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

Juan A. Morales-Cordovilla*, Victoria Sanchez, Martin Ratajczak

*Corresponding author for this work

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article numbere0197912
JournalPLoS ONE
Volume13
Issue number6
DOIs
Publication statusPublished - 1 Jun 2018

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

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