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3pHLA-score improves structure-based peptide-HLA binding affinity prediction.

Authors :
Conev A
Devaurs D
Rigo MM
Antunes DA
Kavraki LE
Source :
Scientific reports [Sci Rep] 2022 Jun 24; Vol. 12 (1), pp. 10749. Date of Electronic Publication: 2022 Jun 24.
Publication Year :
2022

Abstract

Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta's ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protocol outperforms the standard training protocol and leads to an increase from 0.82 to 0.99 of the area under the precision-recall curve. 3pHLA-score outperforms widely used scoring functions (AutoDock4, Vina, Dope, Vinardo, FoldX, GradDock) in a structural virtual screening task. Overall, this work brings structure-based methods one step closer to epitope discovery pipelines and could help advance the development of cancer and viral vaccines.<br /> (© 2022. The Author(s).)

Details

Language :
English
ISSN :
2045-2322
Volume :
12
Issue :
1
Database :
MEDLINE
Journal :
Scientific reports
Publication Type :
Academic Journal
Accession number :
35750701
Full Text :
https://doi.org/10.1038/s41598-022-14526-x