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Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure

Journal of Molecular Modeling volume 19, pages 4337–4348 (2013)Cite this article

Abstract

Exponential growth in the number of available protein sequences is unmatched by the slower growth in the number of structures. As a result, the development of efficient and fast protein secondary structure prediction methods is essential for the broad comprehension of protein structures. Computational methods that can efficiently determine secondary structure can in turn facilitate protein tertiary structure prediction, since most methods rely initially on secondary structure predictions. Recently, we have developed a fast learning optimized prediction methodology (FLOPRED) for predicting protein secondary structure (Saraswathi et al. in JMM 18:4275, 2012). Data are generated by using knowledge-based potentials combined with structure information from the CATH database. A neural network-based extreme learning machine (ELM) and advanced particle swarm optimization (PSO) are used with this data to obtain better and faster convergence to more accurate secondary structure predicted results. A five-fold cross-validated testing accuracy of 83.8 % and a segment overlap (SOV) score of 78.3 % are obtained in this study. Secondary structure predictions and their accuracy are usually presented for three secondary structure elements: α-helix, β-strand and coil but rarely have the results been analyzed with respect to their constituent amino acids. In this paper, we use the results obtained with FLOPRED to provide detailed behaviors for different amino acid types in the secondary structure prediction. We investigate the influence of the composition, physico-chemical properties and position specific occurrence preferences of amino acids within secondary structure elements. In addition, we identify the correlation between these properties and prediction accuracy. The present detailed results suggest several important ways that secondary structure predictions can be improved in the future that might lead to improved protein design and engineering.

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Acknowledgments

The algorithm for the knowledge-based potentials data, was developed by members from the Kolinski [36] lab. This work was supported by the National Science Foundation grant IGERT-0504304, National Science Foundation grant MSB-1021785 and National Institutes of Health grants R01GM081680 and R01GM072014.

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Affiliations

  1. Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children’s Hospital, 700 Children’s Drive, Columbus, OH, USA

    S. Saraswathi & A. Kloczkowski

  2. Department of Mathematics, University of Oviedo, Oviedo, Spain

    J. L. Fernández-Martínez

  3. Laboratory of Theory of Biopolymers, Faculty of Chemistry, Warsaw University, Pasteura 1, 02-093, Warsaw, Poland

    A. Koliński

  4. Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA, USA

    R. L. Jernigan

  5. Department of Pediatrics, The Ohio State University College of Medicine, 700 Children’s Drive, Columbus, OH, 43205, USA

    A. Kloczkowski

Authors
  1. S. Saraswathi
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  2. J. L. Fernández-Martínez
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  3. A. Koliński
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  4. R. L. Jernigan
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  5. A. Kloczkowski
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Corresponding author

Correspondence to A. Kloczkowski.

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Saraswathi, S., Fernández-Martínez, J.L., Koliński, A. et al. Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure. J Mol Model 19, 4337–4348 (2013). https://doi.org/10.1007/s00894-013-1911-z

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  • DOI: https://doi.org/10.1007/s00894-013-1911-z

Keywords

  • Amino acids
  • Knowledge-based potentials
  • Machine learning
  • Neural networks
  • Particle swarm optimization
  • Protein secondary structure prediction