Abstract

Recently-developed methods to predict three-dimensional protein structure with high accuracy have opened new avenues for genome and proteome research. We explore a new hypothesis in genome annotation, namely whether computationally predicted structures can help to identify which of multiple possible gene isoforms represents a functional protein product. Guided by protein structure predictions, we evaluated over 230,000 isoforms of human protein-coding genes assembled from over 10,000 RNA sequencing experiments across many human tissues. From this set of assembled transcripts, we identified hundreds of isoforms with more confidently predicted structure and potentially superior function in comparison to canonical isoforms in the latest human gene database. We illustrate our new method with examples where structure provides a guide to function in combination with expression and evolutionary evidence. Additionally, we provide the complete set of structures as a resource to better understand the function of human genes and their isoforms. These results demonstrate the promise of protein structure prediction as a genome annotation tool, allowing us to refine even the most highly-curated catalog of human proteins. More generally we demonstrate a practical, structure-guided approach that can be used to enhance the annotation of any genome.

Data availability

Gene identifiers for all predicted protein isoforms as well as pLDDT scores and evolutionary conservation data from mouse can be found in table S1. Predicted scores and GTEx expression data for all isoforms overlapping a MANE locus can be found in table S2. Data for the 401 alternate isoforms with evidence of relatively superior structure, and possibly superior function, can be found in table S3. Additionally, all data can be downloaded from the project website, isoform.io.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Markus J Sommer

    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
    For correspondence
    markusjsommer@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3414-1875
  2. Sooyoung Cha

    School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7211-4603
  3. Ales Varabyou

    Center for Computational Biology, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Natalia Rincon

    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Sukhwan Park

    School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
    Competing interests
    The authors declare that no competing interests exist.
  6. Ilia Minkin

    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Mihaela Pertea

    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Martin Steinegger

    School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
    For correspondence
    martin.steinegger@snu.ac.kr
    Competing interests
    The authors declare that no competing interests exist.
  9. Steven L Salzberg

    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
    For correspondence
    salzberg@jhu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8859-7432

Funding

National Institutes of Health (R01-HG006677)

  • Steven L Salzberg

National Institutes of Health (R35-GM130151)

  • Steven L Salzberg

National Research Foundation of Korea (2019R1-A6A1-A10073437)

  • Martin Steinegger

National Research Foundation of Korea (2020M3-A9G7-103933)

  • Martin Steinegger

National Research Foundation of Korea (2021-R1C1-C102065)

  • Martin Steinegger

National Research Foundation of Korea (2021-M3A9-I4021220)

  • Martin Steinegger

Seoul National University (Creative-Pioneering Researchers Program)

  • Martin Steinegger

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Sommer 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. Markus J Sommer
  2. Sooyoung Cha
  3. Ales Varabyou
  4. Natalia Rincon
  5. Sukhwan Park
  6. Ilia Minkin
  7. Mihaela Pertea
  8. Martin Steinegger
  9. Steven L Salzberg
(2022)
Structure-guided isoform identification for the human transcriptome
eLife 11:e82556.
https://doi.org/10.7554/eLife.82556

Share this article

https://doi.org/10.7554/eLife.82556

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