ABSTRACT
Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein-DNA binding and protein-RNA binding, it is important to identify which residues in the protein sequence bind to DNA and RNA. At present, there are few methods for specifically identifying the binding sites of disease-related protein-DNA and protein-RNA. In this study, so we combined four machine learning algorithms into an ensemble classifier (EPDRNA) to predict DNA and RNA binding sites in disease-related proteins. The dataset used in model was collated from UniProt and PDB database, and PSSM, physicochemical properties and amino acid type were used as features. The EPDRNA adopted soft voting and achieved the best AUC value of 0.73 at the DNA binding sites, and the best AUC value of 0.71 at the RNA binding sites in 10-fold cross validation in the training sets. In order to further verify the performance of the model, we assessed EPDRNA for the prediction of DNA-binding sites and the prediction of RNA-binding sites on the independent test dataset. The EPDRNA achieved 85% recall rate and 25% precision on the protein-DNA interaction independent test set, and achieved 82% recall rate and 27% precision on the protein-RNA interaction independent test set. The online EPDRNA webserver is freely available at http://www.s-bioinformatics.cn/epdrna .
Subject(s)
DNA , Machine Learning , RNA , Binding Sites , RNA/metabolism , RNA/chemistry , Humans , DNA/metabolism , DNA/chemistry , RNA-Binding Proteins/chemistry , RNA-Binding Proteins/metabolism , RNA-Binding Proteins/genetics , DNA-Binding Proteins/chemistry , DNA-Binding Proteins/metabolism , DNA-Binding Proteins/genetics , Algorithms , Databases, Protein , Protein Binding , Computational Biology/methodsABSTRACT
BACKGROUND: Intrinsically disordered proteins (IDPs) lack a stable three-dimensional structure under physiological conditions but play crucial roles in many biological processes. Intrinsically disordered proteins perform various biological functions by interacting with other ligands. RESULTS: Here, we present a database, IDPsBind, which displays interacting sites between IDPs and interacting ligands by using the distance threshold method in known 3D structure IDPs complexes from the PDB database. IDPsBind contains 9626 IDPs complexes and 880 intrinsically disordered proteins verified by experiments. The current release of the IDPsBind database is defined as version 1.0. IDPsBind is freely accessible at http://www.s-bioinformatics.cn/idpsbind/home/ . CONCLUSIONS: IDPsBind provides more comprehensive interaction sites for IDPs complexes of known 3D structures. It can not only help the subsequent studies of the interaction mechanism of intrinsically disordered proteins but also provides a suitable background for developing the algorithms for predicting the interaction sites of intrinsically disordered proteins.
