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Bioinformatics Advance Access originally published online on December 7, 2004
Bioinformatics 2005 21(8):1719-1720; doi:10.1093/bioinformatics/bti203
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© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Porter: a new, accurate server for protein secondary structure prediction

Gianluca Pollastri 1,* and Aoife McLysaght 2

1Computer Science Department, University College Dublin Belfield, Dublin 4, Ireland
2Genetics Department, Trinity College Dublin Dublin 2, Ireland

*To whom correspondence should be addressed.

Summary: Porter is a new system for protein secondary structure prediction in three classes. Porter relies on bidirectional recurrent neural networks with shortcut connections, accurate coding of input profiles obtained from multiple sequence alignments, second stage filtering by recurrent neural networks, incorporation of long range information and large-scale ensembles of predictors. Porter's accuracy, tested by rigorous 5-fold cross-validation on a large set of proteins, exceeds 79%, significantly above a copy of the state-of-the-art SSpro server, better than any system published to date.

Availability: Porter is available as a public web server at http://distill.ucd.ie/porter/

Contact: gianluca.pollastri{at}ucd.ie


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