# HLA-EpiCheck : A B-cell epitope prediction tool on HLA antigens using molecular dynamics simulation data

Amaya-Ramirez Diego¹, Lhotte Romain², Usureau Cedric², Devriese Magali², Smail-Tabbone Malika¹, Taupin Jean-Luc² and Devignes Marie-Dominique¹

¹ Université de Lorraine, CNRS, INRIA, LORIA, Nancy, F-54000, France; ²INSERM, Hôpital Saint Louis, F-75010 Paris

Contact : diego.amaya-ramirez@inria.fr

## Introduction

In the context of organ transplantation, recipient's antibodies against donor-specific HLA antigens are the main reason for transplant loss. The prediction of B-cell (antibody) epitopes on HLA antigens is therefore a key challenge on the way to improving the matching step between donor and recipient from a structural point of view. Here, we present **HLA-EpiCheck**, a B-cell epitope prediction tool that relies on an unprecedented dataset of short Molecular Dynamics (MD) simulations of 207 HLA antigens. We use hydrophobic properties, electrostatic charges, flexibility and solvent accessibility as descriptors calculated on patches sampled from MD trajectories. Then, we train an Extremely Randomized Trees machine learning model. This model outperforms the state-of-the-art **DiscoTope 3.0** tool [1] for B-cell epitope prediction on HLA antigens.

## Dataset generation

<table><thead><tr><th colspan="4">Patch Descriptors Dictionary (18)</th></tr><tr><th rowspan="2">Name</th><th rowspan="2">Description (per patch)</th><th colspan="2">Descriptor type</th><th rowspan="2">Value range</th></tr><tr><th>Static</th><th>Dynamic</th></tr></thead><tbody><tr><td>H</td><td>Hydrophobicity of central residue</td><td>x</td><td></td><td>[-4.5, 4.5]<sup>+</sup></td></tr><tr><td>H_patch_min</td><td>Minimum residue hydrophobicity</td><td>x</td><td></td><td>[-4.5, 4.5]<sup>+</sup></td></tr><tr><td>H_patch_max</td><td>Maximum residue hydrophobicity</td><td>x</td><td></td><td>[-4.5, 4.5]<sup>+</sup></td></tr><tr><td>H_patch_avg</td><td>Average of all residue hydrophobicities</td><td>x</td><td></td><td>[-4.5, 4.5]<sup>+</sup></td></tr><tr><td>Pos</td><td>Positive charge of central residue</td><td>x</td><td></td><td>[0, 1]</td></tr><tr><td>Pos_patch</td><td>Sum of positive charges of all residues</td><td>x</td><td></td><td>[0, 1]</td></tr><tr><td>NegSRSA</td><td>Negative charge of central residue</td><td>x</td><td></td><td>[-1, 0]</td></tr><tr><td>Neg_patch</td><td>Sum of negative charges of all residues</td><td>x</td><td></td><td>[-1, 0]</td></tr><tr><td>S SRSA</td><td>Minimum RSASA* of central residue over MD</td><td></td><td>x</td><td>[0, 91] Å<sup>2</sup></td></tr><tr><td>S SRSA</td><td>Maximum RSASA of central residue over MD</td><td></td><td>x</td><td>[21, 138] Å<sup>2</sup></td></tr><tr><td>S SRSA</td><td>Average RSASA of central residue over MD</td><td></td><td>x</td><td>[10, 116] Å<sup>2</sup></td></tr><tr><td>S SRSA</td><td>Weighted<sup>**</sup> average of all residue minimum RSASAs over MD</td><td></td><td>x</td><td>[2, 49] Å<sup>2</sup></td></tr><tr><td>S SRSA</td><td>Weighted average of all residue maximum RSASAs over MD</td><td></td><td>x</td><td>[9, 90] Å<sup>2</sup></td></tr><tr><td>S SRSA</td><td>Weighted average of all residue average RSASAs over MD</td><td></td><td>x</td><td>[6, 65] Å<sup>2</sup></td></tr><tr><td>FSRSA</td><td>N-RMSF*** of central residue</td><td></td><td>x</td><td>[-2, 13]</td></tr><tr><td>F patch_min</td><td>Minimum residue N-RMSF</td><td></td><td>x</td><td>[-2, 3]</td></tr><tr><td>F patch_max</td><td>Maximum residue N-RMSF</td><td></td><td>x</td><td>[-2, 15]</td></tr><tr><td>F patch_Avg</td><td>Weighted average of all residue N-RMSFs</td><td></td><td>x</td><td>[-2, 6]</td></tr></tbody></table>

* RSASA: Relative Accessible Surface Area.

** Weights (of residue values) correspond to the frequency of presence of each residue in the patch over MD (% frames).

*** N-RMSF: Normalized-Root Mean Square Fluctuation during MD [7].

‡ Kyte-Doolittle hydrophobicity scale was used.

## MD data :

* 207 HLA antigens (the most prevalent in the European population).

* Initial templates for MD simulations were obtained from PDB [3] or Alphafold2 [4].

* 10ns MD simulations performed for each HLA antigen and 500 frames extracted from the last 5ns of the trajectory.

* NAMD3 software [5], CHARMM36 [6] force field and TIP3P explicit solvent model were used for MD runs.

<figure><img src="image_1.png" /></figure>

## Dataset :

### Patch definition

* Calculated for each frame of each MD run of each HLA antigen

* Centered on every surface accessible residue (RSASA* ≥ 20%)

* Composed of the surface accessible residues within a radius of 7Å from the central residue (centers of mass where used to measure distances).

* 18 descriptors: (8 static and 10 dynamic).

* 2 classes: epitope and non-epitope.

* Epitope labels = confirmed eplets in the HLA Eplet Registry database [2].

* Non epitope = neither confirmed nor putative eplet.

* Patches composed solely of conserved residues (with respect to the locus) were discarded.

<table><thead><tr><th></th><th>A</th><th>B</th><th>C</th><th>DP</th><th>DQ</th><th>DR</th><th>Total</th></tr></thead><tbody><tr><td># antigens</td><td>34</td><td>59</td><td>17</td><td>31</td><td>30</td><td>36</td><td>207</td></tr><tr><td rowspan="2">Dataset</td><td># epitope class</td><td>435</td><td>542</td><td>174</td><td>179</td><td>464</td><td>308</td><td>2102</td></tr><tr><td># non-epitope class</td><td>1302</td><td>3072</td><td>793</td><td>535</td><td>1646</td><td>626</td><td>7974</td></tr><tr><td rowspan="2">Training set (80%)</td><td># epitope samples</td><td>349</td><td>434</td><td>133</td><td>137</td><td>377</td><td>243</td><td>1673</td></tr><tr><td># non-epitope class</td><td>1047</td><td>2458</td><td>605</td><td>432</td><td>1335</td><td>472</td><td>6349</td></tr><tr><td rowspan="2">Test set (20%)</td><td># epitope class</td><td>81</td><td>101</td><td>44</td><td>47</td><td>88</td><td>66</td><td>427</td></tr><tr><td># non-epitope class</td><td>260</td><td>621</td><td>185</td><td>98</td><td>310</td><td>153</td><td>1627</td></tr></tbody></table>

## Results

### Model selection
(on Training set)

<table><thead><tr><th rowspan="2">Algorithm</th><th colspan="4">F1-score for 10 repetitions of 10-fold cross-validations</th></tr><tr><th colspan="2">With Dynamic features</th><th colspan="2">Only static features</th></tr><tr><th></th><th>Class epitope</th><th>Class non-epitope</th><th>Class epitope</th><th>Class non-epitope</th></tr></thead><tbody><tr><td>Logistic regression</td><td>0.17 +/- 0.05</td><td>0.88 +/- 0.01</td><td>0.05 +/- 0.01</td><td>0.76 +/- 0.002</td></tr><tr><td>Decision trees</td><td>0.57 +/- 0.08</td><td>0.88 +/- 0.03</td><td>0.48 +/- 0.06</td><td>0.74 +/- 0.07</td></tr><tr><td>Random Forest</td><td>0.64 +/- 0.11</td><td>0.92 +/- 0.03</td><td>0.50 +/- 0.06</td><td>0.83 +/- 0.05</td></tr><tr><td>Extremely Randomized Trees (Extra Trees) [8]</td><td>0.74 +/- 0.10</td><td>0.93 +/- 0.03</td><td>0.67 +/- 0.08</td><td>0.92 +/- 0.04</td></tr></tbody></table>

### Feature Importance
for the Extra Trees model

<figure><img src="image_2.png" /></figure>

### Comparison with DiscoTope 3.0
(on Test set)

<table><thead><tr><th rowspan="2"></th><th colspan="2">HLA-EpiCheck</th><th colspan="2">DiscoTope</th></tr><tr><th>Class epitope</th><th>Class non-epitope</th><th>Class epitope</th><th>Class non-epitope</th></tr></thead><tbody><tr><td>Precision</td><td>0.94</td><td>0.94</td><td>0.41</td><td>0.79</td></tr><tr><td>Recall</td><td>0.78</td><td>0.99</td><td>0.05</td><td>0.98</td></tr><tr><td>F1-score</td><td>0.85</td><td>0.97</td><td>0.09</td><td>0.88</td></tr></tbody></table>

## Conclusion and Perspectives

* ✓ Modest contribution of MD descriptors on prediction performance.

* ✓ Poor performance of DiscoTope 3.0: no HLA antigen in the dataset used to train DiscoTope.

* ✓ Find better descriptors for MD: analyse false negative epitopes.

* ✓ Try instance-based predictors such as K-NN.

## References

[1] M. Høie *et al.* DiscoTope-3.0 - bioRxiv, 2023.

[2] R. Duquesnoy *et al.* 16th IHIW. Int. J. Immunogenetics, 2017.

[3] H. M. Berman *et al.* PDB. Nucleic Acids Research, 2000.

[4] J. Jumper *et al.* AlphaFold2. Nature, 2021.

[5] J. Phillips. *et al.* NAMD3. Journal of Chemical Physics, 2020.

[6] J. Huang *et al.* CHARMM36m. Nat Methods, 2017.

[7] K. Dong-Gun *et al.* J. of Chem. Information and Modeling 2021.

[8] P. Geurts *et al.*. Extremely randomized trees. Machine Learning, 2006.

## Acknowledgements

DAR is recipient of an Inria-Inserm doctoral fellowship. This project is funded by ANR EPI-HLA N° ANR-22-CE15-0036-03.

Experiments presented in this poster were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr), as well as the MBI-DS4H platform hosted by Inria/Loria and funded by CPER IT2MP (Contrat Plan État Région, Innovations, Technologiques, Modélisation & Médicine Personalisée), including a FEDER co-funding.