Prediction of DNA-binding residues from protein sequence information using random forests

• Published in: BMC genomics Vol. 10; no. Suppl 1; p. S1
• Main Authors: Wang, Liangjiang, Yang, Mary Qu, Yang, Jack Y
• Format: Journal Article
• Language: English
• Published: London BioMed Central 07.07.2009
• Subjects: Algorithms; Animal Genetics and Genomics; Artificial Intelligence; Binding Sites; Biomedical and Life Sciences; Computational Biology - methods; DNA-Binding Proteins - metabolism; Life Sciences; Microarrays; Microbial Genetics and Genomics; Plant Genetics and Genomics; Proteomics; ROC Curve; Sequence Analysis, Protein - methods; Software; Classifier Performance; Evolutionary Information; Prediction Strength; Receiver Operating Characteristic Curve; Random Forest
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-10-S1-S1

Background Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data. Results A new machine learning approach has been developed in this study for predicting DNA-binding residues from amino acid sequence data. The approach used both the labelled data instances collected from the available structures of protein-DNA complexes and the abundant unlabeled data found in protein sequence databases. The evolutionary information contained in the unlabeled sequence data was represented as position-specific scoring matrices (PSSMs) and several new descriptors. The sequence-derived features were then used to train random forests (RFs), which could handle a large number of input variables and avoid model overfitting. The use of evolutionary information was found to significantly improve classifier performance. The RF classifier was further evaluated using a separate test dataset, and the predicted DNA-binding residues were examined in the context of three-dimensional structures. Conclusion The results suggest that the RF-based approach gives rise to more accurate prediction of DNA-binding residues than previous studies. A new web server called BindN-RF http://bioinfo.ggc.org/bindn-rf/ has thus been developed to make the RF classifier accessible to the biological research community.