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STRUCTURAL BIOLOGY

Rapid protein model refinement by deep learning

Nature Computational Science volume 1, pages 456–457 (2021)Cite this article

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A graph-neural-network-based framework is proposed for the refinement of protein structure models, substantially improving the efficacy and efficiency of refining protein models when compared with the state-of-the-art approaches.

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Fig. 1: Comparison of the GNN approach to alternative deep learning approaches.

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Author information

Authors and Affiliations

  1. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Osama Abdin & Philip M. Kim

  2. Donnelly Centre, University of Toronto, Toronto, Ontario, Canada

    Philip M. Kim

  3. Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

    Philip M. Kim

Authors
  1. Osama Abdin
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  2. Philip M. Kim
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Corresponding author

Correspondence to Philip M. Kim.

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Competing interests

P.M.K. is a co-founder and has been consultant to several biotechnology ventures, including Resolute Bio, and serves on the scientific advisory board of ProteinQure. He also holds several patents in the area of protein and peptide engineering. O.A. declares no competing interests.

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Abdin, O., Kim, P.M. Rapid protein model refinement by deep learning. Nat Comput Sci 1, 456–457 (2021). https://doi.org/10.1038/s43588-021-00104-0

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