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Protein Secondary Structure Prediction

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Book cover Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 609))

Abstract

While the prediction of a native protein structure from sequence continues to remain a challenging problem, over the past decades computational methods have become quite successful in exploiting the mechanisms behind secondary structure formation. The great effort expended in this area has resulted in the development of a vast number of secondary structure prediction methods. Especially the combination of well-optimized/sensitive machine-learning algorithms and inclusion of homologous sequence information has led to increased prediction accuracies of up to 80%. In this chapter, we will first introduce some basic notions and provide a brief history of secondary structure prediction advances. Then a comprehensive overview of state-of-the-art prediction methods will be given. Finally, we will discuss open questions and challenges in this field and provide some practical recommendations for the user.

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Authors and Affiliations

  1. Centre for Integrative Bioinformatics VU (IBIVU), VU University, Amsterdam, The Netherlands

    Walter Pirovano & Jaap Heringa

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  1. Walter Pirovano
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  2. Jaap Heringa
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Editors and Affiliations

  1. Max F. Perutz Laboratories GmbH, Universität Wien, Dr. Bohr-Gasse 9, Wien, 1030, Austria

    Oliviero Carugo

  2. Research (A*STAR), Agency for Science & Technology, Biopolis Street 30, Singapore, 138671, Singapore

    Frank Eisenhaber

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Pirovano, W., Heringa, J. (2010). Protein Secondary Structure Prediction. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 609. Humana Press. https://doi.org/10.1007/978-1-60327-241-4_19

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  • DOI: https://doi.org/10.1007/978-1-60327-241-4_19

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  • Print ISBN: 978-1-60327-240-7

  • Online ISBN: 978-1-60327-241-4

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