Computational design of HLA class I superbinders for broad T cell immunogenicity Article Swipe
Els Van Peer
,
Liel Cohen-Lavi
,
Alessandro Sette
,
John Sidney
,
Tomer Hertz
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.07.10.664084
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.07.10.664084
Human leukocyte antigen (HLA) class I molecules are highly polymorphic, restricting peptide binding to narrow sequence subsets. Designing peptides that bind multiple HLA supertypes-termed superbinders-offers a promising strategy for broad-spectrum T-cell vaccines and immunotherapies. Here we present superHLA, a computational framework that combines Markov Chain Monte Carlo optimization with state-of-the-art MHC binding predictors to design synthetic 9-mer peptides with broad HLA-binding profiles. Using superHLA, we generated over 190,000 candidate superbinders predicted to bind 8-12 HLA class I alleles across distinct supertypes. A multi-tier filtering pipeline-incorporating sequence clustering, synthesis feasibility, cross-predictor validation, and self-peptidome exclusion-yielded a final panel of 100 peptides for experimental testing. Of these, 21 bound ≥ 4 supertypes in vitro, including one that bound 9. Superbinders displayed distinct anchor residue preferences and showed minimal similarity to human peptides. These results suggest that HLA superbinders are more abundant than previously recognized and can be rationally designed at scale. This approach supports development of pan-HLA immunogens with broad population coverage and potential applications in vaccine design, neoantigen discovery, and immunotherapy.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.07.10.664084
- https://www.biorxiv.org/content/biorxiv/early/2025/07/15/2025.07.10.664084.full.pdf
- OA Status
- green
- References
- 51
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://doi.org/10.1101/2025.07.10.664084Digital Object Identifier
- Title
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Computational design of HLA class I superbinders for broad T cell immunogenicityWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-15Full publication date if available
- Authors
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Els Van Peer, Liel Cohen-Lavi, Alessandro Sette, John Sidney, Tomer HertzList of authors in order
- Landing page
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https://doi.org/10.1101/2025.07.10.664084Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2025/07/15/2025.07.10.664084.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2025/07/15/2025.07.10.664084.full.pdfDirect OA link when available
- Concepts
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Immunogenicity, Class (philosophy), Human leukocyte antigen, Computational biology, Computer science, Immunology, Biology, Antigen, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.that | 19, 41, 114, 133 |
| abstract_inverted_index.with | 48, 58, 156 |
| abstract_inverted_index.(HLA) | 3 |
| abstract_inverted_index.9-mer | 56 |
| abstract_inverted_index.Carlo | 46 |
| abstract_inverted_index.Chain | 44 |
| abstract_inverted_index.Human | 0 |
| abstract_inverted_index.Monte | 45 |
| abstract_inverted_index.These | 130 |
| abstract_inverted_index.Using | 62 |
| abstract_inverted_index.bound | 106, 115 |
| abstract_inverted_index.broad | 59, 157 |
| abstract_inverted_index.class | 4, 75 |
| abstract_inverted_index.final | 95 |
| abstract_inverted_index.human | 128 |
| abstract_inverted_index.panel | 96 |
| abstract_inverted_index.Markov | 43 |
| abstract_inverted_index.T-cell | 30 |
| abstract_inverted_index.across | 78 |
| abstract_inverted_index.anchor | 120 |
| abstract_inverted_index.design | 54 |
| abstract_inverted_index.highly | 8 |
| abstract_inverted_index.narrow | 14 |
| abstract_inverted_index.scale. | 148 |
| abstract_inverted_index.showed | 124 |
| abstract_inverted_index.these, | 104 |
| abstract_inverted_index.vitro, | 111 |
| abstract_inverted_index.190,000 | 67 |
| abstract_inverted_index.alleles | 77 |
| abstract_inverted_index.antigen | 2 |
| abstract_inverted_index.binding | 12, 51 |
| abstract_inverted_index.design, | 165 |
| abstract_inverted_index.minimal | 125 |
| abstract_inverted_index.pan-HLA | 154 |
| abstract_inverted_index.peptide | 11 |
| abstract_inverted_index.present | 36 |
| abstract_inverted_index.residue | 121 |
| abstract_inverted_index.results | 131 |
| abstract_inverted_index.suggest | 132 |
| abstract_inverted_index.vaccine | 164 |
| abstract_inverted_index.abundant | 138 |
| abstract_inverted_index.approach | 150 |
| abstract_inverted_index.combines | 42 |
| abstract_inverted_index.coverage | 159 |
| abstract_inverted_index.designed | 146 |
| abstract_inverted_index.distinct | 79, 119 |
| abstract_inverted_index.multiple | 21 |
| abstract_inverted_index.peptides | 18, 57, 99 |
| abstract_inverted_index.sequence | 15, 85 |
| abstract_inverted_index.strategy | 27 |
| abstract_inverted_index.subsets. | 16 |
| abstract_inverted_index.supports | 151 |
| abstract_inverted_index.testing. | 102 |
| abstract_inverted_index.vaccines | 31 |
| abstract_inverted_index.Designing | 17 |
| abstract_inverted_index.candidate | 68 |
| abstract_inverted_index.displayed | 118 |
| abstract_inverted_index.filtering | 83 |
| abstract_inverted_index.framework | 40 |
| abstract_inverted_index.generated | 65 |
| abstract_inverted_index.including | 112 |
| abstract_inverted_index.leukocyte | 1 |
| abstract_inverted_index.molecules | 6 |
| abstract_inverted_index.peptides. | 129 |
| abstract_inverted_index.potential | 161 |
| abstract_inverted_index.predicted | 70 |
| abstract_inverted_index.profiles. | 61 |
| abstract_inverted_index.promising | 26 |
| abstract_inverted_index.superHLA, | 37, 63 |
| abstract_inverted_index.synthesis | 87 |
| abstract_inverted_index.synthetic | 55 |
| abstract_inverted_index.discovery, | 167 |
| abstract_inverted_index.immunogens | 155 |
| abstract_inverted_index.multi-tier | 82 |
| abstract_inverted_index.neoantigen | 166 |
| abstract_inverted_index.population | 158 |
| abstract_inverted_index.predictors | 52 |
| abstract_inverted_index.previously | 140 |
| abstract_inverted_index.rationally | 145 |
| abstract_inverted_index.recognized | 141 |
| abstract_inverted_index.similarity | 126 |
| abstract_inverted_index.supertypes | 109 |
| abstract_inverted_index.HLA-binding | 60 |
| abstract_inverted_index.clustering, | 86 |
| abstract_inverted_index.development | 152 |
| abstract_inverted_index.preferences | 122 |
| abstract_inverted_index.restricting | 10 |
| abstract_inverted_index.supertypes. | 80 |
| abstract_inverted_index.validation, | 90 |
| abstract_inverted_index.Superbinders | 117 |
| abstract_inverted_index.applications | 162 |
| abstract_inverted_index.experimental | 101 |
| abstract_inverted_index.feasibility, | 88 |
| abstract_inverted_index.optimization | 47 |
| abstract_inverted_index.polymorphic, | 9 |
| abstract_inverted_index.superbinders | 69, 135 |
| abstract_inverted_index.computational | 39 |
| abstract_inverted_index.broad-spectrum | 29 |
| abstract_inverted_index.immunotherapy. | 169 |
| abstract_inverted_index.self-peptidome | 92 |
| abstract_inverted_index.cross-predictor | 89 |
| abstract_inverted_index.immunotherapies. | 33 |
| abstract_inverted_index.state-of-the-art | 49 |
| abstract_inverted_index.exclusion-yielded | 93 |
| abstract_inverted_index.supertypes-termed | 23 |
| abstract_inverted_index.superbinders-offers | 24 |
| abstract_inverted_index.pipeline-incorporating | 84 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5004268970 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I124227911 |
| citation_normalized_percentile.value | 0.27257346 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |