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Peptide-MHC Structure Prediction With Mixed Residue and Atom Graph Neural Network

View ORCID ProfileAntoine P. Delaunay, View ORCID ProfileYunguan Fu, Alberto Bégué, Robert McHardy, Bachir A. Djermani, Michael Rooney, Andrey Tovchigrechko, View ORCID ProfileLiviu Copoiu, View ORCID ProfileMarcin J. Skwark, Nicolas Lopez Carranza, View ORCID ProfileMaren Lang, View ORCID ProfileKarim Beguir, View ORCID ProfileUğur Şahin
doi: https://doi.org/10.1101/2022.11.23.517618
Antoine P. Delaunay
1InstaDeep
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  • For correspondence: a.delaunay@instadeep.com
Yunguan Fu
1InstaDeep
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Alberto Bégué
1InstaDeep
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Robert McHardy
1InstaDeep
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Bachir A. Djermani
1InstaDeep
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Michael Rooney
2BioNTech
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Andrey Tovchigrechko
2BioNTech
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Liviu Copoiu
1InstaDeep
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Marcin J. Skwark
1InstaDeep
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Nicolas Lopez Carranza
1InstaDeep
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Maren Lang
2BioNTech
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Karim Beguir
1InstaDeep
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Uğur Şahin
2BioNTech
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Abstract

Neoantigen-targeting vaccines have achieved breakthrough success in cancer immunotherapy by eliciting immune responses against neoantigens, which are proteins uniquely produced by cancer cells. During the immune response, the interactions between peptides and major histocompatibility complexes (MHC) play an important role as peptides must be bound and presented by MHC to be recognised by the immune system. However, only limited experimentally determined peptide-MHC (pMHC) structures are available, and in-silico structure modelling is therefore used for studying their interactions. Current approaches mainly use Monte Carlo sampling and energy minimisation, and are often computationally expensive. On the other hand, the advent of large high-quality proteomic data sets has led to an unprecedented opportunity for deep learning-based methods with pMHC structure prediction becoming feasible with these trained protein folding models. In this work, we present a graph neural network-based model for pMHC structure prediction, which takes an amino acid-level pMHC graph and an atomic-level peptide graph as inputs and predicts the peptide backbone conformation. With a novel weighted reconstruction loss, the trained model achieved a similar accuracy to AlphaFold 2, requiring only 1.7M learnable parameters compared to 93M, representing a more than 98% reduction in the number of required parameters.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • a.delaunay{at}instadeep.com, y.fu{at}instadeep.com, a.begue{at}instadeep.com, r.mchardy{at}instadeep.com

  • b.djermani{at}instadeep.com, l.copoiu{at}instadeep.com, m.skwark{at}instadeep.com, n.lopezcarranza{at}instadeep.com, kb{at}instadeep.com

  • michael.rooney{at}biontech.us, andrey.tovchigrechko{at}biontech.us

  • maren.lang{at}biontech.de, ugur.sahin{at}biontech.de

  • ↵1 https://github.com/phbradley/alphafold_finetune

  • ↵2 https://github.com/HeliXonProtein/OmegaFold

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 24, 2022.
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Peptide-MHC Structure Prediction With Mixed Residue and Atom Graph Neural Network
Antoine P. Delaunay, Yunguan Fu, Alberto Bégué, Robert McHardy, Bachir A. Djermani, Michael Rooney, Andrey Tovchigrechko, Liviu Copoiu, Marcin J. Skwark, Nicolas Lopez Carranza, Maren Lang, Karim Beguir, Uğur Şahin
bioRxiv 2022.11.23.517618; doi: https://doi.org/10.1101/2022.11.23.517618
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Peptide-MHC Structure Prediction With Mixed Residue and Atom Graph Neural Network
Antoine P. Delaunay, Yunguan Fu, Alberto Bégué, Robert McHardy, Bachir A. Djermani, Michael Rooney, Andrey Tovchigrechko, Liviu Copoiu, Marcin J. Skwark, Nicolas Lopez Carranza, Maren Lang, Karim Beguir, Uğur Şahin
bioRxiv 2022.11.23.517618; doi: https://doi.org/10.1101/2022.11.23.517618

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