Structure-guided isoform identification for the human transcriptome
- Johns Hopkins University, United States
- Seoul National University, Republic of Korea
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.
Article and author information
Author details
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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Structure-guided isoform identification for the human transcriptome
eLife 11:e82556.
https://doi.org/10.7554/eLife.82556
Further reading
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