The GeneSilico MetaServer is a gateway to a number of third-party methods for protein structure prediction (identification of domains, secondary structure prediction, fold-recognition, and finally, 3D model generation). Users can submit a protein sequence (or alignment) by a single click, then analyze the summary of results generated by many methods, and finally predict the protein structure according to the "consensus" approach.
This tool builds protein models by recombining fragments of Fold-Recognition alignments. Users can select a set of alignments from the output of the MetaServer or provide their own alignments. The program will generate alternative models, identify fragments that either exhibit consensus conformation in different models or - in the absence of consensus - show the relatively best compatibility of amino acids with the environment of the rest of the model. These fragments will be recombined to create a hybrid model, which will be further optimized by trying alternative alignments in non-consensus regions to improve the sequence-structure compatibility.
This simple server facilitates visual presentation of three-dimensional (3D) protein structures. It is a gateway to a number of methods for evaluation of protein structure (ANOLEA, PROSAII, PROVE or VERIFY3D), but also identifies buried residues and depicts sequence conservation. As an input, it takes a PDB file and, optionally, a multiple sequence alignment. As an output, the server returns a file in which the B-factor column is replaced with values of the chosen parameter (structure quality, residue burial or conservation). These values can be visualized as colors using structure viewers such as RASMOL or SWISS PDB VIEWER.
This is an "alpha" version of a server for discrimination of a large number of alternative models of protein structure against a set of restraints derived from low-resolution experimental analyses (such as cross-linking, mutagenesis, circular dichrosm etc.) as well as from computational predictions (e.g. solvent accessibility, amino acid contact maps).
While most phylogenetic methods calculate trees based on sequence alignments, this server (STRUcture CLAssification) allows to use protein structures. The prefereed input is an alignment of protein coordinates exported from SWISS PDB VIEWER. The user specifies the distance cutoff and selects which measures should be used to calculate the "evolutionary distances" between the protein structures. The server returns series of unrooted trees in the NEXUS format and corresponding distance matrices, as well as a consensus tree. This method is most useful in the 'twilight zone of homology', where amino acid sequences are too diverged to provide reliable relationships.
The CompaRNA web server provides a continuous benchmark of automated standalone and web server methods for RNA secondary structure prediction. It has been inspired by the EVA and Livebench servers for benchmarking of protein structure prediction tools, which have greatly contributed to progress in the field of structural bioinformatics. The aim of CompaRNA is to assess the state of the art in RNA structure prediction, provide a detailed picture of what is possible with the available tools, where progress is being made and what major problems remain. The CompaRNA server is a valuable resource for all researchers who focus their attention on the usage and development of RNA structure prediction methods.
ModeRNA server is an online tool for RNA 3D structure modeling by the comparative approach, based on a template RNA structure and a user-defined target-template sequence alignment. It offers an option to search for potential templates, given the target sequence. The server also provides tools for analyzing, editing and formatting of RNA structure files. It facilitates the use of the ModeRNA software and offers new options in comparison to the standalone program.
MetalionRNA is a web server for the prediction of metal ions (magnesium, sodium, and potassium) in RNA 3D structures, based on a statistical potential inferred from the analysis of binding sites observed in experimentally solved RNA structures. The server is also capable of predicting Mg2+-binding sites for DNA structures.
The MetaGramLocN is a method for subcellular localization prediction of Gram-negative proteins. The MetaGramLocN is a gateway to a number of primary prediction methods (various types: signal peptide, beta-barrel, transmembrane helices and subcellular localization predictors). The MetaGramLocN integrates the primary methods and based on their outputs provides overall consenus prediction. To make a prediction for your protein sequence use Submit or SOAP client In our benchmark, the MetaLocGramN performed better in comparison to other SCL predictive methods, since the average Matthews correlation coefficient reached 0.806 that enhanced the predictive capability by 12% (compared to PSORTb3).
Co-crystallization experiments of proteins with nucleic acids do not guarantee that both components are present in the crystal. While working as a PhD student with Matthias Bochtler, Grzegorz Chojnowski developed DIBER - a method with which to predict crystal content (DNA? protein? or both?) from the diffraction data. Now, we have together developed 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. RIBER/DIBER requires diffraction data to at least 3.0 Å resolution in MTZ or CIF format.
MinkoFit3D is a method for fitting macromolecular assemblies or their components into electron density maps. Our approach is based on finding “tight passages” inferred from the Minkowski sum boundary of two polyhedral surfaces of the structure and its map. Following the initial fit, either a robust brute-force or a genetic algorithm is used to build an initial assembly, and multi-body refinement is applied in direct electron density space.
GDFuzz3D is a method for protein tertiary structure retrieval from a contact map. It can handle binary (i.e. native) contact maps but the algorithm is designed to have predicted contact maps on input. GDFuzz3D makes use of a new non Euclidean distance function as explained in the publication. Then it uses Multi-Dimensional Scaling algorithm to produce a crude 3D model. Subsequently, other bioinformatics programs are used in order to refine the model, i.e. Modeller and Refiner. The results are sent by email as a PDB file.