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Differential Evolution for Protein Structure Prediction Using the HP Model

  • J. Santos
  • M. Diéguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)

Abstract

We used Differential Evolution (DE) for the problem of protein structure prediction. We employed the HP model to represent the folding conformations of a protein in a lattice. In this model the nature of amino acids is reduced considering only two types: hydrophobic residues (H) and polar residues (P), which is based on the recognition that hydrophobic interactions are a dominant force in protein folding. Given a primary sequence of amino acids, the problem is to search for the folding structure in the lattice that minimizes an energy potential. This energy reflects the fact that the hydrophobic amino acids have a propensity to form a hydrophobic core. The complexity of the problem has been shown to be NP-hard, with minimal progress achieved in this category of ab initio folding. We combined DE with methods to transform illegal protein conformations to feasible ones, showing the capabilities of the hybridized DE with respect to previous works.

Keywords

Genetic Algorithm Candidate Solution Protein Structure Prediction Absolute Move Relative Encode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • J. Santos
    • 1
  • M. Diéguez
    • 1
  1. 1.Computer Science DepartmentUniversity of A CoruñaSpain

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