Context-aware geometric deep learning for protein sequence design

Lucien F. Krapp, Fernando A. Meireles, Luciano A. Abriata, Jean Devillard, Sarah Vacle, Maria J. Marcaida and Matteo Dal Peraro ()
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Lucien F. Krapp: Ecole Fédérale de Lausanne (EPFL)
Fernando A. Meireles: Ecole Fédérale de Lausanne (EPFL)
Luciano A. Abriata: Ecole Fédérale de Lausanne (EPFL)
Jean Devillard: Ecole Fédérale de Lausanne (EPFL)
Sarah Vacle: Ecole Fédérale de Lausanne (EPFL)
Maria J. Marcaida: Ecole Fédérale de Lausanne (EPFL)
Matteo Dal Peraro: Ecole Fédérale de Lausanne (EPFL)

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning approach based solely on a geometric transformer of atomic coordinates and element names that predicts protein sequences from backbone scaffolds aware of the restraints imposed by diverse molecular environments. To validate the method, we show that it can produce highly thermostable, catalytically active enzymes with high success rates. This concept is anticipated to improve the versatility of protein design pipelines for crafting desired functions.

Date: 2024
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DOI: 10.1038/s41467-024-50571-y

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