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Distill : a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins
Date Issued
2006-09-05
Date Available
2012-01-20T14:59:36Z
Abstract
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 Cα traces for short proteins (up to 200 amino acids). 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 Cα 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 Cα trace predictor, now take into account homology information from the PDB, when available, resulting in greatly improved reliability. 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: http://distill.ucd.ie/distill/.
Sponsorship
Science Foundation Ireland
Irish Research Council for Science, Engineering and Technology
Health Research Board
Other funder
Other Sponsorship
UCD President's Award 2004
Type of Material
Journal Article
Publisher
BioMed Central
Journal
BMC Bioinformatics
Volume
7
Issue
September 2006
Start Page
402
Copyright (Published Version)
2006 Baú et al; licensee BioMed Central Ltd
Subject – LCSH
Proteins--Analysis
Neural networks (Computer science)
Web versions
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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