Medical Journals

Clustering of Protein Structures Using Hydrophobic Free Energy and Solvent Accessibility of Proteins.

Authors:
  • Yu Z G
  • Anh V V
  • Lau K S
  • Zhou L Q

From: Program in Statistics and Operations Research, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, Australia. yuzg1970@yahoo.com

Physical review. E, Statistical, nonlinear, and soft matter physics

  • Publish Date: Mar 2006
  • ISSN: 1539-3755
  • Volume: 73
  • Issue: 3 Pt 1
  • Pages: 031920
  • Medium: Print
  • Language: English
  • Citation (JAMA): Yu Z G, Anh V V, Lau K S, et al. Clustering of Protein Structures Using Hydrophobic Free Energy and Solvent Accessibility of Proteins.. Mar 2006;73:031920

Abstract

The hydrophobic free energy and solvent accessibility of amino acids are used to study the relationship between the primary structure and structural classification of large proteins. A measure representation and a Z curve representation of protein sequences are proposed. Fractal analysis of the measure and Z curve representations of proteins and multifractal analysis of their hydrophobic free energy and solvent accessibility sequences indicate that the protein sequences possess correlations and multifractal scaling. The parameters from the fractal and multifractal analyses on these sequences are used to construct some parameter spaces. Each protein is represented by a point in these spaces. A method is proposed to distinguish and cluster proteins from the alpha, beta, alpha + beta, and alpha/beta structural classes in these parameter spaces. Fisher’s linear discriminant algorithm is used to give a quantitative assessment of our clustering on the selected proteins. Numerical results indicate that the discriminant accuracies are satisfactory. In particular, they reach 94.12% and 88.89% in separating proteins from {alpha, alpha + beta, alpha/beta} proteins in a three-dimensional space.

Mesh Headings (Keywords): Annexin A6, Binding Sites, Computer Simulation, Dimerization, Energy Transfer, Hydrophobicity, Models, Chemical, Models, Molecular, Multiprotein Complexes, Protein Binding, Sequence Analysis, Protein, Solvents


Check for Full Text / PubMed Unique Identifier (PMID): 16605571


This abstract is part of PubMed, a service of the U.S. National Library of Medicine. PubMed includes more than 17 million citations from MEDLINE and other life science journals for biomedical articles. See Copyright and Disclaimers.

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The data herein was last updated on July 8th, 2008 and may not reflect the most current and accurate data available from NLM.


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