ArticleBriefings in bioinformatics2024
Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 27 citations in OpenAlex.
- Identification of MHC Ligands Through Allele-Guided Isolation Combined With Machine Learning for Improved MHC Assignment Using ARDisplay-I.Molecular & cellular proteomics : MCP · 2026Article
- The digital keystone: how artificial intelligence is reshaping HLA research and clinical practice.Immunogenetics · 2026Review
- Explainable multi-modal deep learning for transparent cancer diagnosis: integrating radiology, clinical features, and decision visualization.Frontiers in artificial intelligence · 2026Article
- The immunogenicity database collaborative: a standardized, publicly available database for clinical immunogenicity observations and insights.Frontiers in immunology · 2026Article
- Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment.Frontiers in immunology · 2026Review
- DSCA-HLAII: A dual-stream cross-attention model for predicting peptide-HLA class II interaction and presentation.PLoS computational biology · 2026Article
- Review
- Beyond Binary: A Machine Learning Framework for Interpreting Organismal Behavior in Cancer Diagnostics.Biomedicines · 2025Review
- AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development.NPJ vaccines · 2025Review
- HLAIIPred: cross-attention mechanism for modeling the interaction of HLA class II molecules with peptides.Communications biology · 2025Article
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- NetMHCpan-4.2: improved prediction of CD8+ epitopes by use of transfer learning and structural features.Frontiers in immunology · 2025Article
- Current Progress in the Development of mRNA Vaccines Against Bacterial Infections.International journal of molecular sciences · 2024Review
- Immunogenicity risk assessment and mitigation for engineered antibody and protein therapeutics.Nature reviews. Drug discovery · 2024Review
- Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability.Communications biology · 2024Article
- Reducing Immunogenicity by Design: Approaches to Minimize Immunogenicity of Monoclonal Antibodies.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.
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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.