An Evolutionary Model Based on Hill-Climbing Search Operators for Protein Structure Prediction

  • Camelia Chira
  • Dragos Horvath
  • Dumitru Dumitrescu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6023)


The prediction of a minimum-energy protein structure from its amino-acid sequence represents one of the most important and challenging problems in computational biology. A new evolutionary model based on hill-climbing genetic operators is proposed to address the hydrophobic - polar model of the protein folding problem. The introduced model ensures an efficient exploration of the search space by implementing a problem-specific crossover operator and enforcing an explicit diversification stage during the evolution. The mutation operator engaged in the proposed model refers to the pull-move operation by which a single residue is moved diagonally causing the potential transition of connecting residues in the same direction in order to maintain a valid protein configuration. Both crossover and mutation are applied using a steepest-ascent hill-climbing approach. The resulting evolutionary algorithm with hill-climbing operators is successfully applied to the protein structure prediction problem for a set of difficult bidimensional instances from lattice models.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Camelia Chira
    • 1
  • Dragos Horvath
    • 2
  • Dumitru Dumitrescu
    • 1
  1. 1.Babes-Bolyai UniversityCluj-NapocaRomania
  2. 2.Laboratoire d’InfochimieUMR 7177, University StrasbourgFrance

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