Surveillance of the 2009 H1N1 virus in China shows that the majority of those infected have a mild illness. The typical period during which the virus can be detected with the use of real-time RT-PCR is 6 days (whether or not fever is present). The duration of infection may be shortened if oseltamivir is administered.
Allostery in proteins influences various biological processes such as regulation of gene transcription and activities of enzymes and cell signaling. Computational approaches for analysis of allosteric coupling provide inexpensive opportunities to predict mutations and to design small-molecule agents to control protein function and cellular activity. We develop a computationally efficient network-based method, Ohm, to identify and characterize allosteric communication networks within proteins. Unlike previously developed simulation-based approaches, Ohm relies solely on the structure of the protein of interest. We use Ohm to map allosteric networks in a dataset composed of 20 proteins experimentally identified to be allosterically regulated. Further, the Ohm allostery prediction for the protein CheY correlates well with NMR CHESCA studies. Our webserver, Ohm.dokhlab.org, automatically determines allosteric network architecture and identifies critical coupled residues within this network.
Model evaluation is a necessary step for better prediction and design of 3D RNA structures. For proteins, this has been widely studied and the knowledge-based statistical potential has been proved to be one of effective ways to solve this problem. Currently, a few knowledge-based statistical potentials have also been proposed to evaluate predicted models of RNA tertiary structures. The benchmark tests showed that they can identify the native structures effectively but further improvements are needed to identify near-native structures and those with non-canonical base pairs. Here, we present a novel knowledge-based potential, 3dRNAscore, which combines distance-dependent and dihedral-dependent energies. The benchmarks on different testing datasets all show that 3dRNAscore are more efficient than existing evaluation methods in recognizing native state from a pool of near-native states of RNAs as well as in ranking near-native states of RNA models.
Direct coupling analysis of nucleotide coevolution provides a novel approach to identify which nucleotides in an RNA molecule are likely in direct contact, and this information obtained from sequence only can be used to predict RNA 3D structures with much improved accuracy. Here we present an efficient method that incorporates this information into current RNA 3D structure prediction methods, specifically 3dRNA. Our method makes much more accurate RNA 3D structure prediction than the original 3dRNA as well as other existing prediction methods that used the direct coupling analysis. In particular our method demonstrates a significant improvement in predicting multi-branch junction conformations, a major bottleneck for RNA 3D structure prediction. We also show that our method can be used to optimize the predictions by other methods. These results indicate that optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide–nucleotide interactions from direct coupling analysis offers an efficient way for accurate RNA tertiary structure predictions.
Nucleic acid–based assemblies that interact with each other and further communicate with the cellular machinery in a controlled manner represent a new class of reconfigurable materials that can overcome limitations of traditional biochemical approaches and improve the potential therapeutic utility of nucleic acids. This notion enables the development of novel biocompatible ‘smart’ devices and biosensors with precisely controlled physicochemical and biological properties. We extend this novel concept by designing RNA–DNA fibers and polygons that are able to cooperate in different human cell lines and that have defined immunostimulatory properties confirmed by ex vivo experiments. The mutual intracellular interaction of constructs results in the release of a large number of different siRNAs while giving a fluorescent response and activating NF-κB decoy DNA oligonucleotides. This work expands the possibilities of nucleic acid technologies by (i) introducing very simple design principles and assembly protocols; (ii) potentially allowing for a simultaneous release of various siRNAs together with functional DNA sequences and (iii) providing controlled rates of reassociation, stabilities in human blood serum, and immunorecognition.
Higher-order
structure governs function for many RNAs. However,
discerning this structure for large RNA molecules in solution is an
unresolved challenge. Here, we present SHAPE-JuMP (selective 2′-hydroxyl
acylation analyzed by primer extension and juxtaposed merged pairs)
to interrogate through-space RNA tertiary interactions. A bifunctional
small molecule is used to chemically link proximal nucleotides in
an RNA structure. The RNA cross-link site is then encoded into complementary
DNA (cDNA) in a single, direct step using an engineered reverse transcriptase
that “jumps” across cross-linked nucleotides. The resulting
cDNAs contain a deletion relative to the native RNA sequence, which
can be detected by sequencing, that indicates the sites of cross-linked
nucleotides. SHAPE-JuMP measures RNA tertiary structure proximity
concisely across large RNA molecules at nanometer resolution. SHAPE-JuMP
is especially effective at measuring interactions in multihelix junctions
and loop-to-helix packing, enables modeling of the global fold for
RNAs up to several hundred nucleotides in length, facilitates ranking
of structural models by consistency with through-space restraints,
and is poised to enable solution-phase structural interrogation and
modeling of complex RNAs.
Molecular docking is the key ingredient of virtual drug screening, a promising and costeffective approach for finding new drugs. A critical limitation of this approach is the inadequate sampling efficiency of both ligand and/or receptor conformations for finding the lowest energy bound state. To circumvent this limitation, we develop a protein-ligand docking methodology capable of incorporating structural constraints, experimentally derived or theoretically predicted, to improve accuracy and efficiency. We develop a web server with a user-friendly online graphical interface as a platform for accurate and efficient protein-ligand molecule docking.
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