中国物理B ›› 2011, Vol. 20 ›› Issue (1): 10505-010505.doi: 10.1088/1674-1056/20/1/010505

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Protein structural classification and family identification by multifractal analysis and wavelet spectrum

Ahn Vo1, 朱少茗2, 喻祖国2   

  1. (1)School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, Q 4001, Australia; (2)School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, Q 4001, Australia;School of Mathematics and Computational Science, Xiangtan University, Hunan 411105, China
  • 收稿日期:2010-04-09 修回日期:2010-09-10 出版日期:2011-01-15 发布日期:2011-01-15
  • 基金资助:
    Project supported by the Australian Research Council (Grant No. DP0559807), a Research Capacity Building Award at QUT, Scientific Research Fund of Hunan Provincial Education Department of China (Grant No. 06C826), the Chinese Program for New Century Excellent Talents in University (Grant No. NCET-08-06867), the Hunan Provincial Natural Science Foundation of China (Grant No. 10JJ7001), the Program for Furong Scholars of Hunan Province of China, and the Aid program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province of China.

Protein structural classification and family identification by multifractal analysis and wavelet spectrum

Zhu Shao-Ming(朱少茗)a)b), Yu Zu-Guo(喻祖国) a)b)†, and Ahn Voa)   

  1. a School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, Q 4001, Australia; b School of Mathematics and Computational Science, Xiangtan University, Hunan 411105, China
  • Received:2010-04-09 Revised:2010-09-10 Online:2011-01-15 Published:2011-01-15
  • Supported by:
    Project supported by the Australian Research Council (Grant No. DP0559807), a Research Capacity Building Award at QUT, Scientific Research Fund of Hunan Provincial Education Department of China (Grant No. 06C826), the Chinese Program for New Century Excellent Talents in University (Grant No. NCET-08-06867), the Hunan Provincial Natural Science Foundation of China (Grant No. 10JJ7001), the Program for Furong Scholars of Hunan Province of China, and the Aid program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province of China.

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摘要： Family identification is helpful for predicting protein functions. It has been known from the literature that longer sequences of base pairs or amino acids are required to study patterns in biological sequences. Since most protein sequences are relatively short, we randomly concatenate or link the protein sequences from the same family or superfamily together to form longer protein sequences. The 6-letter model, 12-letter model, 20-letter model, the revised Schneider and Wrede scale hydrophobicity, solvent accessibility and stochastic standard state accessibility are used to convert linked protein sequences into numerical sequences. Then multifractal analyses and wavelet analysis are performed on these numerical sequences. The parameters from these analyses can be used to construct parameter spaces where each linked protein is represented by a point. The four classes of proteins, namely the α,β, α+βand α /β classes, are then distinguished in these parameter spaces. The Fisher linear discriminant algorithm is used to assess the discriminant accuracy. Numerical results indicate that the discriminant accuracies are satisfactory in separating these classes. We find that the linked proteins from the same family or superfamily tend to group together and can be separated from other linked proteins. The methods are helpful for identifying the family of an unknown protein.

关键词: protein family, multifractal analysis, wavelet spectrum

Abstract: Family identification is helpful for predicting protein functions. It has been known from the literature that longer sequences of base pairs or amino acids are required to study patterns in biological sequences. Since most protein sequences are relatively short, we randomly concatenate or link the protein sequences from the same family or superfamily together to form longer protein sequences. The 6-letter model, 12-letter model, 20-letter model, the revised Schneider and Wrede scale hydrophobicity, solvent accessibility and stochastic standard state accessibility are used to convert linked protein sequences into numerical sequences. Then multifractal analyses and wavelet analysis are performed on these numerical sequences. The parameters from these analyses can be used to construct parameter spaces where each linked protein is represented by a point. The four classes of proteins, namely the $\alpha$, $\beta$,  $\alpha+\beta$ and $\alpha/\beta$ classes, are then distinguished in these parameter spaces. The Fisher linear discriminant algorithm is used to assess the discriminant accuracy. Numerical results indicate that the discriminant accuracies are satisfactory in separating these classes. We find that the linked proteins from the same family or superfamily tend to group together and can be separated from other linked proteins. The methods are helpful for identifying the family of an unknown protein.

Key words: protein family, multifractal analysis, wavelet spectrum

中图分类号:  (Fractals)

  • 05.45.Df
64.60.al (Fractal and multifractal systems) 87.15.bd (Secondary structure)