ProteInfer, deep neural networks for protein functional inference

Abstract
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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

1. The UniProt Consortium
(2021) UniProt: the universal protein knowledgebase
10.1093/nar/gkaa1100.

https://www.uniprot.org/

Article and author information

Author details

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..

"This ORCID iD identifies the author of this article:" 0000-0003-4177-2851
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..
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..
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

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.