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Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction.
- Source :
- Bioinformatics; 5/1/2017, Vol. 33 Issue 9, p1346-1353, 8p
- Publication Year :
- 2017
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13674803
- Volume :
- 33
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 122741442
- Full Text :
- https://doi.org/10.1093/bioinformatics/btw823