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Bioinformatics. 2017 May 1;33(9):1346-1353. doi: 10.1093/bioinformatics/btw823.

Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction.

Author information

1
Department of Statistics, University of Oxford, Oxford, UK.
2
Pharma Research and Early Development, Informatics.
3
Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, DE, Germany.
4
Department of Informatics, UCB Pharma, Slough, UK.

Abstract

Motivation:

Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction.

Results:

We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed.

Availability and Implementation:

Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx.

Contact:

deane@stats.ox.ac.uk.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28453681
PMCID:
PMC5408792
DOI:
10.1093/bioinformatics/btw823
[Indexed for MEDLINE]
Free PMC Article

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