Exploiting the Past and the Future in Protein Secondary Structure Prediction

Pierre Baldi, Søren Brunak, P Frasconi, G Soda, Gianluca Pollastri

    Research output: Contribution to journalJournal articleResearchpeer-review


    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.
    Original languageEnglish
    Issue number11
    Pages (from-to)937-946
    Publication statusPublished - 1999
    Event2nd Georgia Tech Conference on Bioinformatics - Atlanta, USA
    Duration: 1 Jan 1999 → …


    Conference2nd Georgia Tech Conference on Bioinformatics
    CityAtlanta, USA
    Period01/01/1999 → …

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