Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction.
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| Main Authors: | , , , , , |
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| Format: | Artículo científico |
| Language: | en |
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Nature communications
2025
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| _version_ | 1868266247447117825 |
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| author | Peng, Yue Wu, Junze Sun, Yi Zhang, Yuanxing Wang, Qiyao Shao, Shuai |
| author_facet | Peng, Yue Wu, Junze Sun, Yi Zhang, Yuanxing Wang, Qiyao Shao, Shuai Peng, Yue Wu, Junze Sun, Yi Zhang, Yuanxing Wang, Qiyao Shao, Shuai |
| collection | PubMed - marine biology |
| contents | Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction. Peng, Yue Wu, Junze Sun, Yi Zhang, Yuanxing Wang, Qiyao Shao, Shuai Bacterial Proteins Virulence Factors Salmonella typhimurium Computational Biology Enterohemorrhagic Escherichia coli Machine Learning Identifying and characterizing virulence proteins secreted by Gram-negative bacteria are fundamental for deciphering microbial pathogenicity as well as aiding the development of therapeutic strategies. Effector predictors utilizing pre-trained protein language models (PLMs) have shown sound performance by leveraging extensive evolutionary and sequential protein features. However, the accuracy and sensitivity of effector prediction remain challenging. Here, we introduce a model named Contrastive-learning of Language Embedding and Biological Features (CLEF) leveraging contrastive learning to integrate PLM representations with supplementary biological features. Biologically information is captured in learned contextualized embeddings to yield meaningful representations. With cross-modality biological features, CLEF outperforms state-of-the-art (SOTA) models in predicting type III, type IV, and type VI secreted effectors (T3SEs/T4SEs/T6SEs) in enteric pathogens. All experimentally verified effectors in Enterohemorrhagic Escherichia coli and 41 of 43 experimentally verified T3SEs of Salmonella Typhimurium are recognized. Moreover, 12 predicted T3SEs and 11 predicted T6SEs are validated by extensive experiments in Edwardsiella piscicida. Furthermore, integrating omics data via CLEF framework enhances protein representations to illustrate effector-effector interactions and determine in vivo colonization-essential genes. Collectively, CLEF provides a blueprint to bridge the gap between in silico PLM's capacity and experimental biological information to fulfill complicated tasks. |
| format | Artículo científico |
| id | pubmed_39900608 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Nature communications |
| record_format | pubmed |
| spellingShingle | Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction. Peng, Yue Wu, Junze Sun, Yi Zhang, Yuanxing Wang, Qiyao Shao, Shuai Bacterial Proteins Virulence Factors Salmonella typhimurium Computational Biology Enterohemorrhagic Escherichia coli Machine Learning Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction. Peng, Yue Wu, Junze Sun, Yi Zhang, Yuanxing Wang, Qiyao Shao, Shuai Bacterial Proteins Virulence Factors Salmonella typhimurium Computational Biology Enterohemorrhagic Escherichia coli Machine Learning Identifying and characterizing virulence proteins secreted by Gram-negative bacteria are fundamental for deciphering microbial pathogenicity as well as aiding the development of therapeutic strategies. Effector predictors utilizing pre-trained protein language models (PLMs) have shown sound performance by leveraging extensive evolutionary and sequential protein features. However, the accuracy and sensitivity of effector prediction remain challenging. Here, we introduce a model named Contrastive-learning of Language Embedding and Biological Features (CLEF) leveraging contrastive learning to integrate PLM representations with supplementary biological features. Biologically information is captured in learned contextualized embeddings to yield meaningful representations. With cross-modality biological features, CLEF outperforms state-of-the-art (SOTA) models in predicting type III, type IV, and type VI secreted effectors (T3SEs/T4SEs/T6SEs) in enteric pathogens. All experimentally verified effectors in Enterohemorrhagic Escherichia coli and 41 of 43 experimentally verified T3SEs of Salmonella Typhimurium are recognized. Moreover, 12 predicted T3SEs and 11 predicted T6SEs are validated by extensive experiments in Edwardsiella piscicida. Furthermore, integrating omics data via CLEF framework enhances protein representations to illustrate effector-effector interactions and determine in vivo colonization-essential genes. Collectively, CLEF provides a blueprint to bridge the gap between in silico PLM's capacity and experimental biological information to fulfill complicated tasks. |
| title | Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction. |
| topic | Bacterial Proteins Virulence Factors Salmonella typhimurium Computational Biology Enterohemorrhagic Escherichia coli Machine Learning |
| url | https://pubmed.ncbi.nlm.nih.gov/39900608/ |