Evolutionary design of the energy function for protein structure prediction

Authors:
Paweł Widera;Jonathan M. Garibaldi;Natalio Krasnogor
Affiliations:
School of Computer Science, University of Nottingham, Nottingham, UK;School of Computer Science, University of Nottingham, Nottingham, UK;School of Computer Science, University of Nottingham, Nottingham, UK
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

Automatic protein structure predictors use the notion of energy to guide the search towards good candidate structures. The energy functions used by the state-of-the-art predictors are defined as a linear combination of several energy terms designed by human experts. We hypothesised that the energy based guidance could be more accurate if the terms were combined more freely. To test this hypothesis, we designed a genetic programming algorithm to evolve the protein energy function. Using several different fitness functions we examined the potential of the evolutionary approach on a set of candidate structures generated during the protein structure prediction process. Although our algorithms were able to improve over the random walk, the fitness of the best individuals was far from the optimum. We discuss the shortcomings of our initial algorithm design and the possible directions for further research.