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Clustering FunFams using sequence embeddings improves EC purity

View ORCID ProfileMaria Littmann, View ORCID ProfileNicola Bordin, View ORCID ProfileMichael Heinzinger, View ORCID ProfileChristine Orengo, View ORCID ProfileBurkhard Rost
doi: https://doi.org/10.1101/2021.01.21.427551
Maria Littmann
1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
2TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
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  • For correspondence: littmann@rostlab.org assistant@rostlab.org c.orengo@ucl.ac.uk
Nicola Bordin
3Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
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Michael Heinzinger
1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
2TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
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Christine Orengo
3Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
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  • ORCID record for Christine Orengo
  • For correspondence: littmann@rostlab.org assistant@rostlab.org c.orengo@ucl.ac.uk
Burkhard Rost
1TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
4Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
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Abstract

Motivation Classifying proteins into functional families can improve our understanding of a protein’s function and can allow transferring annotations within the same family. Toward this end, functional families need to be “pure”, i.e., contain only proteins with identical function. Functional Families (FunFams) cluster proteins within CATH superfamilies into such groups of proteins sharing function, based on differentially conserved residues. 11% of all FunFams (22,830 of 203,639) also contain EC annotations and of those, 7% (1,526 of 22,830) have at least two different EC annotations, i.e., inconsistent functional annotations.

Results We propose an approach to further cluster FunFams into smaller and functionally more consistent sub-families by encoding their sequences through embeddings. These embeddings originate from deep learned language models (LMs) transferring the knowledge gained from predicting missing amino acids in a sequence (ProtBERT) and have been further optimized to distinguish between proteins belonging to the same or a different CATH superfamily (PB-Tucker). Using distances between sequences in embedding space and DBSCAN to cluster FunFams, as well as identify outlier sequences, resulted in twice as many more pure clusters per FunFam than for a random clustering. 52% of the impure FunFams were split into pure clusters, four times more than for random. While functional consistency was mainly measured using EC annotations, we observed similar results for binding annotations. Thus, we expect an increased purity also for other definitions of function. Our results can help generating FunFams; the resulting clusters with improved functional consistency can be used to infer annotations more reliably. We expect this approach to succeed equally for any other grouping of proteins by their phenotypes.

Availability The source code and PB-Tucker embeddings are available via GitHub: https://github.com/Rostlab/FunFamsClustering

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/Rostlab/FunFamsClustering

  • Abbreviations used

    DBSCAN
    density-based spatial clustering of applications with noise
    d
    dimensions
    EC
    Enzyme Commission
    FunFam
    functional family
    LM
    language model
    NLP
    natural language processing
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted January 21, 2021.
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    Clustering FunFams using sequence embeddings improves EC purity
    Maria Littmann, Nicola Bordin, Michael Heinzinger, Christine Orengo, Burkhard Rost
    bioRxiv 2021.01.21.427551; doi: https://doi.org/10.1101/2021.01.21.427551
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    Clustering FunFams using sequence embeddings improves EC purity
    Maria Littmann, Nicola Bordin, Michael Heinzinger, Christine Orengo, Burkhard Rost
    bioRxiv 2021.01.21.427551; doi: https://doi.org/10.1101/2021.01.21.427551

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