iNClassSec-ESM: Discovering potential non-classical secreted proteins through a novel protein language model
Non-classical secreted proteins (NCSPs) are a class of proteins lacking signal peptides, secreted by Gram-positive bacteria through non-classical secretion pathways. With the increasing demand for highly secreted proteins in recent years, non-classical secretion pathways have received more attention...
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S200103702500114X |
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| author | Yizhou Shao Taigang Liu |
| author_facet | Yizhou Shao Taigang Liu |
| author_sort | Yizhou Shao |
| collection | DOAJ |
| description | Non-classical secreted proteins (NCSPs) are a class of proteins lacking signal peptides, secreted by Gram-positive bacteria through non-classical secretion pathways. With the increasing demand for highly secreted proteins in recent years, non-classical secretion pathways have received more attention due to their advantages over classical secretion pathways (Sec/Tat). However, because the mechanisms of non-classical secretion pathways are not yet clear, identifying NCSPs through biological experiments is expensive and time-consuming, making it imperative to develop computational methods to address this issue. Existing NCSP prediction methods mainly use traditional handcrafted features to represent proteins from sequence information, which limits the models' ability to capture complex protein characteristics. In this study, we proposed a novel NCSP predictor, iNClassSec-ESM, which combined deep learning with traditional classifiers to enhance prediction performance. iNClassSec-ESM integrates an XGBoost model trained on comprehensive handcrafted features and a Deep Neural Network (DNN) trained on hidden layer embeddings from the protein language model (PLM) ESM3. The ESM3 is the recently proposed multimodal PLM and has not yet been fully explored in terms of protein representation. Therefore, we extracted hidden layer embeddings from ESM3 as inputs for multiple classifiers and deep learning networks, and compared them with existing PLMs. Benchmark experiments indicate that iNClassSec-ESM outperforms most of existing methods across multiple performance metrics and could serve as an effective tool for discovering potential NCSPs. Additionally, the ESM3 hidden layer embeddings, as an innovative protein representation method, show great potential for the application in broader protein-related classification tasks. The source code of iNClassSec-ESM and the ESM3 embeddings extraction script are publicly available at https://github.com/AmamiyaHoshie/iNClassSec-ESM/. |
| format | Article |
| id | doaj-art-cf125a1e6d934c1e9cabca09310b037e |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-cf125a1e6d934c1e9cabca09310b037e2025-08-20T03:09:47ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01271350135810.1016/j.csbj.2025.03.043iNClassSec-ESM: Discovering potential non-classical secreted proteins through a novel protein language modelYizhou Shao0Taigang Liu1College of Information Technology, Shanghai Ocean University, Shanghai, 201306, ChinaCorresponding author.; College of Information Technology, Shanghai Ocean University, Shanghai, 201306, ChinaNon-classical secreted proteins (NCSPs) are a class of proteins lacking signal peptides, secreted by Gram-positive bacteria through non-classical secretion pathways. With the increasing demand for highly secreted proteins in recent years, non-classical secretion pathways have received more attention due to their advantages over classical secretion pathways (Sec/Tat). However, because the mechanisms of non-classical secretion pathways are not yet clear, identifying NCSPs through biological experiments is expensive and time-consuming, making it imperative to develop computational methods to address this issue. Existing NCSP prediction methods mainly use traditional handcrafted features to represent proteins from sequence information, which limits the models' ability to capture complex protein characteristics. In this study, we proposed a novel NCSP predictor, iNClassSec-ESM, which combined deep learning with traditional classifiers to enhance prediction performance. iNClassSec-ESM integrates an XGBoost model trained on comprehensive handcrafted features and a Deep Neural Network (DNN) trained on hidden layer embeddings from the protein language model (PLM) ESM3. The ESM3 is the recently proposed multimodal PLM and has not yet been fully explored in terms of protein representation. Therefore, we extracted hidden layer embeddings from ESM3 as inputs for multiple classifiers and deep learning networks, and compared them with existing PLMs. Benchmark experiments indicate that iNClassSec-ESM outperforms most of existing methods across multiple performance metrics and could serve as an effective tool for discovering potential NCSPs. Additionally, the ESM3 hidden layer embeddings, as an innovative protein representation method, show great potential for the application in broader protein-related classification tasks. The source code of iNClassSec-ESM and the ESM3 embeddings extraction script are publicly available at https://github.com/AmamiyaHoshie/iNClassSec-ESM/.http://www.sciencedirect.com/science/article/pii/S200103702500114XNon-classical secreted proteinProtein language modelEmbeddingsEnsemble learningProtein representation |
| spellingShingle | Yizhou Shao Taigang Liu iNClassSec-ESM: Discovering potential non-classical secreted proteins through a novel protein language model Computational and Structural Biotechnology Journal Non-classical secreted protein Protein language model Embeddings Ensemble learning Protein representation |
| title | iNClassSec-ESM: Discovering potential non-classical secreted proteins through a novel protein language model |
| title_full | iNClassSec-ESM: Discovering potential non-classical secreted proteins through a novel protein language model |
| title_fullStr | iNClassSec-ESM: Discovering potential non-classical secreted proteins through a novel protein language model |
| title_full_unstemmed | iNClassSec-ESM: Discovering potential non-classical secreted proteins through a novel protein language model |
| title_short | iNClassSec-ESM: Discovering potential non-classical secreted proteins through a novel protein language model |
| title_sort | inclasssec esm discovering potential non classical secreted proteins through a novel protein language model |
| topic | Non-classical secreted protein Protein language model Embeddings Ensemble learning Protein representation |
| url | http://www.sciencedirect.com/science/article/pii/S200103702500114X |
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