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

摘要: 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 searched find suitable conformations and ab initio, generated computationally. Existing knowledge-based methods only use that the same length as target, even though loops slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines initio techniques with potential extra structural information contained within improve structure prediction. Results We show Sphinx is able generate high-accuracy predictions decoy sets enriched near-native conformations, performing better than algorithm on it based. In addition, provide every unlike some methods. used successfully problem antibody H3 prediction, outperforming RosettaAntibody, one leading H3-specific methods, both in accuracy speed. Availability implementation available at http://opig.stats.ox.ac.uk/webapps/sphinx. Contact deane@stats.ox.ac.uk. Supplementary data Bioinformatics online.