ViralMultiNet: A structure-aware multimodal framework for viral protein function prediction in wastewater...

Area of Science:

• Virology
• Bioinformatics
• Genomic Surveillance

Background:

• Accurate viral protein functional annotation is crucial for genomic surveillance.
• Rapid viral evolution and fragmented sequences challenge traditional sequence-only models.
• Existing models often lack interpretability and struggle with complex environmental samples.

Purpose of the Study:

• To develop a structure-aware multimodal framework, ViralMultiNet, for accurate viral protein function prediction.
• To address challenges posed by functional drift and fragmented sequences in wastewater metagenomics.
• To enhance the interpretability and scalability of viral protein analysis.

Main Methods:

• Integrated multi-scale k-mer encodings with UniProt functional semantic embeddings.
• Employed gated multimodal fusion and triple knowledge distillation.
• Optimized training using Low-Rank Adaptation and Flash Attention on a SARS-CoV-2 dataset.

Main Results:

• Achieved high classification performance (macro F1: 0.921, accuracy: 0.928, AUC: 0.983) via cross-validation.
• Distilled student model matched teacher performance with significantly reduced training time (40.4% reduction).
• Model attention mapped to experimentally validated functional domains of the SARS-CoV-2 Spike protein.

Conclusions:

• ViralMultiNet provides a scalable and interpretable solution for viral protein function prediction.
• The framework generalizes across variants and identifies critical biological regions.
• Supports deployment in wastewater-based early warning systems for enhanced pandemic preparedness.