Predicting MHC-I ligands across alleles and species: how far can we go?
Abstract Background CD8+ T-cell activation is initiated by the recognition of epitopes presented on class I major histocompatibility complex (MHC-I) molecules. Identifying such epitopes is useful for molecular understanding of cellular immune responses and can guide the development of personalized v...
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| Format: | Article |
| Language: | English |
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BMC
2025-03-01
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| Series: | Genome Medicine |
| Online Access: | https://doi.org/10.1186/s13073-025-01450-8 |
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| author | Daniel M. Tadros Julien Racle David Gfeller |
| author_facet | Daniel M. Tadros Julien Racle David Gfeller |
| author_sort | Daniel M. Tadros |
| collection | DOAJ |
| description | Abstract Background CD8+ T-cell activation is initiated by the recognition of epitopes presented on class I major histocompatibility complex (MHC-I) molecules. Identifying such epitopes is useful for molecular understanding of cellular immune responses and can guide the development of personalized vaccines for various diseases including cancer. For a few hundred common human and mouse MHC-I alleles, large datasets of ligands are available and machine learning MHC-I ligand predictors trained on such data reach high prediction accuracy. However, for the vast majority of other MHC-I alleles, no ligand is known. Methods We capitalize on an expanded architecture of our MHC-I ligand predictor (MixMHCpred3.0) to systematically assess the extent to which predictions of MHC-I ligands can be applied to MHC-I alleles that currently lack known ligand data. Results Our results reveal high prediction accuracy for most MHC-I alleles in human and in laboratory mouse strains, but significantly lower accuracy in other species. Our work further outlines some of the molecular determinants of MHC-I ligand prediction accuracy across alleles and species. Robust benchmarking on external data shows that our MHC-I ligand predictor demonstrates competitive performance relative to other state-of-the-art MHC-I ligand predictors and can be used for CD8+ T-cell epitope predictions. Conclusions Our work provides a valuable tool for predicting antigen presentation across all human and mouse MHC-I alleles. MixMHCpred3.0 tool is available at https://github.com/GfellerLab/MixMHCpred . |
| format | Article |
| id | doaj-art-52e359ffab1c4e43a24e763e8c15d372 |
| institution | DOAJ |
| issn | 1756-994X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Medicine |
| spelling | doaj-art-52e359ffab1c4e43a24e763e8c15d3722025-08-20T02:52:19ZengBMCGenome Medicine1756-994X2025-03-0117111310.1186/s13073-025-01450-8Predicting MHC-I ligands across alleles and species: how far can we go?Daniel M. Tadros0Julien Racle1David Gfeller2Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of LausanneDepartment of Oncology, Ludwig Institute for Cancer Research Lausanne, University of LausanneDepartment of Oncology, Ludwig Institute for Cancer Research Lausanne, University of LausanneAbstract Background CD8+ T-cell activation is initiated by the recognition of epitopes presented on class I major histocompatibility complex (MHC-I) molecules. Identifying such epitopes is useful for molecular understanding of cellular immune responses and can guide the development of personalized vaccines for various diseases including cancer. For a few hundred common human and mouse MHC-I alleles, large datasets of ligands are available and machine learning MHC-I ligand predictors trained on such data reach high prediction accuracy. However, for the vast majority of other MHC-I alleles, no ligand is known. Methods We capitalize on an expanded architecture of our MHC-I ligand predictor (MixMHCpred3.0) to systematically assess the extent to which predictions of MHC-I ligands can be applied to MHC-I alleles that currently lack known ligand data. Results Our results reveal high prediction accuracy for most MHC-I alleles in human and in laboratory mouse strains, but significantly lower accuracy in other species. Our work further outlines some of the molecular determinants of MHC-I ligand prediction accuracy across alleles and species. Robust benchmarking on external data shows that our MHC-I ligand predictor demonstrates competitive performance relative to other state-of-the-art MHC-I ligand predictors and can be used for CD8+ T-cell epitope predictions. Conclusions Our work provides a valuable tool for predicting antigen presentation across all human and mouse MHC-I alleles. MixMHCpred3.0 tool is available at https://github.com/GfellerLab/MixMHCpred .https://doi.org/10.1186/s13073-025-01450-8 |
| spellingShingle | Daniel M. Tadros Julien Racle David Gfeller Predicting MHC-I ligands across alleles and species: how far can we go? Genome Medicine |
| title | Predicting MHC-I ligands across alleles and species: how far can we go? |
| title_full | Predicting MHC-I ligands across alleles and species: how far can we go? |
| title_fullStr | Predicting MHC-I ligands across alleles and species: how far can we go? |
| title_full_unstemmed | Predicting MHC-I ligands across alleles and species: how far can we go? |
| title_short | Predicting MHC-I ligands across alleles and species: how far can we go? |
| title_sort | predicting mhc i ligands across alleles and species how far can we go |
| url | https://doi.org/10.1186/s13073-025-01450-8 |
| work_keys_str_mv | AT danielmtadros predictingmhciligandsacrossallelesandspecieshowfarcanwego AT julienracle predictingmhciligandsacrossallelesandspecieshowfarcanwego AT davidgfeller predictingmhciligandsacrossallelesandspecieshowfarcanwego |