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Graph neural networks and sequence embeddings enable the prediction and design of the cofactor specificity of Rossmann fold proteins

Kamil Kaminski, Jan Ludwiczak, Maciej Jasinski, Adriana Bukala, Rafal Madaj, Krzysztof Szczepaniak, View ORCID ProfileStanislaw Dunin-Horkawicz
doi: https://doi.org/10.1101/2021.05.05.440912
Kamil Kaminski
1Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
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Jan Ludwiczak
1Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
2Laboratory of Bioinformatics, Nencki Institute of Experimental Biology, Pasteura 3, 02-093 Warsaw, Poland
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Maciej Jasinski
1Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
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Adriana Bukala
1Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
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Rafal Madaj
3Centre of Molecular and Macromolecular Studies, Polish Academy of Sciences, Sienkiewicza 112, 90-363, Lodz, Poland
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Krzysztof Szczepaniak
1Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
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Stanislaw Dunin-Horkawicz
1Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
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  • ORCID record for Stanislaw Dunin-Horkawicz
  • For correspondence: s.dunin-horkawicz@cent.uw.edu.pl
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Abstract

The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases and enzymes involved in the polyamine synthesis recognize only a single cofactor type, the S-Adenosylmethionine (SAM), the oxidoreductases, depending on the family, bind nicotinamide (NAD, NADP) or flavin-based (FAD) cofactors. In this study, we show that despite its short length, the βαβ motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the features of the βαβ motif. The first utilizes contextualized sequence embeddings, whereas the second relies on structures represented as graphs. A benchmark on two test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 cases of the experimentally confirmed redesign of the cofactor specificity from NAD to NADP and vice versa, revealed nearly-perfect performance (~95% accuracy) of the two methods. Finally, by combining the two approaches, we built a pipeline for the design of cofactor-switching mutations. Both prediction methods can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/labstructbioinf/rossmann-toolbox

  • https://lbs.cent.uw.edu.pl/rossmann-toolbox

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 4.0 International license.
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Posted May 06, 2021.
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Graph neural networks and sequence embeddings enable the prediction and design of the cofactor specificity of Rossmann fold proteins
Kamil Kaminski, Jan Ludwiczak, Maciej Jasinski, Adriana Bukala, Rafal Madaj, Krzysztof Szczepaniak, Stanislaw Dunin-Horkawicz
bioRxiv 2021.05.05.440912; doi: https://doi.org/10.1101/2021.05.05.440912
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Graph neural networks and sequence embeddings enable the prediction and design of the cofactor specificity of Rossmann fold proteins
Kamil Kaminski, Jan Ludwiczak, Maciej Jasinski, Adriana Bukala, Rafal Madaj, Krzysztof Szczepaniak, Stanislaw Dunin-Horkawicz
bioRxiv 2021.05.05.440912; doi: https://doi.org/10.1101/2021.05.05.440912

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