DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes - Qi, Yue; Zheng, Peijie; Huang, Guohua (Frontiers in Microbiology, 2023)
The epitope is where antigens and antibodies interact and is vital to understanding immune system. Experimental identification of linear B cell epitopes (BCEs) is costly, labor-consuming, and low-throughput. Although a few computational methods have been proposed to address this challenge, there is still a long way to go for practical applications. We proposed a deep learning method called DeepLBCEPred for predicting linear BCEs, which consist of bi-directional long short-term memory (Bi-LSTM), feed-forward attention, and multi-scale convolutional neural networks (CNNs). We extensively tested the performance of DeepLBCEPred through cross-validation and independent tests on a training and two testing datasets. The empirical results showed that the DeepLBCEPred obtained the state-of-the-art performance. We also investigated the contribution of different deep learning elements to recognize linear BCEs. In addition, we have developed a user-friendly web application for linear BCEs prediction, which is freely available for all scientific researchers on http://www.biolscience.cn/DeepLBCEPred/.