SirtSAGE: A Structural-Gated Graph Attention Framework for the Unified Classification and Evolutionary Mapping of Sirtuin Proteins

Abstract

Sirtuins comprise a group of proteins that play critical roles in regulating gene expression, DNA repair, metabolic homeostasis, cellular stress responses, apoptosis, and aging-related pathways. Accurate classification of sirtuin proteins is important for understanding biological functions and drug development. While foundational models such as DeepSIRT achieved robust performance using one-dimensional Convolutional Neural Networks (1D-CNN) paired with traditional features like PSSM and AAC, these approaches primarily rely on static, manually-engineered representations. Such methods often fail to capture the deep contextual semantics hidden in protein sequences or the dynamic spatial topologies essential for functional specificity. Furthermore, these sequence-centric models remain 'structurally blind', as they lack a mechanism to distinguish between high-confidence functional domains and disordered, non-informative regions. To address these limitations, we introduce SirtSAGE (Sirtuin Structural-Aware Graph-Evolutionary framework), a hybrid architecture designed to distill evolutionary embeddings through physical structural constraints. SirtSAGE integrates the contextual semantics of ESM-2 into a Graph Attention Network (GATv2), where pLDDT scores serve as spatial confidence gates to fine-tune the influence of structural interactions. To bridge the gap between high performance and transparency, we employ Kolmogorov-Arnold Networks (KAN) as a symbolic reasoning layer, enabling the extraction of non-linear functional signatures that are often obscured in standard black-box MLPs. Extensive evaluations on both 5-fold cross-validation and an independent test set demonstrate that SirtSAGE outperforms state-of-the-art 1D-CNN baselines in predictive robustness, despite operating under a substantially more stringent unified multi-class setting. Our analysis reveals that SirtSAGE effectively concentrates attention on the structurally stable 'Active Core' of sirtuins while organically learning a continuous latent space whose spatial organization is qualitatively consistent with the established 4-class phylogenetic classification of mammalian sirtuins. Furthermore, successful orthogonal validation on C. elegans variants highlights the framework's mathematical rigour for cross-species homology mapping. SirtSAGE represents a paradigm shift from implicit pattern recognition toward structurally-grounded, interpretable protein annotation.