Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins
2006
Walsh Ian | Vullo Alessandro | Mooney Catherine | Martin Alberto JM | Baú Davide | Pollastri Gianluca
Abstract 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, non-redundant subsets of the Protein Data Bank. Together with structural feature predictions, Distill includes a server for prediction of C<sub>α </sub>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<sub>α </sub>traces featured among the top 20 Novel Fold predictors at the last CASP6 experiment as group Distill (ID 0348). The majority of the servers, including the C<sub>α </sub>trace predictor, now take into account homology information from the PDB, when available, resulting in greatly improved reliability. Conclusion All predictions are freely available through a simple joint web interface and the results are returned by email. In a single submission the user can send protein sequences for a total of up to 32k residues to all or a selection of the servers. Distill is accessible at the address: <url>http://distill.ucd.ie/distill/</url>.
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