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Support-vector-machine classification of linear functional motifs in proteins

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Abstract

Our algorithm predicts short linear functional motifs in proteins using only sequence information. Statistical models for short linear functional motifs in proteins are built using the database of short sequence fragments taken from proteins in the current release of the Swiss-Prot database. Those segments are confirmed by experiments to have single-residue post-translational modification. The sensitivities of the classification for various types of short linear motifs are in the range of 70%. The query protein sequence is dissected into short overlapping fragments. All segments are represented as vectors. Each vector is then classified by a machine learning algorithm (Support Vector Machine) as potentially modifiable or not. The resulting list of plausible post-translational sites in the query protein is returned to the user. We also present a study of the human protein kinase C family as a biological application of our method.

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Acknowledgements

This work was supported by the USA grant (“SPAM” GM63208), ELM (QLRT-CT2000-00127), BioSapiens (LHSG-CT-2003-503265), GeneFun (LSHG-CT-2004-503567) projects within five and six FP EC programs. A. K. acknowledges the financial support provided by the NIH grant 1R01GM072014-01. LSW is supported by Foundation for Polish Science within Program for Young Researchers.

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Authors and Affiliations

  1. BioInfoBank Institute, Limanowskiego 24A/16, 60-744, Poznan, Poland

    Dariusz Plewczynski, Adrian Tkacz & Leszek Rychlewski

  2. Interdisciplinary Centre for Mathematical and Computational Modeling, University of Warsaw, Pawinskiego 5a Street, 02-106, Warsaw, Poland

    Dariusz Plewczynski

  3. Bioinformatics Unit, Department of Physics, Adam Mickiewicz University, ul.Umultowska 85, 61-614, Poznan, Poland

    Lucjan Stanisław Wyrwicz

  4. Bioinformatics Core JCSG, University of California San Diego, La Jolla, CA, USA

    Adam Godzik

  5. The Burnham Institute, La Jolla, IO, USA

    Adam Godzik

  6. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, USA

    Andrzej Kloczkowski

Authors
  1. Dariusz Plewczynski
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  2. Adrian Tkacz
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  3. Lucjan Stanisław Wyrwicz
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  4. Adam Godzik
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  5. Andrzej Kloczkowski
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  6. Leszek Rychlewski
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Corresponding author

Correspondence to Dariusz Plewczynski.

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Plewczynski, D., Tkacz, A., Wyrwicz, L.S. et al. Support-vector-machine classification of linear functional motifs in proteins. J Mol Model 12, 453–461 (2006). https://doi.org/10.1007/s00894-005-0070-2

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  • DOI: https://doi.org/10.1007/s00894-005-0070-2

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