Combining protein fragment feature-based resampling and local optimisation

  • Authors:

  • Trent Higgs;Lukas Folkman;Bela Stantic

  • Affiliations:

  • Institute for Integrated and Intelligent Systems, Griffith University, Australia;Institute for Integrated and Intelligent Systems, Griffith University, Australia;Institute for Integrated and Intelligent Systems, Griffith University, Australia

  • Venue:
  • PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
  • Year:
  • 2013

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Abstract

Protein structure prediction (PSP) suites can predict 'near-native' protein models. However, not always these predicted models are close to the native structure with enough precision to be useful for biologists. The literature to date demonstrates that one of the best techniques to predict 'near-native' protein models is to use a fragment-based search strategy. Another technique that can help refine protein models is local optimisation. Local optimisation algorithms use the gradient of the function being optimised to suggest which move will bring the function value closer to its local minimum. In this work we combine the concepts of structural refinement through feature-based resampling, fragment-based PSP, and local optimisation to create an algorithm that can create protein models that are closer to their native states. In experiments we demonstrated that our new method generates models that are close to their native conformations. For structures in the test set, it obtained an average RMSD of 5.09$ \textrm{\AA}$ and an average best TM-Score of 0.47 when no local optimisation was applied. However, by applying local optimisation to our algorithm, additional improvements were achieved.