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Predicts iron-sulfur proteins from protein sequence(s). MetalPredator integrates an existing domain-based approach (Andreini et al., 2011) with a new one designed to search for metal-binding motifs found in proteins with known structure. MetalPredator uniquely combines global and local searches to define whether a protein is a potential metalloprotein. MetalPredator can process the entire proteome of any organism in minutes to a few hours (e.g. for the human proteome), and thus can be applied to any newly sequenced organism, including eukaryotes.


Predicts 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. The basic assumption of this method is that the free energy associated with a given molecular interaction is strictly correlated with the relative frequency by which this interaction occurs among known structures. MetalionRNA can be used to assist crystal structure determination e.g. by identifying tentative metal ion sites to be further validated by comparison with experimental data or to propose metal positions for structural models that lack coordinates of cations, e.g. RNA structures determined by nuclear magnetic resonance (NMR) spectroscopy or theoretical models. MetalionRNA is freely available as a web server.


Discriminates different types of metal-binding sites effectively based on 3D structure data and is useful for accurate metal-binding site prediction. mFASD captures the characteristics of a metal-binding site by investigating the local chemical environment of a set of functional atoms that are considered to be in contact with the bound metal. Then a distance measure defined on functional atom sets enables the comparison between different metal-binding sites. The algorithm could discriminate most types of metal-binding sites from each other with high sensitivity and accuracy.

TEMSP / 3D TEmplate-based Metal Site Prediction

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A structure-based method to predict zinc-binding sites. TEMSP significantly improves over previously reported best methods in predicting as many as possible true ligand residues for zinc with minimum overpredictions: if only those results in which all zinc ligand residues have been correctly predicted are defined as true positives, this method improves sensitivity from less than 30% to above 60%, and selectivity from around 25% to 80%.


Predicts 3D intra-chain protein binding sites for transition metals (Zn, Fe, Mn, Cu, Ni, Co, and Ca and Mg sites that can be replaced by a transition metal). The algorithm searches for a triad of amino acids composed of 4 residue types (Cys, His, Glu, Asp) having ligand atoms within specific distances. It allows one target residue to rotate in rotamer space, taking into account structural rearrangements that may occur upon metal binding. A binding site is considered to be true if one or more correct amino acid ligands have been predicted.


Enables the user to analyze a translated gene sequence for soft (Zn, Fe, Ni, Cu, Co, Mn), and promiscuous hard (Mg, Ca), metal-ion binding sites. The application checks for homology of your target sequence to PDB template sequences and then models the target side chains in 3D (using SCCOMP) on the backbone of the selected template. A metal binding prediction algorithm (based on the CHED procedure) is then applied to the 3D model to identify any putative binding sites and their ligating CHED (Cys, His, Glu, Asp) residues.