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DISULFIND is a server for predicting the disulfide bonding state of cysteines and their disulfide connectivity starting from sequence alone. Optionally, disulfide connectivity can be predicted from sequence and a bonding state assignment given as input. The output is a simple visualization of the assigned bonding state (with confidence degrees) and the most(More)
Cysteines may form covalent bonds, known as disulfide bridges, that have an important role in stabilizing the native conformation of proteins. Several methods have been proposed for predicting the bonding state of cysteines, either using local context or using global protein descriptors. In this paper we introduce an SVM based predictor that operates in two(More)
Accurate predictions of metal-binding sites in proteins by using sequence as the only source of information can significantly help in the prediction of protein structure and function, genome annotation, and in the experimental determination of protein structure. Here, we introduce a method for identifying histidines and cysteines that participate in binding(More)
We propose a method for sequential supervised learning that exploits explicit knowledge of short- and long-range dependencies. The architecture consists of a recursive and bi-directional neural network that takes as input a sequence along with an associated interaction graph. The interaction graph models (partial) knowledge about long-range dependency(More)
BACKGROUND Carbohydrates play a critical role in human diseases and their potential utility as biomarkers for pathological conditions is a major driver for characterization of the glycome. However, the additional complexity of glycans compared to proteins and nucleic acids has slowed the advancement of glycomics in comparison to genomics and proteomics. The(More)
MOTIVATION Several kernel-based methods have been recently introduced for the classification of small molecules. Most available kernels on molecules are based on 2D representations obtained from chemical structures, but far less work has focused so far on the definition of effective kernels that can also exploit 3D information. RESULTS We introduce new(More)
The EUROCarbDB project is a design study for a technical framework, which provides sophisticated, freely accessible, open-source informatics tools and databases to support glycobiology and glycomic research. EUROCarbDB is a relational database containing glycan structures, their biological context and, when available, primary and interpreted analytical data(More)
Predicting the secondary structure of a protein is a main topic in bioinformatics. A reliable predictor is needed by threading methods to improve the prediction of tertiary structure. Moreover, the predicted secondary structure content of a protein can be used to assign the protein to a specific folding class and thus estimate its function. We discuss here(More)
Accuracy of protein secondary structure predictors has been slowly growing during the last decade. Although it is clear that a relatively large fraction of current errors is due to long-range interactions, current predictors are not able to exploit such information. We present a solution based on a generalized bidirectional neural network that learns from(More)
We develop and test a new hierarchical approach for the prediction of protein structure. An algorithm is described to assemble the 3D fold of a protein starting from its secondary structure and β-sheet topology. Reconstruction is carried out by energy minimization of a reduced protein model, where β-partners are derived from appropriate distance constraints(More)