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Using deep learning to annotate the protein universe


Understanding the relationship between amino acid sequence and protein function is a long-standing challenge with far-reaching scientific and translational implications. State-of-the-art alignment-based techniques cannot predict function for one-third of microbial protein sequences, hampering our ability to exploit data from diverse organisms. Here, we train deep learning models to accurately predict functional annotations for unaligned amino acid sequences across rigorous benchmark assessments built from the 17,929 families of the protein families database Pfam. The models infer known patterns of evolutionary substitutions and learn representations that accurately cluster sequences from unseen families. Combining deep models with existing methods significantly improves remote homology detection, suggesting that the deep models learn complementary information. This approach extends the coverage of Pfam by >9.5%, exceeding additions made over the last decade, and predicts function for 360 human reference proteome proteins with no previous Pfam annotation. These results suggest that deep learning models will be a core component of future protein annotation tools.

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Fig. 1: Model performance on Pfam-seed.
Fig. 2: ProtCNN architecture.
Fig. 3: Clustered split performance of ProtCNN, ProtENN, TPHMM and ProtREP, which uses the learned representation of sequence space.
Fig. 4: A combination of ProtENN and TPHMM improves performance on the remote homology task.

Data availability

The data splits described in this manuscript are available for download at and, and an interactive notebook for data loading is available at Model predictions for Pfam-N are freely available to download as part of the Pfam v.34.0 release from

Code availability

The TensorFlow API, specifically tensorflow-gpu v.1.15.4, was used to implement and train all deep models using the architectures described in the Methods. Code that documents model training using Python v.3.7 is available on GitHub at The training and validation datasets used for creating each model are available as described in the preceding section. Trained models are available in Google Cloud Storage at, including the ensembles trained on the Pfam-seed random split, Pfam-seed clustered split, Pfam-full random split (all Pfam v.32.0) and the models used to generate Pfam-N v.34.0. ProtCNN inference was run using a custom Python script that (1) read in FASTA records and (2) ran inference of the ProtCNN as a TensorFlow SavedModel. An interactive notebook that demonstrates inference using ProtCNN is available at An interactive notebook showing use of the trained models to produce Pfam class predictions as well as embeddings is available in GitHub at


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We thank J. Smith for countless conversations and guidance throughout this project; E. Bixby for an implementation of ragged tensor processing that sped up our ProtCNN implementation substantially on GPU; C. McClean, B. Alipanahi and S. Kearnes for extensive proofreading and feedback and Z. Nado for programming advice. L.J.C. gratefully acknowledges support from the Simons Foundation.

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Authors and Affiliations



M.L.B., D.B., M.A.D. and L.J.C. conceived the study. All authors designed, implemented and used machine learning models to annotate protein domain sequences, analyzed the data and developed the approach used for Pfam-N. M.L.B., D.B. and L.J.C. wrote the paper, with input from all authors.

Corresponding authors

Correspondence to Maxwell L. Bileschi or Lucy J. Colwell.

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Competing interests

M.L.B., D.B., D.H.B., T.S., B.C., D.S., M.A.D. and L.J.C. 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.

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Nature Biotechnology thanks Christian Dallago for their contribution to the peer review of this work.

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Supplementary Figs. 1–13, Methods and Tables 1–19.

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Bileschi, M.L., Belanger, D., Bryant, D.H. et al. Using deep learning to annotate the protein universe. Nat Biotechnol 40, 932–937 (2022).

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