ArticleBioinformatics (Oxford, England)2019
DeepLigand: accurate prediction of MHC class I ligands using peptide embedding.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
- Predicting MHC-I ligands across alleles and species: how far can we go?Genome medicine · 2025Article
- Article
- ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model.Briefings in bioinformatics · 2024Article
- Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.Frontiers in immunology · 2024Review
- DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction.Bioinformatics (Oxford, England) · 2023Article
- Align-gram: Rethinking the Skip-gram Model for Protein Sequence Analysis.The protein journal · 2023Article
- Peptide-binding specificity prediction using fine-tuned protein structure prediction networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Improved prediction of MHC-peptide binding using protein language models.Frontiers in bioinformatics · 2023Article
- Improving MHC class I antigen-processing predictions using representation learning and cleavage site-specific kernels.Cell reports methods · 2022Article
- Prediction of Major Histocompatibility Complex Binding with Bilateral and Variable Long Short Term Memory Networks.Biology · 2022Article
- Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences.PLoS computational biology · 2022Article
- Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.Briefings in bioinformatics · 2021Article
- Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification.Briefings in bioinformatics · 2021Article
- A few good peptides: MHC class I-based cancer immunosurveillance and immunoevasion.Nature reviews. Immunology · 2021Review
- Ranking-Based Convolutional Neural Network Models for Peptide-MHC Class I Binding Prediction.Frontiers in molecular biosciences · 2021Article
- Deep Learning in Proteomics.Proteomics · 2020Review
- MHCAttnNet: predicting MHC-peptide bindings for MHC alleles classes I and II using an attention-based deep neural model.Bioinformatics (Oxford, England) · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationThe computational modeling of peptide display by class I major histocompatibility complexes (MHCs) is essential for peptide-based therapeutics design. Existing computational methods for peptide-display focus on modeling the peptide-MHC-binding affinity. However, such models are not able to characterize the sequence features for the other cellular processes in the peptide display pathway that determines MHC ligand selection.
resultsWe introduce a semi-supervised model, DeepLigand that outperforms the state-of-the-art models in MHC Class I ligand prediction. DeepLigand combines a peptide language model and peptide binding affinity prediction to score MHC class I peptide presentation. The peptide language model characterizes sequence features that correspond to secondary factors in MHC ligand selection other than binding affinity. The peptide embedding is learned by pre-training on natural ligands, and can discriminate between ligands and non-ligands in the absence of binding affinity prediction. Although conventional affinity-based models fail to classify peptides with moderate affinities, DeepLigand discriminates ligands from non-ligands with consistently high accuracy. AVAILABILITY AND IMPLEMENTATION: We make DeepLigand available at https://github.com/gifford-lab/DeepLigand. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.