Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation

Abstract

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.

Keywords

• Aim-2
• Graph attention networks
• RNN-CNN
• Transformers
• Periodontitis
• Inflammasome