title: Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.
abstract: As many structures of protein-DNA complexes have been known in the past years, several computational methods have been developed to predict DNA-binding sites in proteins. However, its inverse problem (i.e., predicting protein-binding sites in DNA) has received much less attention. One of the reasons is that the differences between the interaction propensities of nucleotides are much smaller than those between amino acids. Another reason is that DNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding DNA nucleotides is much harder than predicting DNA-binding amino acids. We computed the interaction propensity (IP) of nucleotide triplets with amino acids using an extensive dataset of protein-DNA complexes, and developed two support vector machine (SVM) models that predict protein-binding nucleotides from sequence data alone. One SVM model predicts protein-binding nucleotides using DNA sequence data alone, and the other SVM model predicts protein-binding nucleotides using both DNA and protein sequences. In a 10-fold cross-validation with 1519 DNA sequences, the SVM model that uses DNA sequence data only predicted protein-binding nucleotides with an accuracy of 67.0%, an F-measure of 67.1%, and a Matthews correlation coefficient (MCC) of 0.340. With an independent dataset of 181 DNAs that were not used in training, it achieved an accuracy of 66.2%, an F-measure 66.3% and a MCC of 0.324. Another SVM model that uses both DNA and protein sequences achieved an accuracy of 69.6%, an F-measure of 69.6%, and a MCC of 0.383 in a 10-fold cross-validation with 1519 DNA sequences and 859 protein sequences. With an independent dataset of 181 DNAs and 143 proteins, it showed an accuracy of 67.3%, an F-measure of 66.5% and a MCC of 0.329. Both in cross-validation and independent testing, the second SVM model that used both DNA and protein sequence data showed better performance than the first model that used DNA sequence data. To the best of our knowledge, this is the first attempt to predict protein-binding nucleotides in a given DNA sequence from the sequence data alone.
authors.affiliation: Institute for Information and Electronics Research, Inha University, Incheon, South Korea.
authors.forename: Byungkyu
authors.initials: B
authors.lastname: Park
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, South Korea.
authors.forename: Jinyong
authors.initials: J
authors.lastname: Im
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, South Korea.
authors.forename: Narankhuu
authors.initials: N
authors.lastname: Tuvshinjargal
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, South Korea.
authors.forename: Wook
authors.initials: W
authors.lastname: Lee
authors.affiliation: Department of Computer Science and Engineering, Inha University, Incheon, South Korea. Electronic address: khan@inha.ac.kr.
authors.forename: Kyungsook
authors.initials: K
authors.lastname: Han
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001665:Binding Sites; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry* / Q000235:genetics*; D004268:DNA-Binding Proteins / Q000737:chemistry* / Q000235:genetics*; D008956:Models, Chemical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D011485:Protein Binding; D017422:Sequence Analysis, DNA / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D004268:DNA-Binding Proteins; D004247:DNA
keywords: Binding sites; DNA–protein interactions; Prediction model; Protein-binding nucleotides