pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model
Abstract Background Protein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biologic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-02-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05164-9 |
| _version_ | 1811165681658363904 |
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| author | Pawel Pratyush Suresh Pokharel Hiroto Saigo Dukka B. KC |
| author_facet | Pawel Pratyush Suresh Pokharel Hiroto Saigo Dukka B. KC |
| author_sort | Pawel Pratyush |
| collection | DOAJ |
| description | Abstract Background Protein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO. Results Using an intermediate fusion-based stacked generalization approach, we integrated embeddings from supervised embedding layer and contextualized protein language model (ProtT5) and developed a tool called pLMSNOSite (protein language model-based SNO site predictor). On an independent test set of experimentally identified SNO sites, pLMSNOSite achieved values of 0.340, 0.735 and 0.773 for MCC, sensitivity and specificity respectively. These results show that pLMSNOSite performs better than the compared approaches for the prediction of S-nitrosylation sites. Conclusion Together, the experimental results suggest that pLMSNOSite achieves significant improvement in the prediction performance of S-nitrosylation sites and represents a robust computational approach for predicting protein S-nitrosylation sites. pLMSNOSite could be a useful resource for further elucidation of SNO and is publicly available at https://github.com/KCLabMTU/pLMSNOSite . |
| first_indexed | 2024-04-10T15:40:40Z |
| format | Article |
| id | doaj.art-24358939c36148d098270c815601afab |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-10T15:40:40Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-24358939c36148d098270c815601afab2023-02-12T12:24:24ZengBMCBMC Bioinformatics1471-21052023-02-0124112010.1186/s12859-023-05164-9pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language modelPawel Pratyush0Suresh Pokharel1Hiroto Saigo2Dukka B. KC3Department of Computer Science, Michigan Technological UniversityDepartment of Computer Science, Michigan Technological UniversityDepartment of Electrical Engineering and Computer Science, Kyushu UniversityDepartment of Computer Science, Michigan Technological UniversityAbstract Background Protein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO. Results Using an intermediate fusion-based stacked generalization approach, we integrated embeddings from supervised embedding layer and contextualized protein language model (ProtT5) and developed a tool called pLMSNOSite (protein language model-based SNO site predictor). On an independent test set of experimentally identified SNO sites, pLMSNOSite achieved values of 0.340, 0.735 and 0.773 for MCC, sensitivity and specificity respectively. These results show that pLMSNOSite performs better than the compared approaches for the prediction of S-nitrosylation sites. Conclusion Together, the experimental results suggest that pLMSNOSite achieves significant improvement in the prediction performance of S-nitrosylation sites and represents a robust computational approach for predicting protein S-nitrosylation sites. pLMSNOSite could be a useful resource for further elucidation of SNO and is publicly available at https://github.com/KCLabMTU/pLMSNOSite .https://doi.org/10.1186/s12859-023-05164-9S-nitrosylationDeep learningConvolutional neural networkPost-translational modificationWord embeddingProtein language model |
| spellingShingle | Pawel Pratyush Suresh Pokharel Hiroto Saigo Dukka B. KC pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model BMC Bioinformatics S-nitrosylation Deep learning Convolutional neural network Post-translational modification Word embedding Protein language model |
| title | pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model |
| title_full | pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model |
| title_fullStr | pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model |
| title_full_unstemmed | pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model |
| title_short | pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model |
| title_sort | plmsnosite an ensemble based approach for predicting protein s nitrosylation sites by integrating supervised word embedding and embedding from pre trained protein language model |
| topic | S-nitrosylation Deep learning Convolutional neural network Post-translational modification Word embedding Protein language model |
| url | https://doi.org/10.1186/s12859-023-05164-9 |
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