Target Complementarity Research Articles

MiRU: an automated plant miRNA target prediction server

MicroRNAs (miRNAs) play important roles in gene expression regulation in animals and plants. Since plant miRNAs recognize their target mRNAs by near-perfect base pairing, computational sequence similarity search can be used to identify potential targets. A web-based integrated computing system, miRU, has been developed for plant miRNA target gene prediction in any plant, if a large number of sequences are available. Given a mature miRNA sequence from a plant species, the system thoroughly searches for potential complementary target sites with mismatches tolerable in miRNA–target recognition. True or false positives are estimated based on the number and type of mismatches in the target site, and on the evolutionary conservation of target complementarity in another genome which can be selected according to miRNA conservation. The output for predicted targets, ordered by mismatch scores, includes complementary sequences with mismatches highlighted in colors, original gene sequences and associated functional annotations. The miRU web server is available at .

A statistical approach predicts human microRNA targets

MicroRNAs are approximately 18-24 nt. noncoding RNAs found in all eukaryotes that degrade messenger RNAs via RNA interference (if they bind in a perfect or near-perfect complementarity to the target mRNA), or arrest translation (if the binding is imperfect). Several microRNA targets have been identified in lower organisms, but no mammalian microRNA targets have yet been validated experimentally. We carried out a population-wide statistical analysis of how human microRNAs interact complementarily with human mRNAs present in the RefSeq database, looking for characteristics that differ significantly as compared with scrambled control sequences. These characteristics were used to predict a list of 72 candidate mRNA targets with 81% confidence. Unlike the case in C. elegans and Drosophila, many human microRNAs exhibited long exact matches (10 or more bases in a row), up to and including perfect target complementarity. Human microRNAs hit putative mRNA targets within the protein coding region about 2/3 of the time. And, microRNA hits in the candidate list did not have better complementarity near their 5'-end than expected by chance. In several cases, an individual microRNA hit multiple mRNAs that belonged to the same functional class. The candidate list predicts a significant number of well-known and novel human genes that warrant experimental validation as mRNA targets, including several that may be regulated by RNA interference. The list also provides a training set and suggests an unified model to assist prediction of mRNA targets that do not have especially long regions of target complementarity.

Interaction between hammerhead ribozyme and RNA substrates measured by a surface plasmon resonance biosensor

Dynamic interactions between hammerhead ribozymes and RNA substrates were measured using the surface plasmon resonance (SPR) technology. Two in vitro transcribed substrates (non-cleavable and cleavable) were immobilised on streptavidin-coated dextran matrices and subsequently challenged with non-related yeast tRNA or two hammerhead ribozymes, both of which had previously been shown to exhibit functional binding and cleavage of complementary target RNAs. The target-binding domain of one of the ribozymes was fully complementary to a 16-ribonucleotide stretch on the immobilised substrates, while the other ribozyme had a nine-ribonucleotide complementarity. The two ribozymes could readily be differentiated with regard to affinity. Cleavage could be measured, using the ribozyme with full target complementarity to the cleavable substrate. In contrast, the ribozyme with lower affinity lacked cleavage activity. We suggest that SPR will be useful for investigations of ribozyme-substrate interactions.

Quantized Surface Complementarity Diversity (QSCD): A Model Based on Small Molecule−Target Complementarity

A model of molecular diversity is presented. The model, termed "Quantized Surface Complementarity Diversity" (QSCD), defines molecular diversity by measuring molecular complementarity to a fully enumerated set of theoretical target surfaces. Molecular diversity space is defined as the molecular complement to this set of enumerated surfaces. Using a set of known test compounds, the model is shown to be biologically relevant, consistently scoring known actives as similar. At the resolution of the model, which examines molecules "quantized" into 4.24 A cubic units and treats four points of specific energetic complementarity, the minimum number of compounds needed to fully cover molecular diversity space up to volume 1070 cubic A is estimated to be on the order of 24 million molecules. Most importantly, QSCD allows for individual points in diversity space to be filled by direct modeling of molecular libraries into detailed 3D templates of shape and functionality.

Prototropic molecular forms and theoretical descriptors in QSAR analysis

Computational chemistry allows us to define and compute ad hoc theoretical reactivity and size—shape descriptors on the different prototropic forms assumed by drugs in pharmacological test solutions. These are essential elements for obtaining simple, consistent, comparable and easily interpretable theoretical QSAR models based on the ligand similarity—target complementarity paradigm. In this context, theoretical QSAR models have been obtained for 34 structurally and pharmacologically (antagonists, partial agonists and full agonists) heterogeneous M 1-muscarinic ligands and the above concepts have been highlighted.