Genetic Programming and Evolvable Machines

, Volume 11, Issue 1, pp 61–88 | Cite as

GP challenge: evolving energy function for protein structure prediction

  • Paweł Widera
  • Jonathan M. Garibaldi
  • Natalio Krasnogor
Original Paper

Abstract

One of the key elements in protein structure prediction is the ability to distinguish between good and bad candidate structures. This distinction is made by estimation of the structure energy. The energy function used in the best state-of-the-art automatic predictors competing in the most recent CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiment is defined as a weighted sum of a set of energy terms designed by experts. We hypothesised that combining these terms more freely will improve the prediction quality. To test this hypothesis, we designed a genetic programming algorithm to evolve the protein energy function. We compared the predictive power of the best evolved function and a linear combination of energy terms featuring weights optimised by the Nelder–Mead algorithm. The GP based optimisation outperformed the optimised linear function. We have made the data used in our experiments publicly available in order to encourage others to further investigate this challenging problem by using GP and other methods, and to attempt to improve on the results presented here.

Keywords

Genetic programming Protein structure prediction Protein energy function 

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Paweł Widera
    • 1
  • Jonathan M. Garibaldi
    • 1
  • Natalio Krasnogor
    • 1
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK

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