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PreprintCited by 2RNA and protein synthesis mechanismsProtein Structure and DynamicsMachine Learning in Bioinformatics

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sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure

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Bugnon, Leandro A., Di Persia, Leandro, Gerard, Matias, Raad, Jonathan, Prochetto, Santiago, Fenoy, Emilio, Chorostecki, Uciel, Ariel, Federico, Stegmayer, Georgina, Milone, Diego H.·2023·bioRxiv (Cold Spring Harbor Laboratory)
DOI 10.1101/2023.10.10.561771

Preprint — published as 10.1093/bib/bbae271

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Abstract

Motivation: Coding and non-coding RNA molecules participate in many important biological processes. Non-coding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged thanks to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but still leaving a wide margin for improvement. Results: In this work we present sincFold an end-to-end deep learning approach that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared against classical methods and recent deep learning models, showing that it can outperform state-of-the-art methods. Availability: The source code is available at https://github.com/sinc-lab/sincFold (v0.16) and the web access is provided at https://sinc.unl.edu.ar/web-demo/sincFold Contact: lbugnon@sinc.unl.edu.ar

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Metadata

Authors
Bugnon, L. A., Di Persia, L., Gerard, M., Raad, J., Prochetto, S., Fenoy, E., Chorostecki, U., Ariel, F., Stegmayer, G., & Milone, D. H.
Affiliations
  • Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
  • Universitat Internacional de Catalunya (ES)
Corresponding author
Bugnon, Leandro A.
ORCID
Venue
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Published
2023-10-13
DOI
10.1101/2023.10.10.561771
OpenAlex
W4387641578
Cited by
2
References
56
Citations by year
2024: 2
Type
posted content
Language
en
Open access
Licence
cc-by-nc
Locations
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • Conicet (publishedVersion, cc-by-nc-sa)
Funders
  • Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación
  • Agencia Nacional de Promoción Científica y Tecnológica (PICT 2018, PICT 2018-3384, CAI+D 2020)
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 secondary structure prediction, deep learning, contact matrices, long-range interactions, short-range interactions, ResNet, non-coding RNA, end-to-end learning

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