Abstract
BackgroundDNA-binding proteins are vital for the study of cellular processes. In recent genome engineering studies, the identification of proteins with certain functions has become increasingly important and needs to be performed rapidly and efficiently. In previous years, several approaches have been developed to improve the identification of DNA-binding proteins. However, the currently available resources are insufficient to accurately identify these proteins. Because of this, the previous research has been limited by the relatively unbalanced accuracy rate and the low identification success of the current methods.ResultsIn this paper, we explored the practicality of modelling DNA binding identification and simultaneously employed an ensemble classifier, and a new predictor (nDNA-Prot) was designed. The presented framework is comprised of two stages: a 188-dimension feature extraction method to obtain the protein structure and an ensemble classifier designated as imDC. Experiments using different datasets showed that our method is more successful than the traditional methods in identifying DNA-binding proteins. The identification was conducted using a feature that selected the minimum Redundancy and Maximum Relevance (mRMR). An accuracy rate of 95.80% and an Area Under the Curve (AUC) value of 0.986 were obtained in a cross validation. A test dataset was tested in our method and resulted in an 86% accuracy, versus a 76% using iDNA-Prot and a 68% accuracy using DNA-Prot.ConclusionsOur method can help to accurately identify DNA-binding proteins, and the web server is accessible at http://datamining.xmu.edu.cn/~songli/nDNA. In addition, we also predicted possible DNA-binding protein sequences in all of the sequences from the UniProtKB/Swiss-Prot database.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-298) contains supplementary material, which is available to authorized users.
Highlights
DNA-binding proteins are vital for the study of cellular processes
The presentation of the new ensemble classifier imDC was shown to improve the ease of discriminating DNA-binding proteins from other complex proteins
After a series of feature extraction comparisons, the 188D feature extraction method suggested the superiority of our unbalanced dataset, even if the improved dimension resulted in the loss of time
Summary
DNA-binding proteins are vital for the study of cellular processes. In recent genome engineering studies, the identification of proteins with certain functions has become increasingly important and needs to be performed rapidly and efficiently. Several approaches have been developed to improve the identification of DNA-binding proteins. In the 188D method, the first 20 feature vectors are obtained based on the probability that every amino acid appears in a given protein sequence. According to the protein position-specific scoring matrix, Zou et al [3] extracted a 20D feature from protein sequences, and in 1992, Brown et al [10] proposed the n-gram natural language algorithm. This type of algorithm, applied in another previous study [11], obtains the feature vectors by using a probability calculation. The Basic Local Alignment Search Tool (BLAST), which is based on a position-specific scoring matrix, has been applied to detect remote protein homology [12]
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