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

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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.

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Abbreviations

BTSVM:

binary-tree support vector machine

PseAA:

pseudo amino acid composition

SVM:

support vector machines

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Authors and Affiliations

  1. College of Information Sciences and Technology, Donghua University, Shanghai, China

    T.-L. Zhang & Y.-S. Ding

  2. Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of Education, Donghua University, Shanghai, China

    Y.-S. Ding

Authors
  1. T.-L. Zhang
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  2. Y.-S. Ding
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Zhang, TL., Ding, YS. Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes. Amino Acids 33, 623–629 (2007). https://doi.org/10.1007/s00726-007-0496-1

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  • DOI: https://doi.org/10.1007/s00726-007-0496-1

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