Visualizing spatially correlated dynamics that directs RNA conformational transitions

RNAs fold into three- dimensional ( 3D) structures that subsequently undergo large, functionally important, conformational transitions in response to a variety of cellular signals(1-3). RNA structures are believed to encode spatially tuned flexibility that can direct transitions along specific conformational pathways(4,5). However, this hypothesis has proved difficult to examine directly because atomic movements in complex biomolecules cannot be visualized in 3D by using current experimental methods. Here we report the successful implementation of a strategy using NMR that has allowed us to visualize, with complete 3D rotational sensitivity, the dynamics between two RNA helices that are linked by a functionally important trinucleotide bulge over timescales extending up to milliseconds. The key to our approach is to anchor NMR frames of reference onto each helix and thereby directly measure their dynamics, one relative to the other, using 'relativistic' sets of residual dipolar couplings ( RDCs)(6,7). Using this approach, we uncovered super- large amplitude helix motions that trace out a surprisingly structured and spatially correlated 3D dynamic trajectory. The two helices twist around their individual axes by approximately 536 and 1106 in a highly correlated manner ( R = 0.97) while simultaneously ( R = 0.81 - 0.92) bending by about 94 degrees. Remarkably, the 3D dynamic trajectory is dotted at various positions by seven distinct ligand- bound conformations of the RNA. Thus even partly unstructured RNAs can undergo structured dynamics that directs ligand- induced transitions along specific predefined conformational pathways.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62506/1/nature06389.pd

Development of eco-friendly wall insulation layer utilising the wastes of the packing industry

Efficient thermal insulation materials considerably lower power consumption for heating and cooling of buildings, which in turn minimises CO2 emissions and improves indoor comfort conditions. However, the selection of suitable insulation materials is governed by several factors, such as the environmental impact, health impact, cost and durability. Additionally, the disposal of used insulation materials is a major factor that affects the selection of materials because some materials could be very toxic for humans and the environment, such as asbestos-containing materials. Therefore, there is a continuous research effort, in both industry and academia, to develop sustainable and affordable insulation materials. In this context, this work aims at utilising the packing industry wastes (cardboard) to develop an eco-friendly insulation layer, which is a biodegradable material that can be disposed of safely after use. Experimentally, wasted cardboard was collected, cleaned, and soaked in water for 24 h. Then, the wet cardboard was minced and converted into past papers, then cast in square moulds and left in a ventilated oven at 75 °C to dry before de-moulding them. The produced layers were subjected to a wide range of tests, including thermal conductivity, acoustic insulation, infrared imaging and bending resistance. The obtained results showed the developed material has a good thermal and acoustic insulation performance. Thermally, the developed material had the lowest thermal conductivity (λ) (0.039 W/m.K) compared to the studied traditional materials. Additionally, it successfully decreased the noise level from 80 to about 58 dB, which was better than the efficiency of the commercial polyisocyanurate layer. However, the bending strength of the developed material was a major drawback because the material did not resist more than 0.6 MPa compared to 2.0 MPa for the commercial polyisocyanurate and 70.0 MPa for the wood boards. Therefore, it is recommended to investigate the possibility of strengthening the new material by adding fibres or cementitious materials

Design Principles for Ligand-Sensing, Conformation-Switching Ribozymes

Nucleic acid sensor elements are proving increasingly useful in biotechnology and biomedical applications. A number of ligand-sensing, conformational-switching ribozymes (also known as allosteric ribozymes or aptazymes) have been generated by some combination of directed evolution or rational design. Such sensor elements typically fuse a molecular recognition domain (aptamer) with a catalytic signal generator (ribozyme). Although the rational design of aptazymes has begun to be explored, the relationships between the thermodynamics of aptazyme conformational changes and aptazyme performance in vitro and in vivo have not been examined in a quantitative framework. We have therefore developed a quantitative and predictive model for aptazymes as biosensors in vitro and as riboswitches in vivo. In the process, we have identified key relationships (or dimensionless parameters) that dictate aptazyme performance, and in consequence, established equations for precisely engineering aptazyme function. In particular, our analysis quantifies the intrinsic trade-off between ligand sensitivity and the dynamic range of activity. We were also able to determine how in vivo parameters, such as mRNA degradation rates, impact the design and function of aptazymes when used as riboswitches. Using this theoretical framework we were able to achieve quantitative agreement between our models and published data. In consequence, we are able to suggest experimental guidelines for quantitatively predicting the performance of aptazyme-based riboswitches. By identifying factors that limit the performance of previously published systems we were able to generate immediately testable hypotheses for their improvement. The robust theoretical framework and identified optimization parameters should now enable the precision design of aptazymes for biotechnological and clinical applications

Design principles for riboswitch function

Scientific and technological advances that enable the tuning of integrated regulatory components to match network and system requirements are critical to reliably control the function of biological systems. RNA provides a promising building block for the construction of tunable regulatory components based on its rich regulatory capacity and our current understanding of the sequence–function relationship. One prominent example of RNA-based regulatory components is riboswitches, genetic elements that mediate ligand control of gene expression through diverse regulatory mechanisms. While characterization of natural and synthetic riboswitches has revealed that riboswitch function can be modulated through sequence alteration, no quantitative frameworks exist to investigate or guide riboswitch tuning. Here, we combined mathematical modeling and experimental approaches to investigate the relationship between riboswitch function and performance. Model results demonstrated that the competition between reversible and irreversible rate constants dictates performance for different regulatory mechanisms. We also found that practical system restrictions, such as an upper limit on ligand concentration, can significantly alter the requirements for riboswitch performance, necessitating alternative tuning strategies. Previous experimental data for natural and synthetic riboswitches as well as experiments conducted in this work support model predictions. From our results, we developed a set of general design principles for synthetic riboswitches. Our results also provide a foundation from which to investigate how natural riboswitches are tuned to meet systems-level regulatory demands

Fluorescent RNA cytosine analogue - an internal probe for detailed structure and dynamics investigations

The bright fluorescent cytosine analogue tCO stands out among fluorescent bases due to its virtually unquenched fluorescence emission in duplex DNA. However, like most reported base analogues, it has not been thoroughly characterized in RNA. We here report on the first synthesis and RNA-incorporation of tCO, and characterize its base-mimicking and fluorescence properties in RNA. As in DNA, we find a high quantum yield inside RNA duplexes (<?F> = 0.22) that is virtually unaffected by the neighbouring bases (?F = 0.20-0.25), resulting in an average brightness of 1900 M-1 cm-1. The average fluorescence lifetime in RNA duplexes is 4.3 ns and generally two lifetimes are required to fit the exponential decays. Fluorescence properties in ssRNA are defined by a small increase in average quantum yield (<?F > = 0.24) compared to dsRNA, with a broader distribution (?F = 0.17-0.34) and slightly shorter average lifetimes. Using circular dichroism, we find that the tCO-modified RNA duplexes form regular A-form helices and in UV-melting experiments the stability of the duplexes is only slightly higher than that of the corresponding natural RNA (<?T m> = + 2.3 °C). These properties make tCO a highly interesting fluorescent RNA base analogue for detailed FRET-based structural measurements, as a bright internal label in microscopy, and for fluorescence anisotropy measurements of RNA dynamics

Biomass production of site selective 13C/15N nucleotides using wild type and a transketolase E. coli mutant for labeling RNA for high resolution NMR

Characterization of the structure and dynamics of nucleic acids by NMR benefits significantly from position specifically labeled nucleotides. Here an E. coli strain deficient in the transketolase gene (tktA) and grown on glucose that is labeled at different carbon sites is shown to facilitate cost-effective and large scale production of useful nucleotides. These nucleotides are site specifically labeled in C1′ and C5′ with minimal scrambling within the ribose ring. To demonstrate the utility of this labeling approach, the new site-specific labeled and the uniformly labeled nucleotides were used to synthesize a 36-nt RNA containing the catalytically essential domain 5 (D5) of the brown algae group II intron self-splicing ribozyme. The D5 RNA was used in binding and relaxation studies probed by NMR spectroscopy. Key nucleotides in the D5 RNA that are implicated in binding Mg2+ ions are well resolved. As a result, spectra obtained using selectively labeled nucleotides have higher signal-to-noise ratio compared to those obtained using uniformly labeled nucleotides. Thus, compared to the uniformly 13C/15N-labeled nucleotides, these specifically labeled nucleotides eliminate the extensive 13C–13C coupling within the nitrogenous base and ribose ring, give rise to less crowded and more resolved NMR spectra, and accurate relaxation rates without the need for constant-time or band-selective decoupled NMR experiments. These position selective labeled nucleotides should, therefore, find wide use in NMR analysis of biologically interesting RNA molecules

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues