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On the Encoding of Proteins for Disordered Regions Prediction

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  • Julien Becker
  • Francis Maes
  • Louis Wehenkel

Abstract

Disordered regions, i.e., regions of proteins that do not adopt a stable three-dimensional structure, have been shown to play various and critical roles in many biological processes. Predicting and understanding their formation is therefore a key sub-problem of protein structure and function inference. A wide range of machine learning approaches have been developed to automatically predict disordered regions of proteins. One key factor of the success of these methods is the way in which protein information is encoded into features. Recently, we have proposed a systematic methodology to study the relevance of various feature encodings in the context of disulfide connectivity pattern prediction. In the present paper, we adapt this methodology to the problem of predicting disordered regions and assess it on proteins from the 10th CASP competition, as well as on a very large subset of proteins extracted from PDB. Our results, obtained with ensembles of extremely randomized trees, highlight a novel feature function encoding the proximity of residues according to their accessibility to the solvent, which is playing the second most important role in the prediction of disordered regions, just after evolutionary information. Furthermore, even though our approach treats each residue independently, our results are very competitive in terms of accuracy with respect to the state-of-the-art. A web-application is available at http://m24.giga.ulg.ac.be:81/x3Disorder.

Suggested Citation

  • Julien Becker & Francis Maes & Louis Wehenkel, 2013. "On the Encoding of Proteins for Disordered Regions Prediction," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
  • Handle: RePEc:plo:pone00:0082252
    DOI: 10.1371/journal.pone.0082252
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    References listed on IDEAS

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    1. Julien Becker & Francis Maes & Louis Wehenkel, 2013. "On the Relevance of Sophisticated Structural Annotations for Disulfide Connectivity Pattern Prediction," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-14, February.
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    Cited by:

    1. Pahalage Dhanushka Sandaruwan & Champi Thusangi Wannige, 2021. "An improved deep learning model for hierarchical classification of protein families," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-15, October.
    2. Zhiheng Wang & Qianqian Yang & Tonghua Li & Peisheng Cong, 2015. "DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.

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