Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features

• Published in: Scientific reports Vol. 14; no. 1; p. 2961
• Main Authors: Mahmud, S. M. Hasan, Goh, Kah Ong Michael, Hosen, Md. Faruk, Nandi, Dip, Shoombuatong, Watshara
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.02.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/2397; 631/1647; Algorithms; Amino acid sequence; Antibiotics; Antineoplastic drugs; Computational Biology - methods; Deep Learning; Deoxyribonucleic acid; DNA; DNA repair; DNA-binding protein; DNA-Binding Proteins; Embedding; Experimental methods; Humanities and Social Sciences; Machine Learning; multidisciplinary; Neural networks; Neural Networks, Computer; Proteomics; Science; Science (multidisciplinary); Steroid hormones
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-52653-9

DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.