (HMMs) [20,24] is an HMM with a structure that allows insertions and deletions in the model, and models gaps in a position dependent manner to give position sensitive gap scores. pHMMs can be constructed from a set of sequences belonging to a family and can be used for selective and sensitive database search for finding other members of that family

Summary

Introduction

Riboswitches are a type of noncoding RNA that regulate gene expression by switching from one structural conformation to another on ligand binding. They are complex folded structures that act as high affinity receptors for specific cellular metabolites [79] On metabolite binding they undergo conformational change, which modulates gene expression at post-transcriptional level, either through premature termination of transcription [10] or inhibition of translation initiation [11]. They are composed of two structural domains: an aptamer domain [12] and an expression platform [13]. Riboswitches regulate genes in several metabolic pathways involved in the biosynthesis of vitamins, amino acids and purines [14,15]

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