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bioRxiv · 10.1101/2023.12.18.572133

HLA-EpiCheck: A B-cell epitope prediction tool for HLA proteins using molecular dynamics simulation data.

Amaya-Ramirez, D.·Devriese, M.·Lhotte, R.·Usureau, C.·Smail-Tabbone, M.·Taupin, J.-L.·Devignes, M.-D.
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Abstract

1The Human Leukocyte Antigen (HLA) system is the main cause of organ transplant loss through the recognition of HLA proteins by Donor-Specific Antibodies (DSA). Therefore, the identification of potentially immunogenic epitopes is a key task to refine organ allocation and then to improve the survival of transplanted organs. Here, we present HLA-EpiCheck, a machine learning predictor for B-cell epitopes on HLA proteins that leverages an unprecedented dataset of high-quality molecular dynamics simulations of 207 HLA proteins. Candidate epitopes are represented as surface patches centered on solvent-accessible residues and described by a set of 18 descriptors. The descriptors include both static and dynamic properties, such as hydrophobicity, electrostatic charges, relative solvent-accessible surface area and side-chain flexibility. The HLA-EpiCheck was trained using an Extra Trees ensemble learning method and was compared to DiscoTope-3.0, a state-of-the-art B-cell epitope predictor. HLA-EpiCheck largely outperformed DiscoTope-3.0 in the task of predicting HLA epitopes. HLA-EpiCheck was also used to assess the epitope status of a subset of non-confirmed eplets. The predictions were compared to experimental data and a notable consistency was found. These results suggest that HLA-EpiCheck could be used to better define HLA matching between donor and recipient to reduce de novo DSA formation and graft rejection.

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Amaya-Ramirez, D., Devriese, M., Lhotte, R., Usureau, C., Smail-Tabbone, M., Taupin, J.-L., Devignes, M.-D.. 2023-12-18. HLA-EpiCheck: A B-cell epitope prediction tool for HLA proteins using molecular dynamics simulation data.. https://doi.org/10.1101/2023.12.18.572133

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