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

Journal: Bioinformatics (Oxford, England)
Published Date:

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.

Authors

  • Claire Marks
    Department of Statistics, University of Oxford, Oxford, UK.
  • Jaroslaw Nowak
    Department of Statistics, University of Oxford, Oxford, UK.
  • Stefan Klostermann
    Roche Pharma Research and Early Development Informatics, Roche Innovation Center Munich, Penzberg, Germany.
  • Guy Georges
    a Roche Pharmaceutical Research and Early Development, Large Molecule Research, Roche Innovation Center Penzberg , Nonnenwald 2, Penzberg , Germany.
  • James Dunbar
    Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, DE, Germany.
  • Jiye Shi
    UCB Pharma, Slough, Berkshire SL1 3WE, U.K.
  • Sebastian Kelm
    Department of Informatics, UCB Pharma, Slough, UK.
  • Charlotte M Deane
    Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom.