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SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions

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  • Lasse M. Blaabjerg

    (University of Copenhagen)

  • Nicolas Jonsson

    (University of Copenhagen)

  • Wouter Boomsma

    (University of Copenhagen)

  • Amelie Stein

    (University of Copenhagen)

  • Kresten Lindorff-Larsen

    (University of Copenhagen)

Abstract

The ability to predict how amino acid changes affect proteins has a wide range of applications including in disease variant classification and protein engineering. Many existing methods focus on learning from patterns found in either protein sequences or protein structures. Here, we present a method for integrating information from sequence and structure in a single model that we term SSEmb (Sequence Structure Embedding). SSEmb combines a graph representation for the protein structure with a transformer model for processing multiple sequence alignments. We show that by integrating both types of information we obtain a variant effect prediction model that is robust when sequence information is scarce. We also show that SSEmb learns embeddings of the sequence and structure that are useful for other downstream tasks such as to predict protein-protein binding sites. We envisage that SSEmb may be useful both for variant effect predictions and as a representation for learning to predict protein properties that depend on sequence and structure.

Suggested Citation

  • Lasse M. Blaabjerg & Nicolas Jonsson & Wouter Boomsma & Amelie Stein & Kresten Lindorff-Larsen, 2024. "SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53982-z
    DOI: 10.1038/s41467-024-53982-z
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    1. Kotaro Tsuboyama & Justas Dauparas & Jonathan Chen & Elodie Laine & Yasser Mohseni Behbahani & Jonathan J. Weinstein & Niall M. Mangan & Sergey Ovchinnikov & Gabriel J. Rocklin, 2023. "Mega-scale experimental analysis of protein folding stability in biology and design," Nature, Nature, vol. 620(7973), pages 434-444, August.
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    3. Umberto Lupo & Damiano Sgarbossa & Anne-Florence Bitbol, 2022. "Protein language models trained on multiple sequence alignments learn phylogenetic relationships," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Jonathan Frazer & Pascal Notin & Mafalda Dias & Aidan Gomez & Joseph K. Min & Kelly Brock & Yarin Gal & Debora S. Marks, 2021. "Disease variant prediction with deep generative models of evolutionary data," Nature, Nature, vol. 599(7883), pages 91-95, November.
    5. Matteo Cagiada & Sandro Bottaro & Søren Lindemose & Signe M. Schenstrøm & Amelie Stein & Rasmus Hartmann-Petersen & Kresten Lindorff-Larsen, 2023. "Discovering functionally important sites in proteins," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
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