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Peptide-MHC Structure Prediction With Mixed Residue and Atom Graph Neural Network
Antoine Delaunay · Yunguan Fu · Alberto Bégué · Robert McHardy · Bachir Djermani · Liviu Copoiu · Michael Rooney · Andrey Tovchigrechko · Marcin Skwark · Nicolas Lopez Carranza · Maren Lang · Karim Beguir · Ugur Sahin

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in Machine Learning in Structural Biology Workshop »

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 \textit{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 in a more than 98\% reduction in the number of required parameters.

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Author Information

Antoine Delaunay (InstaDeep)
Yunguan Fu (InstaDeep)

I am a senior research engineer from the Department of BioAI at InstaDeep, currently managing multiple R&D teams for bioinformatics projects in collaboration with BioNTech. In particular, we are monitoring the SARS-CoV-2 pandemic with the Early Warning System (Preprint, InstaDeep announcement, BioNTech announcement). I am actively hiring Senior Research Engineer, Research Engineer, and Computational Biologist in London.

Alberto Bégué (InstaDeep)
Robert McHardy (InstaDeep)
Bachir Djermani (InstaDeep)
Liviu Copoiu (InstaDeep)
Michael Rooney (BioNTech)
Andrey Tovchigrechko (BioNTech US)
Marcin Skwark (InstaDeep)
Nicolas Lopez Carranza (Instadeep)
Maren Lang (BioNTech SE)
Karim Beguir (InstaDeep)
Ugur Sahin (BioNTech)

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