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1.  RIBER/DIBER: a software suite for crystal content analysis in the studies of protein–nucleic acid complexes 
Bioinformatics  2012;28(6):880-881.
Summary: Co-crystallization experiments of proteins with nucleic acids do not guarantee that both components are present in the crystal. We have previously developed DIBER to predict crystal content when protein and DNA are present in the crystallization mix. Here, we present RIBER, which should be used when protein and RNA are in the crystallization drop. The combined RIBER/DIBER suite builds on machine learning techniques to make reliable, quantitative predictions of crystal content for non-expert users and high-throughput crystallography.
Availability: The program source code, Linux binaries and a web server are available at RIBER/DIBER requires diffraction data to at least 3.0 Å resolution in MTZ or CIF (web server only) format. The RIBER/DIBER code is subject to the GNU Public License.
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3307108  PMID: 22238259
2.  FILTREST3D: discrimination of structural models using restraints from experimental data 
Bioinformatics  2010;26(23):2986-2987.
Summary: Automatic methods for macromolecular structure prediction (fold recognition, de novo folding and docking programs) produce large sets of alternative models. These large model sets often include many native-like structures, which are often scored as false positives. Such native-like models can be more easily identified based on data from experimental analyses used as structural restraints (e.g. identification of nearby residues by cross-linking, chemical modification, site-directed mutagenesis, deuterium exchange coupled with mass spectrometry, etc.). We present a simple server for scoring and ranking of models according to their agreement with user-defined restraints.
Availability: FILTREST3D is freely available for users as a web server and standalone software at:
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2982159  PMID: 20956242
3.  MetalionRNA: computational predictor of metal-binding sites in RNA structures 
Bioinformatics  2011;28(2):198-205.
Motivation: Metal ions are essential for the folding of RNA molecules into stable tertiary structures and are often involved in the catalytic activity of ribozymes. However, the positions of metal ions in RNA 3D structures are difficult to determine experimentally. This motivated us to develop a computational predictor of metal ion sites for RNA structures.
Results: We developed a statistical potential for predicting positions of metal ions (magnesium, sodium and potassium), based on the analysis of binding sites in experimentally solved RNA structures. The MetalionRNA program is available as a web server that predicts metal ions for RNA structures submitted by the user.
Availability: The MetalionRNA web server is accessible at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3259437  PMID: 22110243

Results 1-3 (3)