Structure-guided isoform identification for the human transcriptome

Dec 15, 2022·
Markus J Sommer*
Equal contribution
Sooyoung Cha
Sooyoung Cha
Author
,
...
Author
,
Martin Steinegger
Corresponding author
,
Steven L Salzberg
Corresponding author
· 2 min read
Image credit: Unsplash
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.
Type
Publication
Elife, 11

title: “GPU-accelerated homology search with MMseqs2. bioRxiv, 2024” authors:

  • Felix Kallenborn*
  • …
  • admin
  • Christian Dallago
  • Milot Mirdita
  • Bertil Schmidt
  • Martin Steinegger author_notes:
  • “Equal contribution”
  • “Author”
  • “Author”
  • “Corresponding author”
  • “Corresponding author”
  • “Corresponding author”
  • “Corresponding author”

date: “2024-11-13T00:00:00Z” doi: “https://doi.org/10.1101/2024.11.13.623350"

Schedule page publish date (NOT publication’s date).

publishDate: “2024-07-07T00:00:00Z”

Publication type.

Accepts a single type but formatted as a YAML list (for Hugo requirements).

Enter a publication type from the CSL standard.

publication_types: [“article-journal”]

Publication name and optional abbreviated publication name.

publication: “Elife, 11”

publication_short: "”

abstract: Rapidly growing protein databases demand faster sensitive sequence similarity detection. We present GPU-accelerated search utilizing intra-query parallelization delivering 6x faster single-protein searches compared to state-of-the-art CPU methods on 2×64 cores—speeds previously requiring large protein batches. It is most cost effective, including in large-batches at 0.45x MMseqs2-CPU speed (8 GPUs delivering 2.4x). It accelerates ColabFold structure prediction 31.8x compared to AlphaFold2 and Foldseek search 4-27x. MMseqs2-GPU is open-source at mmseqs.com.

Summary. An optional shortened abstract.

summary: ''

tags:

  • ’' featured: false

links:

- name: ""

url: ""

url_pdf: https://www.biorxiv.org/content/10.1101/2024.11.13.623350v6.full.pdf url_code: ’' url_dataset: ’' url_poster: ’' url_project: ’' url_slides: ’' url_source: ’' url_video: ''

Featured image

To use, add an image named featured.jpg/png to your page’s folder.

image: caption: ‘Image credit: Unsplash’ focal_point: "" preview_only: false

Associated Projects (optional).

Associate this publication with one or more of your projects.

Simply enter your project’s folder or file name without extension.

E.g. internal-project references content/project/internal-project/index.md.

Otherwise, set projects: [].

projects: []

Slides (optional).

Associate this publication with Markdown slides.

Simply enter your slide deck’s filename without extension.

E.g. slides: "example" references content/slides/example/index.md.

Otherwise, set slides: "".

slides: example

Sooyoung Cha
Authors
Sooyoung Cha
Integrated Ph.D Student