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Protein Fold Recognition Based Upon the Amino Acid Occurrence

  • Y. -h. Taguchi
  • M. Michael Gromiha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)

Abstract

We have investigated the relative performance of amino acid occurrence and other features, such as predicted secondary structure, hydrophobicity, normalized van der Waals volume, polarity, polarizability, and real/predicted contact information of residues, for recognizing protein folds. We observed that the improvement over other features is only marginal compared with amino acid occurrence. This is because amino acid occurrence, indirectly, can consider varieties of physical properties which are useful to discriminate protein folds. If we consider only proteins which are well aligned structurally with each other, the accuracy of discrimination is drastically improved. In order to discriminate protein folds more accurately, we need to consider anything other than structure alignment.

Keywords

Linear Discriminant Analysis Structural Alignment Solvent Accessible Surface Area Gibbs Free Energy Change Fold Recognition 
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 2007

Authors and Affiliations

  • Y. -h. Taguchi
    • 1
  • M. Michael Gromiha
    • 2
  1. 1.Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551Japan
  2. 2.Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064Japan

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