Bioinformatics Vol. 15 no. 11 1999
Pages 937-946
© 1999 Oxford University Press
Exploiting the past and the future in protein secondary structure prediction
1 Department of Information and Computer
Science, and Department of Biological Chemistry, College of
Medicine, University of California, Irvine, Irvine, CA 92697-3425,
USA
2 Center for Biological Sequence Analysis,
The Technical University of Denmark, DK-2800 Lyngby, Denmark
3 Department of Informatics and Systems,
University of Florence, 50139 Florence, Italy
4 Department of Information and Computer
Science, University of California, Irvine, Irvine, CA 92697-3425,
USA
Pierre Baldi
Motivation: Predicting the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three-dimensional structure, as well as its function. Presently, the best predictors are based on machine learning approaches, in particular neural network architectures with a fixed, and relatively short, input window of amino acids, centered at the prediction site. Although a fixed small window avoids overfitting problems, it does not permit capturing variable long-rang information.
Results: We introduce a family of novel architectures which can learn to make predictions based on variable ranges of dependencies. These architectures extend recurrent neural networks, introducing non-causal bidirectional dynamics to capture both upstream and downstream information. The prediction algorithm is completed by the use of mixtures of estimators that leverage evolutionary information, expressed in terms of multiple alignments, both at the input and output levels. While our system currently achieves an overall performance close to 76% correct prediction – at least comparable to the best existing systems – the main emphasis here is on the development of new algorithmic ideas.
Availability: The executable program for predicting protein secondary structure is available from the authors free of charge.
Contact: pfbaldi{at}ics.uci.edu, gpollast{at}ics.uci.edu, brunak{at}cbs.dtu.dk, paolo{at}dsi.unifi.it
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