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

作者：Zhang T L^*; Ding Y S

来源：Amino Acids, 2007, 33(4): 623-629.

DOI：10.1007/s00726-007-0496-1

• protein structure classes
• pseudo amino acid composition
• correlation of amino acid
• hydrophobic amino acid couple
• binary tree support vector machines

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.

• 出版日期2007-11
• 单位东华大学