3pHLA-score improves structure-based peptide-HLA binding affinity prediction
Conev, Anja and Devaurs, Didier and Rigo, Mauricio Menegatti and Antunes, Dinler Amaral and Kavraki, Lydia E. (2022) 3pHLA-score improves structure-based peptide-HLA binding affinity prediction. Scientific Reports, 12 (1). 10749. ISSN 2045-2322 (https://doi.org/10.1038/s41598-022-14526-x)
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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.
ORCID iDs
Conev, Anja, Devaurs, Didier![ORCID logo](/images/orcid_16x16.png)
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Item type: Article ID code: 90229 Dates: DateEvent24 June 2022Published8 June 2022AcceptedSubjects:
Medicine > Biomedical engineering. Electronics. Instrumentation
Science > Mathematics > Electronic computers. Computer scienceDepartment: Faculty of Science > Computer and Information Sciences Depositing user: Pure Administrator Date deposited: 13 Aug 2024 14:39 Last modified: 01 Sep 2024 01:49 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/90229