Integrating Protein Language Models with Multimodal Embeddings to Accelerate Function Prediction of Uncharacterized...

Area of Science:

• Biotechnology and Biomedicine
• Computational Biology
• Bioinformatics

Background:

• The rapid growth of genomic data outpaces experimental functional annotation, creating a significant sequence-function gap.
• Traditional homology-based tools struggle with this data deluge, limited by sequence identity thresholds.
• Existing protein language models (PLMs) often use limited, unimodal frameworks for function prediction.

Purpose of the Study:

• To review and elucidate multimodal strategies for protein function annotation.
• To explore the synergistic integration of 3D geometry, chemical semantics, and knowledge graphs with PLMs.
• To assess the translational potential and identify bottlenecks in advanced protein function prediction.

Main Methods:

• Systematic review of multimodal global-fusion approaches for protein function annotation.
• Integration of PLMs with 3D structural features, chemical reaction logic, and textual knowledge graphs.
• Analysis of complementary mechanisms and integration strategies for fine-grained annotation.

Main Results:

• Multimodal integration around PLMs surpasses single-sequence method performance for protein function annotation.
• Demonstrates synergistic benefits of combining geometric, chemical, and textual data.
• Identifies key challenges including activity cliffs, transition-state inference, and dynamics.

Conclusions:

• Multimodal frameworks offer a powerful approach to overcome the sequence-function gap.
• Integrating physics-informed machine learning and dynamics-aware architectures is crucial for mechanism-level understanding.
• Future directions focus on causal inference and dynamic protein behavior for enhanced biological applications.