DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction
Abstract Human leukocyte antigen (HLA) is closely involved in regulating the human immune system. Despite great advance in detecting classical HLA Class I binders, there are few methods or toolkits for recognizing non-classical HLA Class I binders. To fill in this gap, we have developed a deep learn...
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-023-09796-2 |
| _version_ | 1797453799367901184 |
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| author | Guohua Huang Xingyu Tang Peijie Zheng |
| author_facet | Guohua Huang Xingyu Tang Peijie Zheng |
| author_sort | Guohua Huang |
| collection | DOAJ |
| description | Abstract Human leukocyte antigen (HLA) is closely involved in regulating the human immune system. Despite great advance in detecting classical HLA Class I binders, there are few methods or toolkits for recognizing non-classical HLA Class I binders. To fill in this gap, we have developed a deep learning-based tool called DeepHLAPred. The DeepHLAPred used electron-ion interaction pseudo potential, integer numerical mapping and accumulated amino acid frequency as initial representation of non-classical HLA binder sequence. The deep learning module was used to further refine high-level representations. The deep learning module comprised two parallel convolutional neural networks, each followed by maximum pooling layer, dropout layer, and bi-directional long short-term memory network. The experimental results showed that the DeepHLAPred reached the state-of-the-art performanceson the cross-validation test and the independent test. The extensive test demonstrated the rationality of the DeepHLAPred. We further analyzed sequence pattern of non-classical HLA class I binders by information entropy. The information entropy of non-classical HLA binder sequence implied sequence pattern to a certain extent. In addition, we have developed a user-friendly webserver for convenient use, which is available at http://www.biolscience.cn/DeepHLApred/ . The tool and the analysis is helpful to detect non-classical HLA Class I binder. The source code and data is available at https://github.com/tangxingyu0/DeepHLApred . |
| first_indexed | 2024-03-09T15:28:05Z |
| format | Article |
| id | doaj.art-3ac0f22c9f384503810b2d22c70adb06 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-03-09T15:28:05Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-3ac0f22c9f384503810b2d22c70adb062023-11-26T12:25:09ZengBMCBMC Genomics1471-21642023-11-0124111610.1186/s12864-023-09796-2DeepHLAPred: a deep learning-based method for non-classical HLA binder predictionGuohua Huang0Xingyu Tang1Peijie Zheng2School of Information Technology and Administration, Hunan University of Finance and EconomicsCollege of Information Science and Engineering, Shaoyang UniversityCollege of Information Science and Engineering, Shaoyang UniversityAbstract Human leukocyte antigen (HLA) is closely involved in regulating the human immune system. Despite great advance in detecting classical HLA Class I binders, there are few methods or toolkits for recognizing non-classical HLA Class I binders. To fill in this gap, we have developed a deep learning-based tool called DeepHLAPred. The DeepHLAPred used electron-ion interaction pseudo potential, integer numerical mapping and accumulated amino acid frequency as initial representation of non-classical HLA binder sequence. The deep learning module was used to further refine high-level representations. The deep learning module comprised two parallel convolutional neural networks, each followed by maximum pooling layer, dropout layer, and bi-directional long short-term memory network. The experimental results showed that the DeepHLAPred reached the state-of-the-art performanceson the cross-validation test and the independent test. The extensive test demonstrated the rationality of the DeepHLAPred. We further analyzed sequence pattern of non-classical HLA class I binders by information entropy. The information entropy of non-classical HLA binder sequence implied sequence pattern to a certain extent. In addition, we have developed a user-friendly webserver for convenient use, which is available at http://www.biolscience.cn/DeepHLApred/ . The tool and the analysis is helpful to detect non-classical HLA Class I binder. The source code and data is available at https://github.com/tangxingyu0/DeepHLApred .https://doi.org/10.1186/s12864-023-09796-2Non-classical HLA class IDeep learningRepresentationInformation entropyConvolutional neural network |
| spellingShingle | Guohua Huang Xingyu Tang Peijie Zheng DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction BMC Genomics Non-classical HLA class I Deep learning Representation Information entropy Convolutional neural network |
| title | DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction |
| title_full | DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction |
| title_fullStr | DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction |
| title_full_unstemmed | DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction |
| title_short | DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction |
| title_sort | deephlapred a deep learning based method for non classical hla binder prediction |
| topic | Non-classical HLA class I Deep learning Representation Information entropy Convolutional neural network |
| url | https://doi.org/10.1186/s12864-023-09796-2 |
| work_keys_str_mv | AT guohuahuang deephlapredadeeplearningbasedmethodfornonclassicalhlabinderprediction AT xingyutang deephlapredadeeplearningbasedmethodfornonclassicalhlabinderprediction AT peijiezheng deephlapredadeeplearningbasedmethodfornonclassicalhlabinderprediction |