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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)
MOTIVATION We focus on the prediction of disulfide bridges in proteins starting from their amino acid sequence and from the knowledge of the disulfide bonding state of each cysteine. The location of disulfide bridges is a structural feature that conveys important information about the protein main chain conformation and can therefore help towards the(More)
Structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. Structural properties are also routinely used in protein analysis even when homology is available, largely because(More)
The formation of disulphide bridges among cysteines is an important feature of protein structures. Here we develop new methods for the prediction of disulphide bond connectivity. We first build a large curated data set of proteins containing disulphide bridges and then use 2-Dimensional Recursive Neural Networks to predict bonding probabilities between(More)
Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvements have occurred over the last years, the problem of accurately predicting residue contact maps from primary sequences is still largely unsolved. Among the reasons for this are the unbalanced(More)
Predicting the disulfide bonding state of each cysteine is a step towards location of disul-phide bridges in proteins. The solution proposed here is a refinement of a recently introduced method based on a two-stage combination of multiple classifiers. In our approach, the first stage operates at the protein level and attempts to predict whether all, none,(More)
Cysteines may form covalent bonds, known as disul-fide 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(More)
CSpritz is a web server for the prediction of intrinsic protein disorder. It is a combination of previous Spritz with two novel orthogonal systems developed by our group (Punch and ESpritz). Punch is based on sequence and structural templates trained with support vector machines. ESpritz is an efficient single sequence method based on bidirectional(More)
We describe Distill, a suite of servers for the prediction of protein structural features: secondary structure; relative solvent accessibility; contact density; backbone structural motifs; residue contact maps at 6, 8 and 12 Angstrom; coarse protein topology. The servers are based on large-scale ensembles of recursive neural networks and trained on large,(More)
Intrinsically disordered proteins have long stretches of their polypeptide chain, which do not adopt a single native structure composed of stable secondary and tertiary structure in the absence of binding partners. The prediction of intrinsically disordered regions in proteins from sequence is increasingly becoming of interest, as the presence of many such(More)