UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides
Abstract Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose UniAMP, a systematic prediction framework...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06033-3 |
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| _version_ | 1841544230588121088 |
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| author | Zixin Chen Chengming Ji Wenwen Xu Jianfeng Gao Ji Huang Huanliang Xu Guoliang Qian Junxian Huang |
| author_facet | Zixin Chen Chengming Ji Wenwen Xu Jianfeng Gao Ji Huang Huanliang Xu Guoliang Qian Junxian Huang |
| author_sort | Zixin Chen |
| collection | DOAJ |
| description | Abstract Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose UniAMP, a systematic prediction framework for discovering AMPs. We observe that feature vectors used in various existing studies constructed from peptide information, such as sequence, composition, and structure, can be augmented and even replaced by information inferred by deep learning models. Specifically, we use a feature vector with 2924 values inferred by two deep learning models, UniRep and ProtT5, to demonstrate that such inferred information of peptides suffice for the task, with the help of our proposed deep neural network model composed of fully connected layers and transformer encoders for predicting the antibacterial activity of peptides. Evaluation results demonstrate superior performance of our proposed model on both balanced benchmark datasets and imbalanced test datasets compared with existing studies. Subsequently, we analyze the relations among peptide sequences, manually extracted features, and automatically inferred information by deep learning models, leading to observations that the inferred information is more comprehensive and non-redundant for the task of predicting AMPs. Moreover, this approach alleviates the impact of the scarcity of positive data and demonstrates great potential in future research and applications. |
| format | Article |
| id | doaj-art-40330b9f0efa4550a6f4475bb482a1c7 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-40330b9f0efa4550a6f4475bb482a1c72025-01-12T12:41:54ZengBMCBMC Bioinformatics1471-21052025-01-0126112210.1186/s12859-025-06033-3UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptidesZixin Chen0Chengming Ji1Wenwen Xu2Jianfeng Gao3Ji Huang4Huanliang Xu5Guoliang Qian6Junxian Huang7College of Artificial Intelligence, Nanjing Agricultural UniversityCollege of Artificial Intelligence, Nanjing Agricultural UniversityCollege of Artificial Intelligence, Nanjing Agricultural UniversityStarHelix IncCollege of Agriculture, Nanjing Agricultural UniversityCollege of Artificial Intelligence, Nanjing Agricultural UniversityCollege of Plant Protection, Nanjing Agricultural UniversityCollege of Artificial Intelligence, Nanjing Agricultural UniversityAbstract Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose UniAMP, a systematic prediction framework for discovering AMPs. We observe that feature vectors used in various existing studies constructed from peptide information, such as sequence, composition, and structure, can be augmented and even replaced by information inferred by deep learning models. Specifically, we use a feature vector with 2924 values inferred by two deep learning models, UniRep and ProtT5, to demonstrate that such inferred information of peptides suffice for the task, with the help of our proposed deep neural network model composed of fully connected layers and transformer encoders for predicting the antibacterial activity of peptides. Evaluation results demonstrate superior performance of our proposed model on both balanced benchmark datasets and imbalanced test datasets compared with existing studies. Subsequently, we analyze the relations among peptide sequences, manually extracted features, and automatically inferred information by deep learning models, leading to observations that the inferred information is more comprehensive and non-redundant for the task of predicting AMPs. Moreover, this approach alleviates the impact of the scarcity of positive data and demonstrates great potential in future research and applications.https://doi.org/10.1186/s12859-025-06033-3Antimicrobial peptidesDeep learningFeature extractionProtein language model |
| spellingShingle | Zixin Chen Chengming Ji Wenwen Xu Jianfeng Gao Ji Huang Huanliang Xu Guoliang Qian Junxian Huang UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides BMC Bioinformatics Antimicrobial peptides Deep learning Feature extraction Protein language model |
| title | UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides |
| title_full | UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides |
| title_fullStr | UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides |
| title_full_unstemmed | UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides |
| title_short | UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides |
| title_sort | uniamp enhancing amp prediction using deep neural networks with inferred information of peptides |
| topic | Antimicrobial peptides Deep learning Feature extraction Protein language model |
| url | https://doi.org/10.1186/s12859-025-06033-3 |
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