ProteInfer, deep neural networks for protein functional inference
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.
Article and author information
Author details
Funding
- 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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