arXiv:2605.29158v1 Announce Type: new Abstract: Protein homology search underlies function annotation, structure prediction, and evolutionary analysis, but remains challenging in the "twilight zone," where global sequence similarity is weak and classical alignment methods lose sensitivity. Protein language models provide context-aware representations that could improve alignment sensitivity in this regime. However, prior protein embedding-based retrieval pipelines often pool these representations into a single vector, potentially obscuring local motifs, domains, or conserved residues that reve
The increasing sophistication of protein language models (PLMs) and their integration with advanced retrieval techniques allows for more nuanced biological discovery, especially in challenging domains like the 'twilight zone' of protein homology.
Improved protein homology search significantly accelerates drug discovery, enzyme engineering, and fundamental biological research by more accurately identifying functional relationships between proteins.
Classical alignment methods may become less central for protein homology in certain contexts, with protein embedding-based retrieval techniques offering greater sensitivity for distant homologs.
- · Pharmaceutical companies
- · Biotechnology startups
- · Computational biology researchers
- · AI/ML research labs
- · Developers of legacy alignment software
Faster identification of novel protein functions and drug targets.
Reduced R&D costs and accelerated timelines for therapeutic development.
The potential to design de novo proteins with desired functions becomes more feasible, revolutionizing synthetic biology and materials science.
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Read at arXiv cs.LG