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From HP Lattice Models to Real Proteins: Coordination Number Prediction Using Learning Classifier Systems

  • Michael Stout
  • Jaume Bacardit
  • Jonathan D. Hirst
  • Natalio Krasnogor
  • Jacek Blazewicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

Prediction of the coordination number (CN) of residues in proteins based solely on protein sequence has recently received renewed attention. At the same time, simplified protein models such as the HP model have been used to understand protein folding and protein structure prediction. These models represent the sequence of a protein using two residue types: hydrophobic and polar, and restrict the residue locations to those of a lattice. The aim of this paper is to compare CN prediction at three levels of abstraction a) 3D Cubic lattice HP model proteins, b) Real proteins represented by their HP sequence and c) Real proteins using residue sequence alone. For the 3D HP lattice model proteins the CN of each residue is simply the number of neighboring residues on the lattice. For the real proteins, we use a recent real-valued definition of CN proposed by Kinjo et al. To perform the predictions we use GAssist, a recent evolutionary computation based machine learning method belonging to the Learning Classifier System (LCS) family. Its performance was compared against some alternative learning techniques. Predictions using the HP sequence representation with only two residue types were only a little worse than those using a full 20 letter amino acid alphabet (64% vs 68% for two state prediction, 45% vs 50% for three state prediction and 30% vs 33% for five state prediction). That HP sequence information alone can result in predictions accuracies that are within 5% of those obtained using full residue type information indicates that hydrophobicity is a key determinant of CN and further justifies studies of simplified models.

Keywords

Window Size Coordination Number State Prediction Protein Structure Prediction Residue Type 
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 2006

Authors and Affiliations

  • Michael Stout
    • 1
  • Jaume Bacardit
    • 1
  • Jonathan D. Hirst
    • 2
  • Natalio Krasnogor
    • 1
  • Jacek Blazewicz
    • 3
  1. 1.Automated Scheduling, Optimization and Planning research group, School of Computer Science and ITUniversity of NottinghamNottinghamUK
  2. 2.School of ChemistryUniversity of NottinghamNottinghamUK
  3. 3.Institute of Computing SciencePoznan University of TechnologyPoznanPoland

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