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Protein Supersecondary Structures pp 63–86Cite as

Computational Prediction of Secondary and Supersecondary Structures

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 932))

Abstract

The sequence-based prediction of the secondary and supersecondary structures enjoys strong interest and finds applications in numerous areas related to the characterization and prediction of protein structure and function. Substantial efforts in these areas over the last three decades resulted in the development of accurate predictors, which take advantage of modern machine learning models and availability of evolutionary information extracted from multiple sequence alignment. In this chapter, we first introduce and motivate both prediction areas and introduce basic concepts related to the annotation and prediction of the secondary and supersecondary structures, focusing on the β hairpin, coiled coil, and α-turn-α motifs. Next, we overview state-of-the-art prediction methods, and we provide details for 12 modern secondary structure predictors and 4 representative supersecondary structure predictors. Finally, we provide several practical notes for the users of these prediction tools.

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Acknowledgment

This work was supported by the Alberta Ingenuity and Alberta Innovates Graduate Student Scholarship to KC and the NSERC Discovery grant to LK.

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  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Ke Chen & Lukasz Kurgan

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  1. Ke Chen
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  2. Lukasz Kurgan
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Correspondence to Lukasz Kurgan .

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  1. , Department of Mathematics, Rutgers University, Frelinghuysen Road 110, Piscataway, 08854-8019, USA

    Alexander E. Kister

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© 2012 Springer Science+Business Media New York

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Chen, K., Kurgan, L. (2012). Computational Prediction of Secondary and Supersecondary Structures. In: Kister, A. (eds) Protein Supersecondary Structures. Methods in Molecular Biology, vol 932. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-065-6_5

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  • DOI: https://doi.org/10.1007/978-1-62703-065-6_5

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-064-9

  • Online ISBN: 978-1-62703-065-6

  • eBook Packages: Springer Protocols

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