ArticleFrontiers in immunology2022
PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 48 citations in OpenAlex.
- Pocket-PROTACs: an interpretable pocket-aware deep learning framework for predicting PROTAC-induced protein degradation.Bioinformatics (Oxford, England) · 2026Article
- PMGen: from peptide-MHC structure prediction to peptide generation.Bioinformatics (Oxford, England) · 2026Article
- SwiftTCR: efficient computational docking protocol of TCRpMHC-I complexes using restricted rotation matrices.Briefings in bioinformatics · 2026Article
- Integrated reiterative pipeline for rapid epitope-based pan-alphavirus vaccines.Science advances · 2026Article
- Decoding TCR recognition via geometric deep learning of immunological fingerprints.Briefings in bioinformatics · 2026Article
- Multi-strategy embedded framework for neoantigen vaccine maturation.Scientific reports · 2026Article
- A structure-guided approach to predict MHC-I restriction of T cell receptors for public antigens.Structure (London, England : 1993) · 2025Article
- A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: glimpse and foresight.Briefings in bioinformatics · 2025Review
- Potential shared neoantigens from pan-cancer transcript isoforms.Scientific reports · 2025Article
- Role of artificial intelligence in advancing immunology.Immunologic research · 2025Review
- Article
- Predicting reverse-bound peptide conformations in MHC Class II with PANDORA.Frontiers in immunology · 2025Article
- Human microbiome-derived peptide affects the development of experimental autoimmune encephalomyelitis via molecular mimicry.EBioMedicine · 2025Article
- Perspective: host factors variants and the underlying causes of long COVID.Frontiers in medicine · 2025Article
- AI/ML-empowered approaches for predicting T Cell-mediated immunity and beyond.Frontiers in immunology · 2025Article
- Geometric deep learning improves generalizability of MHC-bound peptide predictions.Communications biology · 2024Article
- RankMHC: Learning to Rank Class-I Peptide-MHC Structural Models.Journal of chemical information and modeling · 2024Article
- Strengths and limitations of web servers for the modeling of TCRpMHC complexes.Computational and structural biotechnology journal · 2024Article
- MHC-Fine: Fine-tuned AlphaFold for precise MHC-peptide complex prediction.Biophysical journal · 2024Article
- NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
9 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deeper understanding of T-cell-mediated adaptive immune responses is important for the design of cancer immunotherapies and antiviral vaccines against pandemic outbreaks. T-cells are activated when they recognize foreign peptides that are presented on the cell surface by Major Histocompatibility Complexes (MHC), forming peptide:MHC (pMHC) complexes. 3D structures of pMHC complexes provide fundamental insight into T-cell recognition mechanism and aids immunotherapy design. High MHC and peptide diversities necessitate efficient computational modelling to enable whole proteome structural analysis. We developed PANDORA, a generic modelling pipeline for pMHC class I and II (pMHC-I and pMHC-II), and present its performance on pMHC-I here. Given a query, PANDORA searches for structural templates in its extensive database and then applies anchor restraints to the modelling process. This restrained energy minimization ensures one of the fastest pMHC modelling pipelines so far. On a set of 835 pMHC-I complexes over 78 MHC types, PANDORA generated models with a median RMSD of 0.70 Å and achieved a 93% success rate in top 10 models. PANDORA performs competitively with three pMHC-I modelling state-of-the-art approaches and outperforms AlphaFold2 in terms of accuracy while being superior to it in speed. PANDORA is a modularized and user-configurable python package with easy installation. We envision PANDORA to fuel deep learning algorithms with large-scale high-quality 3D models to tackle long-standing immunology challenges.
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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.