Amino Acids

, Volume 33, Issue 4, pp 623–629

Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes

  • T.-L. Zhang
  • Y.-S. Ding
Article

DOI: 10.1007/s00726-007-0496-1

Cite this article as:
Zhang, TL. & Ding, YS. Amino Acids (2007) 33: 623. doi:10.1007/s00726-007-0496-1

Summary.

Compared with the conventional amino acid composition (AA), the pseudo amino acid composition (PseAA) as originally introduced by Chou can incorporate much more information of a protein sequence; this remarkably enhances the power to use a discrete model for predicting various attributes of a protein. In this study, based on the concept of Chou’s PseAA, a 46-D (dimensional) PseAA was formulated to represent the sample of a protein and a new approach based on binary-tree support vector machines (BTSVMs) was proposed to predict the protein structural class. BTSVMs algorithm has the capability in solving the problem of unclassifiable data points in multi-class SVMs. The results by both the 10-fold cross-validation and jackknife tests demonstrate that the predictive performance using the new PseAA (46-D) is better than that of AA (20-D), which is widely used in many algorithms for protein structural class prediction. The results obtained by the new approach are quite encouraging, indicating that it can at least play a complimentary role to many of the existing methods and is a useful tool for predicting many other protein attributes as well.

Keywords: Protein structure classes – Pseudo amino acid composition – Correlation of amino acid – Hydrophobic amino acid couple – Binary tree support vector machines 

Abbreviations:

BTSVM

binary-tree support vector machine

PseAA

pseudo amino acid composition

SVM

support vector machines

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • T.-L. Zhang
    • 1
  • Y.-S. Ding
    • 1
    • 2
  1. 1.College of Information Sciences and TechnologyDonghua UniversityShanghaiChina
  2. 2.Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of EducationDonghua UniversityShanghaiChina

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