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Computational Prediction of Disordered Protein Motifs Using SLiMSuite

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Intrinsically Disordered Proteins

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2141))

Abstract

Short linear motifs (SLiMs) are important mediators of interactions between intrinsically disordered regions of proteins and their interaction partners. Here, we detail instructions for the computational prediction of SLiMs in disordered protein regions, using the main tools of the SLiMSuite package: (1) SLiMProb identifies and calculates enrichment of predefined motifs in a set of proteins; (2) SLiMFinder predicts SLiMs de novo in a set of proteins, accounting for evolutionary relationships; (3) QSLiMFinder increases SLiMFinder sensitivity by focusing SLiM prediction on a specific query protein/region; (4) CompariMotif compares predicted SLiMs to known SLiMs or other SLiM predictions to identify common patterns. For each tool, command-line and online server examples are provided. Detailed notes provide additional advice on different applications of SLiMSuite, including batch running of multiple datasets and conservation masking using alignments of predicted orthologues.

SLiMSuite is freely available under a GNU General Public License at https://github.com/slimsuite/SLiMSuite. SLiMSuite servers are available at http://www.slimsuite.unsw.edu.au/servers.php.

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Acknowledgments

This work was supported by the University of New South Wales. The authors would like to thank Adrian Plummer and the UNSW Science Faculty Computing unit for support in setting up the SLiMSuite servers.

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Authors and Affiliations

  1. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia

    Richard J. Edwards, Kirsti Paulsen, Carla M. Aguilar Gomez & Åsa Pérez-Bercoff

Authors
  1. Richard J. Edwards
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  2. Kirsti Paulsen
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  3. Carla M. Aguilar Gomez
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  4. Åsa Pérez-Bercoff
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Corresponding author

Correspondence to Richard J. Edwards .

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Editors and Affiliations

  1. Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, København N, Denmark

    Birthe B. Kragelund

  2. Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark

    Karen Skriver

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Edwards, R.J., Paulsen, K., Aguilar Gomez, C.M., Pérez-Bercoff, Å. (2020). Computational Prediction of Disordered Protein Motifs Using SLiMSuite. In: Kragelund, B.B., Skriver, K. (eds) Intrinsically Disordered Proteins. Methods in Molecular Biology, vol 2141. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0524-0_3

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  • DOI: https://doi.org/10.1007/978-1-0716-0524-0_3

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0523-3

  • Online ISBN: 978-1-0716-0524-0

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