FANTASIA leverages language models to decode the functional dark proteome across the animal tree of life
Abstract Protein functional annotation is crucial in biology, but many protein-coding genes remain uncharacterized, especially in non-model organisms. FANTASIA (Functional ANnoTAtion based on embedding space SImilArity) integrates protein language models for large-scale functional annotation. Applie...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-08651-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Protein functional annotation is crucial in biology, but many protein-coding genes remain uncharacterized, especially in non-model organisms. FANTASIA (Functional ANnoTAtion based on embedding space SImilArity) integrates protein language models for large-scale functional annotation. Applied to ~1000 animal proteomes, FANTASIA predicts functions to virtually all proteins, including up to 50% that remained unannotated by traditional homology-based methods. This enables the discovery of novel gene functions, enhancing our understanding of molecular evolution and organismal biology. FANTASIA holds particular promise for functional discovery in non-model taxa, offering advantages over homology-based tools in sensitivity and generalizability. FANTASIA is available on GitHub at https://github.com/CBBIO/FANTASIA . |
|---|---|
| ISSN: | 2399-3642 |