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Protein alignment based on higher order conditional random fields for template-based modeling

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  • Juan A Morales-Cordovilla
  • Victoria Sanchez
  • Martin Ratajczak

Abstract

The query-template alignment of proteins is one of the most critical steps of template-based modeling methods used to predict the 3D structure of a query protein. This alignment can be interpreted as a temporal classification or structured prediction task and first order Conditional Random Fields have been proposed for protein alignment and proven to be rather successful. Some other popular structured prediction problems, such as speech or image classification, have gained from the use of higher order Conditional Random Fields due to the well known higher order correlations that exist between their labels and features. In this paper, we propose and describe the use of higher order Conditional Random Fields for query-template protein alignment. The experiments carried out on different public datasets validate our proposal, especially on distantly-related protein pairs which are the most difficult to align.

Suggested Citation

  • Juan A Morales-Cordovilla & Victoria Sanchez & Martin Ratajczak, 2018. "Protein alignment based on higher order conditional random fields for template-based modeling," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-14, June.
    • Handle: RePEc:plo:pone00:0197912
    DOI: 10.1371/journal.pone.0197912
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    References listed on IDEAS

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    1. Sheng Wang & Siqi Sun & Zhen Li & Renyu Zhang & Jinbo Xu, 2017. "Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-34, January.
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