Abstract
DNA-binding proteins play an important role in most cellular processes, such as gene regulation, recombination, repair, replication, and DNA modification. In this article, an optimal Chou’s pseudo amino acid composition (PseAAC) based on physicochemical characters of amino acid is proposed to represent proteins for identifying DNAbinding proteins. Six physicochemical characters of amino acids are utilized to generate the sequence features via the web server PseAAC. The optimal values of two important parameters (correlation factor λ and weighting factor w) about PseAAC are determined to get the appropriate representation of proteins, which ultimately result in better prediction performance. Experimental results on the benchmark datasets using random forest show that our method is really promising to predict DNA-binding proteins and may at least be a useful supplement tool to existing methods.
Keywords: DNA-binding proteins, pseudo amino acid composition, random forest (RF), physicochemical character
Protein & Peptide Letters
Title:Identify DNA-Binding Proteins with Optimal Chou’s Amino Acid Composition
Volume: 19 Issue: 4
Author(s): Xiao-Wei Zhao, Xiang-Tao Li, Zhi-Qiang Ma and Ming-Hao Yin
Affiliation:
Keywords: DNA-binding proteins, pseudo amino acid composition, random forest (RF), physicochemical character
Abstract: DNA-binding proteins play an important role in most cellular processes, such as gene regulation, recombination, repair, replication, and DNA modification. In this article, an optimal Chou’s pseudo amino acid composition (PseAAC) based on physicochemical characters of amino acid is proposed to represent proteins for identifying DNAbinding proteins. Six physicochemical characters of amino acids are utilized to generate the sequence features via the web server PseAAC. The optimal values of two important parameters (correlation factor λ and weighting factor w) about PseAAC are determined to get the appropriate representation of proteins, which ultimately result in better prediction performance. Experimental results on the benchmark datasets using random forest show that our method is really promising to predict DNA-binding proteins and may at least be a useful supplement tool to existing methods.
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Cite this article as:
Zhao Xiao-Wei, Li Xiang-Tao, Ma Zhi-Qiang and Yin Ming-Hao, Identify DNA-Binding Proteins with Optimal Chou’s Amino Acid Composition, Protein & Peptide Letters 2012; 19 (4) . https://dx.doi.org/10.2174/092986612799789404
DOI https://dx.doi.org/10.2174/092986612799789404 |
Print ISSN 0929-8665 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5305 |
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