Abstract
BackgroundDNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions.ResultsWe firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods.ConclusionsThe experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.
Highlights
DNA-binding proteins are pivotal to the cell functions such as DNA replication, transcriptional regulation, packaging recombination, DNA repair, DNA modification and other fundamental activities associated with DNA
To further advance the prediction accuracy and understand the binding mechanism of DNA-protein interaction, we presented here a novel encoding method called PSSM distance transformation (PSSM-DT) to transform the PSSM profiles of query sequences into uniform numeric representations
We can conclude that support vector machine (SVM)-PSSM-DT achieves the best performance when LG = 5, which mean that the dimension of the feature space applied in this work is 2000
Summary
DNA-binding proteins are pivotal to the cell functions such as DNA replication, transcriptional regulation, packaging recombination, DNA repair, DNA modification and other fundamental activities associated with DNA. In case of structure-based prediction methods, Stawiski et al [19] examined positively charged patches on the surface of putative DNA-binding proteins in comparison with that on non DNA-binding proteins They employed 12 features including the patch size, hydrogen-bonding potential, and the fraction of evolutionary conserved positively charged residues and other properties of the protein to train a neural network (NN) for identifying DNA-binding proteins. Bhardwaj et al [15] examined the sizes of positively charged patches on the surface of putative DNA-binding proteins They based their SVM classifier on the protein’s overall charge, overall and surface amino acid composition. To further advance the prediction accuracy and understand the binding mechanism of DNA-protein interaction, we presented here a novel encoding method called PSSM distance transformation (PSSM-DT) to transform the PSSM profiles of query sequences into uniform numeric representations. The benchmark test and independent test showed that PSSM-DT is a promising protein encoding method
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