Protein alignment based on higher order conditional random fields for template-based modeling
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Public Library of Science (PLOS)
Morales-Cordovilla JA, Sanchez V, RatajczakM (2018) Protein alignment based on higher order conditional random fields for template-based modeling. PLoS ONE 13(6): e0197912. [https://doi.org/10.1371/journal. pone.0197912]
SponsorshipThis research was supported by Project P12.TIC.1485 funded by Consejeria de Economia, Innovacion y Ciencia (Junta de Andalucia) and Spanish MINECO/FEDER Project TEC2016-80141- P.
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.