MkAtt-SDN2GO: Multi-kernel Attentive-SDN2GO Network for Protein Function Prediction in Humans

Accurately annotating the functions of uncharacterised human proteins remains a major bottleneck in biology. We present MkAtt–SDN2GO , a neural architecture that extends SDN2GO by integrating adaptive multi-kernel convolution and attention mechanisms to predict Gene Ontology terms from protein sequences, domains, and protein–protein interaction (PPI) context. The sequence stream employs a learnable multi-kernel convolution layer that combines features from multiple kernel sizes through attention-based gating, enabling adaptive motif detection without relying on a fixed receptive field. A self-attention layer models long-range dependencies, while cross-attention integrates sequence, domain, and PPI representations into a unified prediction space. On a CAFA-style benchmark, MkAtt-SDN2GO improves Molecular Function (MF) F _max by 14.8% (0.657 vs 0.572) and Recall _max by 18.8% over SDN2GO. Across Homo sapiens , the fused model achieves top F _max scores in Biological Process (BP) (0.441), MF (0.657), and Cellular Component (CC) (0.522) compared with other methods. Although the domain-only stream performs strongly, the cross-attention fusion enhances robustness and interpretability when individual modalities are weak or missing. Overall, adaptive multi-scale convolution combined with attention thus advances large-scale protein annotation and offers a scalable and potential tool for functional genomics and disease research.