FANTASIA leverages language models to decode the functional dark proteome across the animal tree of life

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 .

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Publication Details

Journal
Communications Biology
Published
2025-08-14
DOI
https://doi.org/10.1038/s42003-025-08651-2
Citations
8
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
6.45

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article

FANTASIA leverages language models to decode the functional dark proteome across the animal tree of life

Gemma I. Martínez‐Redondo, Ana M. Rojas, Ildefonso Cases, Rosa Fernández et al.
8 citations
Communications Biology
Machine Learning in Bioinformatics
6.45
article

FANTASIA leverages language models to decode the functional dark proteome across the animal tree of life

Gemma I. Martínez‐Redondo, Ana M. Rojas, Ildefonso Cases, Rosa Fernández, José M. Fernández, Israel Barrios-Núñez, Marçal Vázquez-Valls, Francisco M Pérez-Canales, Belén Carbonetto
article en
8 citations

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 .

Communications BiologyVol. 8(1)
Consejo Superior de Investigaciones Científicas (ES), Barcelona Supercomputing Center (ES), Centro Andaluz de Biología del Desarrollo (ES), Institut de Biologia Evolutiva (ES), Institut de Biologia Molecular de Barcelona (ES), Universitat Politècnica de Catalunya (ES)
European Commission, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, Agencia Estatal de Investigación
Openalex Percentile: Top 3%
Machine Learning in Bioinformatics
6.45
DOIOpen Access PDFOpenAlex
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FANTASIA leverages language models to decode the functional dark proteome across the animal tree of life — Gemma I. Martínez‐Redondo, Ana M. Rojas, et al. · Communications Biology (2025) | TGRS Research Map | TGRS