In vitro selection of conformational probes for riboswitches

Survey of the 2009 commercial optical biosensor literature

We took a different approach to reviewing the commercial biosensor literature this year by inviting 22 biosensor users to serve as a review committee. They set the criteria for what to expect in a publication and ultimately decided to use a pass/fail system for selecting which papers to include in this year's reference list. Of the 1514 publications in 2009 that reported using commercially available optical biosensor technology, only 20% passed their cutoff. The most common criticism the reviewers had with the literature was that "the biosensor experiments could have been done better." They selected 10 papers to highlight good experimental technique, data presentation, and unique applications of the technology. This communal review process was educational for everyone involved and one we will not soon forget.

Dynamic energy landscapes of riboswitches help interpret conformational rearrangements and function

Riboswitches are RNAs that modulate gene expression by ligand-induced conformational changes. However, the way in which sequence dictates alternative folding pathways of gene regulation remains unclear. In this study, we compute energy landscapes, which describe the accessible secondary structures for a range of sequence lengths, to analyze the transcriptional process as a given sequence elongates to full length. In line with experimental evidence, we find that most riboswitch landscapes can be characterized by three broad classes as a function of sequence length in terms of the distribution and barrier type of the conformational clusters: low-barrier landscape with an ensemble of different conformations in equilibrium before encountering a substrate; barrier-free landscape in which a direct, dominant “downhill” pathway to the minimum free energy structure is apparent; and a barrier-dominated landscape with two isolated conformational states, each associated with a different biological function. Sharing concepts with the “new view” of protein folding energy landscapes, we term the three sequence ranges above as the sensing, downhill folding, and functional windows, respectively. We find that these energy landscape patterns are conserved in various riboswitch classes, though the order of the windows may vary. In fact, the order of the three windows suggests either kinetic or thermodynamic control of ligand binding. These findings help understand riboswitch structure/function relationships and open new avenues to riboswitch design.

Riboswitches are RNAs that modulate gene expression by ligand-induced conformational changes. However, the way that sequence dictates alternative folding pathways of gene regulation remains unclear. In this study, we mimic transcription by computing energy landscapes which describe accessible secondary structures for a range of sequence lengths. Consistent with experimental evidence, we find that most riboswitch landscapes can be characterized by three broad classes as a function of sequence length in terms of the distribution and barrier type of the conformational clusters: Low-barrier landscape with an ensemble of conformations in equilibrium before encountering a substrate; barrier-free landscape with a dominant “downhill” pathway to the minimum free energy structure; and barrier-dominated landscape with two isolated conformational states with different functions. Sharing concepts with the “new view” of protein folding energy landscapes, we term the three sequence ranges above as the sensing, downhill folding, and functional windows, respectively. We find that these energy landscape patterns are conserved between riboswitch classes, though the order of the windows may vary. In fact, the order of the three windows suggests either kinetic or thermodynamic control of ligand binding. These findings help understand riboswitch structure/function relationships and open new avenues to riboswitch design.

Biomolecular engineering of intracellular switches in eukaryotes

Tools to selectively and reversibly control gene expression are useful to study and model cellular functions. When optimized, these cellular switches can turn a protein's function "on" and "off" based on cues designated by the researcher. These cues include small molecules, drugs, hormones, and even temperature variations. Here we review three distinct areas in gene expression that are commonly targeted when designing cellular switches. Transcriptional switches target gene expression at the level of mRNA polymerization, with examples including the tetracycline gene induction system as well as nuclear receptors. Translational switches target the process of turning the mRNA signal into protein, with examples including riboswitches and RNA interference. Post-translational switches control how proteins interact with one another to attenuate or relay signals. Examples of post-translational modification include dimerization and intein splicing. In general, the delay times between switch and effect decreases from transcription to translation to post-translation; furthermore, the fastest switches may offer the most elegant opportunities to influence and study cell behavior. We discuss the pros and cons of these strategies, which directly influence their usefulness to study and implement drug targeting at the tissue and cellular level.