Amaya-Ramirez, D. | bioRxiv Science

bioRxiv2023

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

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.

bioinformatics↗

bioRxiv2022

Usefulness of docking and molecular dynamics in selecting tumor neoantigens to design personalized cancer vaccines: a proof of concept.

Personalized cancer vaccines are presented as a new and promising treatment alternative for cancer, especially in those cases where effective treatments do not yet exist. However, multiple challenges remain to be resolved so that this type of immunotherapy can be used in the clinical setting. Among these, the effective identification of immunogenic peptides stands out, since the in-silico tools currently used generate a significant portion of false positives. This is where molecular simulation techniques can play an important role when it comes to refining the results produced by these tools. In the present work, we explore the use of molecular simulation techniques such as docking and molecular dynamics to study the relationship between stability of peptide-HLA complexes and their immunogenicity using two HLA-A2-restricted neoantigens that have already been evaluated in vitro. The results obtained agreed with the in vitro immunogenicity of the immunogenic neoantigen ASTN1 the only one that remains bound at both ends to the HLA-A2 molecule. Additionally, molecular dynamics indicates that position 1 of the peptide has a more important role in stabilizing the N-terminal part than previously assumed. Likewise, the results suggest that the mutations may have a "delocalized" effect on the peptide-HLA interaction, that is, they may modulate the intensity of the interactions of other amino acids in the peptide. These results highlight the suitability of this type of in silico strategy to identify peptides that form stable complexes with HLA proteins that are highly immunogenic for CD8+ T cells.

immunology↗