DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network

• DOI: 10.1093/bioinformatics/btz464
• Publication Date: 2019-06-05T19:18:49Z
• Authors: Qiang Shi; Weiya Chen; Siqi Huang; Fanglin Jin; Yinghao Dong; Yan Wang; Zhidong Xue
• Keywords: Machine Learning; Deep Learning; Protein Domains; 0206 medical engineering; Proteins; Neural Networks, Computer; 02 engineering and technology; Software

AbstractMotivationAccurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine-learning methods are widely used to predict domain boundary, these approaches often ignore long-range interactions among residues, which have been proven to improve the prediction performance. However, how to simultaneously model the local and global interactions to further improve domain boundary prediction is still a challenging problem.ResultsThis article employs a hybrid deep learning method that combines convolutional neural network and gate recurrent units’ models for domain boundary prediction. It not only captures the local and non-local interactions, but also fuses these features for prediction. Additionally, we adopt balanced Random Forest for classification to deal with high imbalance of samples and high dimensions of deep features. Experimental results show that our proposed approach (DNN-Dom) outperforms existing machine-learning-based methods for boundary prediction. We expect that DNN-Dom can be useful for assisting protein structure and function prediction.Availability and implementationThe method is available as DNN-Dom Server at http://isyslab.info/DNN-Dom/.Supplementary informationSupplementary data are available at Bioinformatics online.