Abstract
BackgroundDNA-binding proteins (DBPs) play fundamental roles in many biological processes. Therefore, the developing of effective computational tools for identifying DBPs is becoming highly desirable.ResultsIn this study, we proposed an accurate method for the prediction of DBPs. Firstly, we focused on the challenge of improving DBP prediction accuracy with information solely from the sequence. Secondly, we used multiple informative features to encode the protein. These features included evolutionary conservation profile, secondary structure motifs, and physicochemical properties. Thirdly, we introduced a novel improved Binary Firefly Algorithm (BFA) to remove redundant or noisy features as well as select optimal parameters for the classifier. The experimental results of our predictor on two benchmark datasets outperformed many state-of-the-art predictors, which revealed the effectiveness of our method. The promising prediction performance on a new-compiled independent testing dataset from PDB and a large-scale dataset from UniProt proved the good generalization ability of our method. In addition, the BFA forged in this research would be of great potential in practical applications in optimization fields, especially in feature selection problems.ConclusionsA highly accurate method was proposed for the identification of DBPs. A user-friendly web-server named iDbP (identification of DNA-binding Proteins) was constructed and provided for academic use.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1201-8) contains supplementary material, which is available to authorized users.
Highlights
DNA-binding proteins (DBPs) play fundamental roles in many biological processes
The DBPs span over many protein families from enzymes to transcription factors [16], which makes it very difficult to describe DBPs discriminatively using mathematical models; (iii) A common approach to describe a protein in DBP prediction is by forming a feature vector, but the redundancy and contradiction among these features may seriously deteriorate the predication and generalization ability of the model
In light of the aforementioned problems, we proposed a novel sequence-based predictor, named iDbP, to identify DBPs in this study
Summary
We proposed an accurate method for the prediction of DBPs. Firstly, we focused on the challenge of improving DBP prediction accuracy with information solely from the sequence. We used multiple informative features to encode the protein. These features included evolutionary conservation profile, secondary structure motifs, and physicochemical properties. We introduced a novel improved Binary Firefly Algorithm (BFA) to remove redundant or noisy features as well as select optimal parameters for the classifier. The promising prediction performance on a new-compiled independent testing dataset from PDB and a large-scale dataset from UniProt proved the good generalization ability of our method. The BFA forged in this research would be of great potential in practical applications in optimization fields, especially in feature selection problems
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