Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction
Abstract Background Peptide-based therapeutics have great potential due to their versatility, high specificity, and suitability for a variety of therapeutic applications. Despite these advantages, the inherent toxicities of some peptides pose challenges in drug development. Several computational met...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Biology |
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| Online Access: | https://doi.org/10.1186/s12915-025-02329-1 |
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| author | Shihu Jiao Xiucai Ye Tetsuya Sakurai Quan Zou Wu Han Chao Zhan |
| author_facet | Shihu Jiao Xiucai Ye Tetsuya Sakurai Quan Zou Wu Han Chao Zhan |
| author_sort | Shihu Jiao |
| collection | DOAJ |
| description | Abstract Background Peptide-based therapeutics have great potential due to their versatility, high specificity, and suitability for a variety of therapeutic applications. Despite these advantages, the inherent toxicities of some peptides pose challenges in drug development. Several computational methods have been developed to allow rapid and efficient large-scale screening of peptide toxicity. However, these methods mainly rely on the primary sequence and often ignore critical structural information, which limits their predictive accuracy. Results In this study, we introduce a novel framework named StrucToxNet that integrates a pre-trained protein language model with an equivariant graph neural network to improve peptide toxicity prediction. By combining sequence embeddings from the ProtT5 language model and 3D structural data predicted by ESMFold, StrucToxNet can capture both sequential and spatial characteristics of peptides. Testing on the independent dataset indicates that StrucToxNet outperforms existing sequence-based models in various metrics, achieving higher balanced accuracy and overall performance. Conclusions The results demonstrate the robustness and generalizability of StrucToxNet, marking it a reliable tool in the computational screening of toxic peptides and facilitating safer peptide-based drug development. |
| format | Article |
| id | doaj-art-dca9fc95c5514cf79d2002734e45bda2 |
| institution | Kabale University |
| issn | 1741-7007 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Biology |
| spelling | doaj-art-dca9fc95c5514cf79d2002734e45bda22025-08-20T03:46:29ZengBMCBMC Biology1741-70072025-07-0123111010.1186/s12915-025-02329-1Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity predictionShihu Jiao0Xiucai Ye1Tetsuya Sakurai2Quan Zou3Wu Han4Chao Zhan5Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of ChinaDepartment of Computer Science, University of TsukubaDepartment of Computer Science, University of TsukubaYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of ChinaDepartment of Statistics, Stanford UniversityHarbin Medical University Cancer HospitalAbstract Background Peptide-based therapeutics have great potential due to their versatility, high specificity, and suitability for a variety of therapeutic applications. Despite these advantages, the inherent toxicities of some peptides pose challenges in drug development. Several computational methods have been developed to allow rapid and efficient large-scale screening of peptide toxicity. However, these methods mainly rely on the primary sequence and often ignore critical structural information, which limits their predictive accuracy. Results In this study, we introduce a novel framework named StrucToxNet that integrates a pre-trained protein language model with an equivariant graph neural network to improve peptide toxicity prediction. By combining sequence embeddings from the ProtT5 language model and 3D structural data predicted by ESMFold, StrucToxNet can capture both sequential and spatial characteristics of peptides. Testing on the independent dataset indicates that StrucToxNet outperforms existing sequence-based models in various metrics, achieving higher balanced accuracy and overall performance. Conclusions The results demonstrate the robustness and generalizability of StrucToxNet, marking it a reliable tool in the computational screening of toxic peptides and facilitating safer peptide-based drug development.https://doi.org/10.1186/s12915-025-02329-1PeptidesToxicityStructureProtein language modelEquivariant graph neural networks |
| spellingShingle | Shihu Jiao Xiucai Ye Tetsuya Sakurai Quan Zou Wu Han Chao Zhan Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction BMC Biology Peptides Toxicity Structure Protein language model Equivariant graph neural networks |
| title | Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction |
| title_full | Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction |
| title_fullStr | Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction |
| title_full_unstemmed | Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction |
| title_short | Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction |
| title_sort | integration of pre trained protein language models with equivariant graph neural networks for peptide toxicity prediction |
| topic | Peptides Toxicity Structure Protein language model Equivariant graph neural networks |
| url | https://doi.org/10.1186/s12915-025-02329-1 |
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