Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles

Secondary structure predictions are increasingly becoming the workhorse for several methods aiming at predicting protein structure and function. Here we use ensembles of bidirectional recurrent neural network architectures, PSI‐BLAST‐derived profiles, and a large nonredundant training set to derive...

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Published in:Proteins, structure, function, and bioinformatics structure, function, and bioinformatics, 2002-05, Vol.47 (2), p.228-235
Main Authors: Pollastri, Gianluca, Przybylski, Darisz, Rost, Burkhard, Baldi, Pierre
Format: Article
Language:English
Subjects:
Algorithms
Animals
Databases, Protein
evolutionary information
Internet
Neural Networks (Computer)
profiles
Protein Structure, Secondary
Proteins - chemistry
Proteins - classification
PSI-BLAST
recurrent neural networks
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Summary:Secondary structure predictions are increasingly becoming the workhorse for several methods aiming at predicting protein structure and function. Here we use ensembles of bidirectional recurrent neural network architectures, PSI‐BLAST‐derived profiles, and a large nonredundant training set to derive two new predictors: (a) the second version of the SSpro program for secondary structure classification into three categories and (b) the first version of the SSpro8 program for secondary structure classification into the eight classes produced by the DSSP program. We describe the results of three different test sets on which SSpro achieved a sustained performance of about 78% correct prediction. We report confusion matrices, compare PSI‐BLAST to BLAST‐derived profiles, and assess the corresponding performance improvements. SSpro and SSpro8 are implemented as web servers, available together with other structural feature predictors at: http://promoter.ics.uci.edu/BRNN‐PRED/. Proteins 2002;47:228–235. © 2002 Wiley‐Liss, Inc.
ISSN:0887-3585
1097-0134
DOI:10.1002/prot.10082