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Cited by 11RNA and protein synthesis mechanismsProtein Structure and DynamicsMachine Learning in Bioinformatics

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RNA contact prediction by data efficient deep learning

Open access
Taubert, Oskar, von der Lehr, Fabrice, Bazarova, Alina, Faber, Christian, Knechtges, Philipp, Weiel, Marie, Debus, Charlotte, Coquelin, Daniel, Basermann, Achim, Streit, Achim, Kesselheim, Stefan, Götz, Markus, Schug, Alexander·2023·Communications Biology
DOI 10.1038/s42003-023-05244-9PMC PMC10482910

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Abstract

On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps”) as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNACLE shows a considerable improvement over both the established classical baseline and a deep neural network. In order to demonstrate that our approach can be applied to tasks with similar data constraints, we show that our findings generalize to the related setting of accessible surface area prediction.

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Metadata

Authors
Taubert, O., von der Lehr, F., Bazarova, A., Faber, C., Knechtges, P., Weiel, M., Debus, C., Coquelin, D., Basermann, A., Streit, A., Kesselheim, S., Götz, M., & Schug, A.
Affiliations
  • Karlsruhe Institute of Technology (DE)
  • Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
  • Forschungszentrum Jülich (DE)
Corresponding author
  • Götz, Markus
  • Schug, Alexander
ORCID
Venue
Communications Biology
Publisher
Springer Science and Business Media LLC
Published
2023-09-06
Volume
6
Issue
1
Pages
913-913
ISSN
2399-3642 (ISSN-L)
DOI
10.1038/s42003-023-05244-9
PMC
PMC10482910
OpenAlex
W4386479765
Cited by
11
References
12
Citations by year
2026: 2 · 2025: 6 · 2024: 3
FWCI
0.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Communications Biology (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • elib (German Aerospace Center) (acceptedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Helmholtz Association (ZT-I-0003, ZT-I-PF-5-18)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
RNA structure prediction, barnacles, self-supervised pre-training, XGBoost, sparse labeled data, data-efficient deep learning, structure-function relationship, RNA design
MeSH terms
Deep Learning, Animals, Thoracica, Records, RNA, Neural Networks, Computer

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