A sequence-based multiple kernel model for identifying DNA-binding proteins

• Published in: BMC bioinformatics Vol. 22; no. 1; pp. 1 - 291
• Main Authors: Qian, Yuqing, Jiang, Limin, Ding, Yijie, Tang, Jijun, Guo, Fei
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 31.05.2021; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Amino acid sequence; Amino acids; Binding; Centered kernel alignment; Datasets; Deoxyribonucleic acid; DNA; DNA binding proteins; DNA methylation; DNA-binding protein; Experimental methods; Feature extraction; Identification and classification; Kernels; Learning algorithms; Machine learning; Methods; Model accuracy; Multiple kernel learning; Nucleotide sequence; Prediction models; Proteins; Support vector machine; Support vector machines; China
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-020-03875-x

DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting DNA-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. The integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 ([formula omitted]) and PDB186 ([formula omitted]), respectively. Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.