1. Computational and Systems Biology

ProteInfer, deep neural networks for protein functional inference

  1. Theo Sanderson  Is a corresponding author
  2. Maxwell L Bileschi
  3. David Belanger
  4. Lucy J Colwell
  1. The Francis Crick Institute, United Kingdom
  2. Google AI, United States
https://doi.org/10.7554/eLife.80942
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  1. Theo Sanderson
  2. Maxwell L Bileschi
  3. David Belanger
  4. Lucy J Colwell
(2023)
ProteInfer, deep neural networks for protein functional inference
eLife 12:e80942.
https://doi.org/10.7554/eLife.80942
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Abstract

Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large databases of individual protein sequences. Here we introduce ProteInfer, which instead employs deep convolutional neural networks to directly predict a variety of protein functions - EC numbers and GO terms - directly from an unaligned amino acid sequence. This approach provides precise predictions which complement alignment-based methods, and the computational efficiency of a single neural network permits novel and lightweight software interfaces, which we demonstrate with an in-browser graphical interface for protein function prediction in which all computation is performed on the user's personal computer with no data uploaded to remote servers. Moreover, these models place full-length amino acid sequences into a generalised functional space, facilitating downstream analysis and interpretation. To read the interactive version of this paper, please visit https://google-research.github.io/proteinfer/.

Data availability

Source code is available on GitHub from https://github.com/google-research/proteinfer. Processed TensorFlow files are available from the indicated URLs. Raw training data is from UniProt.

The following previously published data sets were used

Article and author information

Author details

  1. Theo Sanderson

    The Francis Crick Institute, London, United Kingdom
    For correspondence
    theo.sanderson@crick.ac.uk
    Competing interests
    Theo Sanderson, performed research as part of their employment at Google LLC. Google is a technology company that sells machine learning services as part of its business. Portions of this work are covered by US patent WO2020210591A1, filed by Google..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4177-2851

  2. Maxwell L Bileschi

    Google AI, Boston, United States
    Competing interests
    Maxwell L Bileschi, performed research as part of their employment at Google LLC. Google is a technology company that sells machine learning services as part of its business. Portions of this work are covered by US patent WO2020210591A1, filed by Google..

  3. David Belanger

    Google AI, Boston, United States
    Competing interests
    David Belanger, performed research as part of their employment at Google LLC. Google is a technology company that sells machine learning services as part of its business. Portions of this work are covered by US patent WO2020210591A1, filed by Google..

  4. Lucy J Colwell

    Google AI, Boston, United States
    Competing interests
    Lucy J Colwell, performed research as part of their employment at Google LLC. Google is a technology company that sells machine learning services as part of its business. Portions of this work are covered by US patent WO2020210591A1, filed by Google..

Funding

Google

  • Theo Sanderson
  • Maxwell L Bileschi
  • David Belanger
  • Lucy J Colwell

The authors were employed by the funder while completing this work.

Copyright

© 2023, Sanderson et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Theo Sanderson
  2. Maxwell L Bileschi
  3. David Belanger
  4. Lucy J Colwell
(2023)
ProteInfer, deep neural networks for protein functional inference
eLife 12:e80942.
https://doi.org/10.7554/eLife.80942

Share this article

https://doi.org/10.7554/eLife.80942

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