GAG Builder: a web-tool for modeling 3D structures of glycosaminoglycans.

Here we report the launch of a web-tool (the GLYCAM-Web GAG Builder, www.glycam.org/gag) for the rapid and straightforward prediction of 3D structural models for glycosaminoglycans (GAGs). The tool provides the user with coordinate files (PDB format) for use in visualization, as well as files for performing MD simulation with the AMBER software package. Counter ions and water may also be added as desired. The tool is designed with the non-expert in mind, and as such has implemented typical default values for structural parameters, which the user may change if desired. Multiple GAG types are supported, including Heparin/Heparan Sulfate, Chondroitin Sulfate, Dermatan Sulfate, Keratan Sulfate, and Hyaluronan; however, the user may alter the default sulfation patterns to create novel sequences. The common non-natural unsaturated uronic acid (ΔUA) and its sulfated derivative are also supported.

Accurate prediction of RNA nucleotide interactions with backbone k-tree model.

MOTIVATION: Given the importance of non-coding RNAs to cellular regulatory functions, it would be highly desirable to have accurate computational prediction of RNA 3D structure, a task which remains challenging. Even for a short RNA sequence, the space of tertiary conformations is immense; existing methods to identify native-like conformations mostly resort to random sampling of conformations to achieve computational feasibility. However, native conformations may not be examined and prediction accuracy may be compromised due to sampling. State-of-the-art methods have yet to deliver satisfactory predictions for RNAs of length beyond 50 nucleotides. RESULTS: This paper presents a method to tackle a key step in the RNA 3D structure prediction problem, the prediction of the nucleotide interactions that constitute the desired 3D structure. The research is based on a novel graph model, called a backbone k-tree, to tightly constrain the nucleotide interaction relationships considered for RNA 3D structures. It is shown that the new model makes it possible to efficiently predict the optimal set of nucleotide interactions (including the non-canonical interactions in all recently revealed families) from the query sequence along with known or predicted canonical basepairs. The preliminary results indicate that in most cases the new method can predict with a high accuracy the nucleotide interactions that constitute the 3D structure of the query sequence. It thus provides a useful tool for the accurate prediction of RNA 3D structure. AVAILABILITY AND IMPLEMENTATION: The source package for BkTree is available at http://rna-informatics.uga.edu/index.php?f=software&p=BkTree. CONTACT: lding@uga.edu or cai@cs.uga.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TRFolder-W: a web server for telomerase RNA structure prediction in yeast genomes.

UNLABELLED: TRFolder-W is a web server capable of predicting core structures of telomerase RNA (TR) in yeast genomes. TRFolder is a command-line Python toolkit for TR-specific structure prediction. We developed a web-version built on the django web framework, leveraging the work done previously, to include enhancements to increase flexibility of usage. To date, there are five core sub-structures commonly found in TR of fungal species, which are the template region, downstream pseudoknot, boundary element, core-closing stem and triple helix. The aim of TRFolder-W is to use the five core structures as fundamental units to predict potential TR genes for yeast, and to provide a user-friendly interface. Moreover, the application of TRFolder-W can be extended to predict the characteristic structure on species other than fungal species. AVAILABILITY: The web server TRFolder-W is available at http://rna-informatics.uga.edu/?f=software&p=TRFolder-w.