ArticleScientific reports2021
NetCleave: an open-source algorithm for predicting C-terminal antigen processing for MHC-I and MHC-II.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- Substrate secondary structure is associated with proteasomal cleavage propensity.Frontiers in bioinformatics · 2026Article
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- PredIG: an interpretable predictor of T-cell epitope immunogenicity.Genome medicine · 2025Article
- Framework for analyzing MAE-derived immunopeptidomes from cell lines with shared HLA haplotypes.PloS one · 2025Article
- Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.Briefings in bioinformatics · 2024Article
- Advancing cancer immunotherapy: from innovative preclinical models to clinical insights.Scientific reports · 2024Article
- Virus-like particle-mediated delivery of structure-selected neoantigens demonstrates immunogenicity and antitumoral activity in mice.Journal of translational medicine · 2024Article
- Harnessing SARS-CoV-2-specific CD8Cancer communications (London, England) · 2024Article
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2 authors.
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Abstract
Antigens presented on the cell surface have been subjected to multiple biological processes. Among them, C-terminal antigen processing constitutes one of the main bottlenecks of the peptide presentation pathways, as it delimits the peptidome that will be subjected downstream. Here, we present NetCleave, an open-source and retrainable algorithm for the prediction of the C-terminal antigen processing for both MHC-I and MHC-II pathways. NetCleave architecture consists of a neural network trained on 46 different physicochemical descriptors of the cleavage site amino acids. Our results demonstrate that prediction of C-terminal antigen processing achieves high accuracy on MHC-I (AUC of 0.91), while it remains challenging for MHC-II (AUC of 0.66). Moreover, we evaluated the performance of NetCleave and other prediction tools for the evaluation of four independent immunogenicity datasets (H2-Db, H2-Kb, HLA-A*02:01 and HLA-B:07:02). Overall, we demonstrate that NetCleave stands out as one of the best algorithms for the prediction of C-terminal processing, and we provide one of the first evidence that C-terminal processing predictions may help in the discovery of immunogenic peptides.
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