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Augmented base pairing networks encode RNA-small molecule binding preferences

Carlos Oliver, Vincent Mallet, Roman Sarrazin Gendron, View ORCID ProfileVladimir Reinharz, William L. Hamilton, Nicolas Moitessier, View ORCID ProfileJérôme Waldispühl
doi: https://doi.org/10.1101/701326
Carlos Oliver
1School of Computer Science, McGill University, Mila - Quebec Artificial Intelligence Institute,
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  • For correspondence: carlos.gonzalezoliver@mail.mcgill.ca
Vincent Mallet
2Institut Pasteur, Structural Bioinformatics Unit, MINES ParisTech, PSL Research University, CBIO - Centre for Computational Biology,
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  • For correspondence: v.mallet96@gmail.com
Roman Sarrazin Gendron
3School of Computer Science, McGill University,
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  • For correspondence: roman.sarrazingendron@mail.mcgill.ca
Vladimir Reinharz
4Department of Computer Science, Université du Québec à Montréal,
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  • ORCID record for Vladimir Reinharz
  • For correspondence: vreinharz@gmail.com
William L. Hamilton
5School of Computer Science, McGill Universety, Mila - Quebec Artificial Intelligence Institute,
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  • For correspondence: wlh@cs.mcgill.ca
Nicolas Moitessier
6Department of Chemistry, McGill University,
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  • For correspondence: nicolas.moitessier@mcgill.ca
Jérôme Waldispühl
7School of Computer Science, McGill University,
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  • ORCID record for Jérôme Waldispühl
  • For correspondence: jeromew@cs.mcgill.ca jeromew@cs.mcgill.ca
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Abstract

Motivation The binding of small molecules to RNAs is an important mechanism which can stabilize 3D structures or activate key molecular functions. To date, computational and experimental efforts toward small molecule binding prediction have primarily focused on protein targets. Considering that a very large portion of the genome is transcribed into non-coding RNAs but only few regions are translated into proteins, successful annotations of RNA elements targeted by small-molecule would likely uncover a vast repertoire of biological pathways and possibly lead to new therapeutic avenues.

Results Our work is a first attempt at bringing machine learning approaches to the problem of RNA drug discovery. RNAmigos takes advantage of the unique structural properties of RNA to predict small molecule ligands for unseen binding sites. A key feature of our model is an efficient representation of binding sites as augmented base pairing networks (ABPNs) aimed at encoding important structural patterns. We subject our ligand predictions to two virtual screen settings and show that we are able to rank the known ligand on average in the 73rd percentile, showing a significant improvement over several baselines. Furthermore, we observe that graphs which are augmented with non-Watson Crick (a.k.a non-canonical) base pairs are the only representation which is able to retrieve a significant signal, suggesting that non-canonical interactions are an necessary source of binding specificity in RNAs. We also find that an auxiliary graph representation task significantly boosts performance by providing efficient structural embeddings to the low data setting of ligand prediction. RNAmigos shows that RNA binding data contains structural patterns with potential for drug discovery, and provides methodological insights which can be applied to other structure-function learning tasks.

Availability Code and data is freely available at http://csb.cs.mcgill.ca/RNAmigos.

Contact jerome{at}cs.mcgill.ca

Footnotes

  • http://jwgitlab.cs.mcgill.ca/cgoliver/rnamigos

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 01, 2020.
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Augmented base pairing networks encode RNA-small molecule binding preferences
Carlos Oliver, Vincent Mallet, Roman Sarrazin Gendron, Vladimir Reinharz, William L. Hamilton, Nicolas Moitessier, Jérôme Waldispühl
bioRxiv 701326; doi: https://doi.org/10.1101/701326
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Augmented base pairing networks encode RNA-small molecule binding preferences
Carlos Oliver, Vincent Mallet, Roman Sarrazin Gendron, Vladimir Reinharz, William L. Hamilton, Nicolas Moitessier, Jérôme Waldispühl
bioRxiv 701326; doi: https://doi.org/10.1101/701326

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