ArticleInternational dental journal2026
Graph-Contrastive Convolutional Neural Network for Extracting and Classifying Peptide-Based Periodontal Immunomodulatory and Anti-Inflammatory Signatures.
Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionPeriodontitis is a chronic inflammatory disease that occurs when the body's immune system fails to respond properly to microbial biofilms. Peptides exhibiting anti-inflammatory and immunomodulatory properties are promising as biomarkers and therapeutic agents; however, identifying functional peptide signatures remains a significant challenge due to sequence variability and limited data availability. The aim of this study was to develop a graph-contrastive convolutional neural network (GCCNN) framework for accurately categorizing periodontitis-related peptides into immunomodulatory and anti-inflammatory classes, to enhance model interpretability, and facilitate customized peptide-based therapies.
methodsIn this research, we introduce a Graph-Contrastive Convolutional Neural Network (GCCNN) framework for classifying peptide sequences relevant to periodontal health into anti-inflammatory peptides (AIPs) and immunomodulatory peptides (IMPs). Our model combines a 1D CNN encoder with SimCLR-style contrastive learning to obtain motif-level representations from peptide sequences that remain unchanged. To fix a big class imbalance, pre-trained representations are fine-tuned using supervised learning with class-weighted binary cross-entropy.
resultsOn a curated peptide dataset, GCCNN performed well with AIPs (F1 = 0.94, ROC-AUC = 0.98), but struggled to generalize IMP predictions due to insufficient data (F1 = 0.04, ROC-AUC = 0.54). Using t-SNE to visualize the latent space revealed that AIP clusters were closely grouped, whereas IMP embeddings were more widely dispersed. SHAP-based interpretation revealed conserved sequence motifs that contribute to AIP predictions, highlighting the model's transparency.
conclusionThis study demonstrates the effectiveness of contrastive learning for peptide signature extraction and provides a foundation for improving minority-class peptide classification in biomedical sequence tasks.
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