Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins

Background: 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, up-to-date, nonredundant subsets of the Protein Data Bank. Together with structural feature predictions, Distill includes a server for prediction of C traces for short proteins (up to 200 amino acids). Results: The servers are state-of-the-art, with secondary structure predicted correctly for nearly 80% of residues (currently the top performance on EVA), 2-class solvent accessibility nearly 80% correct, and contact maps exceeding 50% precision on the top non-diagonal contacts. A preliminary implementation of the predictor of protein C traces featured among the top 20 Novel Fold predictors at the last CASP6 experiment as g...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Davide Baù, Alberto J. M. Martin, Catherine Mooney, Alessandro Vullo, Ian Walsh, Gianluca Pollastri
• Keywords: BMCBI 2006, Prediction, Protein, Structural Features