Portrait de David Heurtel--Depeiges

David Heurtel--Depeiges

david.heurtel-depeiges@mila.quebec
Doctorat - Polytechnique
Superviseur⋅e principal⋅e
Sarath Chandar
Sujets de recherche
IA pour la science
Sécurité de l'IA
Traitement du langage naturel
Site web
Google Scholar
GitHub

Publications

pLM representations unlock metagenomic space beyond homology
Lola Le Breton
David Heurtel-Depeiges
Douglas C. Millar
Lara E. Zetzsche
Robert M. Vernon
Christopher J. Langmead
Sarath Chandar
Quentin Fournier
Metagenomic sequencing has uncovered billions of proteins from uncultured microorganisms, vastly expanding the known protein space. Yet most… (voir plus) remain functionally inaccessible because existing annotation methods depend on close homologs or accurate structure predictions. Here, we show that protein language models (pLMs) can unlock this diversity only when their training data are appropriately curated. We introduce Residue Embedding Diversity (RED), a metric for protein quality assessment orders of magnitude cheaper than likelihood, and a calibration task that measures model alignment with natural evolutionary distributions. We discover a fundamental trade-off between evolutionary calibration and structural modeling, establishing training data composition as a primary determinant of pLM behavior. Finally, we successfully retrieve diverse enzyme candidates from billions of metagenomic sequences and validate their expression in vivo .
2026-07-29
bioRxiv (prépublication)
doi.org
Structure-Aligned Protein Language Model
Can Chen
David Heurtel-Depeiges
Robert M. Vernon
Christopher J. Langmead
Yoshua Bengio
Quentin Fournier
2025-05-21
ArXiv (prépublication)
doi.org
arxiv.org