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StackSSSPred: A Stacking-Based Prediction of Supersecondary Structure from Sequence

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Book cover Protein Supersecondary Structures

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1958))

Abstract

Supersecondary structure (SSS) refers to specific geometric arrangements of several secondary structure (SS) elements that are connected by loops. The SSS can provide useful information about the spatial structure and function of a protein. As such, the SSS is a bridge between the secondary structure and tertiary structure. In this chapter, we propose a stacking-based machine learning method for the prediction of two types of SSSs, namely, β-hairpins and β-α-β, from the protein sequence based on comprehensive feature encoding. To encode protein residues, we utilize key features such as solvent accessibility, conservation profile, half surface exposure, torsion angle fluctuation, disorder probabilities, and more. The usefulness of the proposed approach is assessed using a widely used threefold cross-validation technique. The obtained empirical result shows that the proposed approach is useful and prediction can be improved further.

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Acknowledgment

The authors gratefully acknowledge the Louisiana Board of Regents through the Board of Regents Support Fund LEQSF (2016-19)-RD-B-07.

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Author notes

  1. Michael Flot and Avdesh Mishra contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science, University of New Orleans, New Orleans, LA, USA

    Michael Flot, Avdesh Mishra, Aditi Sharma Kuchi & Md Tamjidul Hoque

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  1. Michael Flot
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  2. Avdesh Mishra
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  3. Aditi Sharma Kuchi
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  4. Md Tamjidul Hoque
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Correspondence to Md Tamjidul Hoque .

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  1. Department of Mathematics, Rutgers University, Piscataway, NJ, USA

    Alexander E. Kister

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Flot, M., Mishra, A., Kuchi, A.S., Hoque, M.T. (2019). StackSSSPred: A Stacking-Based Prediction of Supersecondary Structure from Sequence. In: Kister, A. (eds) Protein Supersecondary Structures. Methods in Molecular Biology, vol 1958. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9161-7_5

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  • DOI: https://doi.org/10.1007/978-1-4939-9161-7_5

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