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

By Claire Marks, Jaroslaw Nowak, S. Klostermann, Guy Georges, James Dunbar, Jiye Shi, Sebastian Kelm, Charlotte M. Deane. 2017

53 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging