Cargando…
DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes
The epitope is the site where antigens and antibodies interact and is vital to understanding the immune system. Experimental identification of linear B-cell epitopes (BCEs) is expensive, is labor-consuming, and has a low throughput. Although a few computational methods have been proposed to address...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992402/ https://www.ncbi.nlm.nih.gov/pubmed/36910218 http://dx.doi.org/10.3389/fmicb.2023.1117027 |
_version_ | 1784902304082690048 |
---|---|
author | Qi, Yue Zheng, Peijie Huang, Guohua |
author_facet | Qi, Yue Zheng, Peijie Huang, Guohua |
author_sort | Qi, Yue |
collection | PubMed |
description | The epitope is the site where antigens and antibodies interact and is vital to understanding the immune system. Experimental identification of linear B-cell epitopes (BCEs) is expensive, is labor-consuming, and has a 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 consists 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 training and two testing datasets. The empirical results showed that the DeepLBCEPred obtained 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 at: http://www.biolscience.cn/DeepLBCEPred/. |
format | Online Article Text |
id | pubmed-9992402 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99924022023-03-09 DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes Qi, Yue Zheng, Peijie Huang, Guohua Front Microbiol Microbiology The epitope is the site where antigens and antibodies interact and is vital to understanding the immune system. Experimental identification of linear B-cell epitopes (BCEs) is expensive, is labor-consuming, and has a 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 consists 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 training and two testing datasets. The empirical results showed that the DeepLBCEPred obtained 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 at: http://www.biolscience.cn/DeepLBCEPred/. Frontiers Media S.A. 2023-02-22 /pmc/articles/PMC9992402/ /pubmed/36910218 http://dx.doi.org/10.3389/fmicb.2023.1117027 Text en Copyright © 2023 Qi, Zheng and Huang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Qi, Yue Zheng, Peijie Huang, Guohua DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes |
title | DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes |
title_full | DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes |
title_fullStr | DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes |
title_full_unstemmed | DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes |
title_short | DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes |
title_sort | deeplbcepred: a bi-lstm and multi-scale cnn-based deep learning method for predicting linear b-cell epitopes |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992402/ https://www.ncbi.nlm.nih.gov/pubmed/36910218 http://dx.doi.org/10.3389/fmicb.2023.1117027 |
work_keys_str_mv | AT qiyue deeplbcepredabilstmandmultiscalecnnbaseddeeplearningmethodforpredictinglinearbcellepitopes AT zhengpeijie deeplbcepredabilstmandmultiscalecnnbaseddeeplearningmethodforpredictinglinearbcellepitopes AT huangguohua deeplbcepredabilstmandmultiscalecnnbaseddeeplearningmethodforpredictinglinearbcellepitopes |