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

Abstract

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) Fmax by 14.8% (0.657 vs 0.572) and Recallmax by 18.8% over SDN2GO. Across Homo sapiens, the fused model achieves top Fmax 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.