Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation
Abstract Periodontal inflammation is a chronic condition affecting the tissues surrounding teeth. Initiated by dental plaque, it triggers an immune response leading to tissue destruction. The AIM-2 inflammasome regulates this response, and understanding its peptide sequences could aid in developing...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93409-3 |
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| author | Pradeep Kumar Yadalam Deepavalli Arumuganainar Prabhu Manickam Natarajan Carlos M. Ardila |
| author_facet | Pradeep Kumar Yadalam Deepavalli Arumuganainar Prabhu Manickam Natarajan Carlos M. Ardila |
| author_sort | Pradeep Kumar Yadalam |
| collection | DOAJ |
| description | Abstract Periodontal inflammation is a chronic condition affecting the tissues surrounding teeth. Initiated by dental plaque, it triggers an immune response leading to tissue destruction. The AIM-2 inflammasome regulates this response, and understanding its peptide sequences could aid in developing targeted therapeutics. This study explores using transformers and graph attention networks (GAT) to treat periodontal inflammation. UniProt was used to download AIM-2 inflammasome proteins and FASTA sequences with 100%, 90%, and 50% similarity. DeepBio, a web service for developing deep-learning architectures, analyzed these sequences. Peptide sequence prediction methods were evaluated using a transformer, RNN-CNN, and GAT models. The transformer model achieved 84% accuracy, the GAT model 86%, and the RNN-CNN 64%. Both transformer and GAT models predicted peptide sequences more effectively than the RNN-CNN model, with the Transformer showing the highest class accuracy at 85%, followed by the GAT model at 80%. Models exhibited varying sensitivity and specificity, with the Transformer demonstrating superior performance in overall and class-specific peptide sequence prediction. AI-based peptide sequence prediction using transformers, GAT, and RNN-CNN shows promise for accurately predicting AIM-2 peptide sequences, with transformers and GAT outperforming RNN-CNN in accuracy and class accuracy. |
| format | Article |
| id | doaj-art-b505e741ae1f4dc4ba4b6ea6fa00a6f5 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b505e741ae1f4dc4ba4b6ea6fa00a6f52025-08-20T02:56:15ZengNature PortfolioScientific Reports2045-23222025-03-0115111210.1038/s41598-025-93409-3Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammationPradeep Kumar Yadalam0Deepavalli Arumuganainar1Prabhu Manickam Natarajan2Carlos M. Ardila3Department of Periodontics, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospital, Saveetha UniversityDepartment of Periodontics, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospital, Saveetha UniversityDepartment of Clinical Sciences, Center of Medical and Bio-allied Health Sciences and Research, College of Dentistry, Ajman UniversityDepartment of Basic Sciences, Faculty of Dentistry, Universidad de Antioquia U de AAbstract Periodontal inflammation is a chronic condition affecting the tissues surrounding teeth. Initiated by dental plaque, it triggers an immune response leading to tissue destruction. The AIM-2 inflammasome regulates this response, and understanding its peptide sequences could aid in developing targeted therapeutics. This study explores using transformers and graph attention networks (GAT) to treat periodontal inflammation. UniProt was used to download AIM-2 inflammasome proteins and FASTA sequences with 100%, 90%, and 50% similarity. DeepBio, a web service for developing deep-learning architectures, analyzed these sequences. Peptide sequence prediction methods were evaluated using a transformer, RNN-CNN, and GAT models. The transformer model achieved 84% accuracy, the GAT model 86%, and the RNN-CNN 64%. Both transformer and GAT models predicted peptide sequences more effectively than the RNN-CNN model, with the Transformer showing the highest class accuracy at 85%, followed by the GAT model at 80%. Models exhibited varying sensitivity and specificity, with the Transformer demonstrating superior performance in overall and class-specific peptide sequence prediction. AI-based peptide sequence prediction using transformers, GAT, and RNN-CNN shows promise for accurately predicting AIM-2 peptide sequences, with transformers and GAT outperforming RNN-CNN in accuracy and class accuracy.https://doi.org/10.1038/s41598-025-93409-3Aim-2Graph attention networksRNN-CNNTransformersPeriodontitisInflammasome |
| spellingShingle | Pradeep Kumar Yadalam Deepavalli Arumuganainar Prabhu Manickam Natarajan Carlos M. Ardila Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation Scientific Reports Aim-2 Graph attention networks RNN-CNN Transformers Periodontitis Inflammasome |
| title | Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation |
| title_full | Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation |
| title_fullStr | Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation |
| title_full_unstemmed | Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation |
| title_short | Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation |
| title_sort | artificial intelligence powered prediction of aim 2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation |
| topic | Aim-2 Graph attention networks RNN-CNN Transformers Periodontitis Inflammasome |
| url | https://doi.org/10.1038/s41598-025-93409-3 |
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