Predicting DNA- and RNA-binding proteins from sequences with kernel methods.
In this paper, support vector machines (SVMs) are applied to predict the nucleic-acid-binding proteins. We constructed two classifiers to differentiate DNA/RNA-binding proteins from non-nucleic-acid-binding proteins by using a conjoint triad feature which extract information directly from amino acids sequence of protein. Both self-consistency and jackknife tests show promising results on the protein datasets in which the sequences identity is less than 25%. In the self-consistency test, the predictive accuracy is 90.37% for DNA-binding proteins and 89.70% for RNA-binding proteins. In the jackknife test, the predictive accuracies are 78.93% and 76.75%, respectively. Comparison results show that our method is very competitive by outperforming other previously published sequence-based prediction methods.
Shao
Xiaojian
X
College of Science, China Agricultural University, Beijing 100083, China.
Tian
Yingjie
Y
Wu
Lingyun
L
Wang
Yong
Y
Jing
Ling
L
Deng
Naiyang
N
D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology; D004268:DNA-Binding Proteins / Q000737:chemistry* / Q000235:genetics; D030562:Databases, Protein; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D016601:RNA-Binding Proteins / Q000737:chemistry* / Q000235:genetics; D020539:Sequence Analysis, Protein / Q000379:methods*
D004268:DNA-Binding Proteins; D016601:RNA-Binding Proteins