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Nucleic Acid-Protein Interaction Prediction Using Geometric Deep Learning

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Supercomputing (RuSCDays 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14389))

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Abstract

In biology, it remains challenging to predict interactions between proteins and DNA or RNA. When it comes to nucleic acids, existing methods of binding site identification or interaction prediction are inefficient, especially in minor cases, such as aptamer binding. In order to predict NA-protein interactions, we use a deep-learning framework called dMaSIF. Therefore, we modified the atom encoding module to reflect atom positions and relationships more precisely and used parallel calculation to optimize training process. The framework showed effectiveness on two tasks: identifying NA binding sites and predicting NA-protein interactions. This approach can thereby be used to find potential NA binding sites, to perform NA-protein docking and virtual screening, etc.

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Acknowledgements

This research was done with the support of MSU Program of Development, Project No 23-SCH03-05. The research was carried out using the equipment of the shared research facilities of HPC computing resources at Lomonosov Moscow State University.

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Authors and Affiliations

  1. Bioengineering and Bioinformatics Department, M. V. Lomonosov Moscow State University, Moscow, Russia

    Elizaveta Geraseva & Andrey Golovin

Authors
  1. Elizaveta Geraseva
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  2. Andrey Golovin
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Corresponding author

Correspondence to Elizaveta Geraseva .

Editor information

Editors and Affiliations

  1. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Vladimir Voevodin

  2. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Sergey Sobolev

  3. Russian Academy of Sciences (RAS), Keldysh Institute of Applied Mathematics, Moscow, Russia

    Mikhail Yakobovskiy

  4. Russian Federal Nuclear Center, Sarov, Russia

    Rashit Shagaliev

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Geraseva, E., Golovin, A. (2023). Nucleic Acid-Protein Interaction Prediction Using Geometric Deep Learning. In: Voevodin, V., Sobolev, S., Yakobovskiy, M., Shagaliev, R. (eds) Supercomputing. RuSCDays 2023. Lecture Notes in Computer Science, vol 14389. Springer, Cham. https://doi.org/10.1007/978-3-031-49435-2_17

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  • DOI: https://doi.org/10.1007/978-3-031-49435-2_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49434-5

  • Online ISBN: 978-3-031-49435-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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