ArticleMolecules (Basel, Switzerland)2019
APE-Gen: A Fast Method for Generating Ensembles of Bound Peptide-MHC Conformations.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 41 citations in OpenAlex.
- Identification of immunogenic KIF5B-RET fusion neopeptides driving immune stimulation in tumor specific CD8+ T cells.Frontiers in immunology · 2025Article
- The dawn of biophysical representations in computational immunology.QRB discovery · 2025Review
- 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
- Article
- MHC-Fine: Fine-tuned AlphaFold for precise MHC-peptide complex prediction.Biophysical journal · 2024Article
- APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries.Journal of chemical information and modeling · 2024Article
- MHC-Fine: Fine-tuned AlphaFold for Precise MHC-Peptide Complex Prediction.bioRxiv : the preprint server for biology · 2023Article
- HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes.Nature communications · 2023Article
- Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors.International journal of molecular sciences · 2023Review
- The PANDORA Software for Anchor-Restrained Peptide:MHC Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure.Frontiers in immunology · 2023Article
- 3pHLA-score improves structure-based peptide-HLA binding affinity prediction.Scientific reports · 2022Article
- SARS-Arena: Sequence and Structure-Guided Selection of Conserved Peptides from SARS-related Coronaviruses for Novel Vaccine Development.Frontiers in immunology · 2022Article
- Physicochemical Heuristics for Identifying High Fidelity, Near-Native Structural Models of Peptide/MHC Complexes.Frontiers in immunology · 2022Article
- Snowflake epitope matching correlates with child-specific antibodies during pregnancy and donor-specific antibodies after kidney transplantation.Frontiers in immunology · 2022Article
- Snowflake: A deep learning-based human leukocyte antigen matching algorithm considering allele-specific surface accessibility.Frontiers in immunology · 2022Article
- PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes.Frontiers in immunology · 2022Article
- Rapid Assessment of T-Cell Receptor Specificity of the Immune Repertoire.Nature computational science · 2021Article
- Glycoproteogenomics: Setting the Course for Next-generation Cancer Neoantigen Discovery for Cancer Vaccines.Genomics, proteomics & bioinformatics · 2021Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The Class I Major Histocompatibility Complex (MHC) is a central protein in immunology as it binds to intracellular peptides and displays them at the cell surface for recognition by T-cells. The structural analysis of bound peptide-MHC complexes (pMHCs) holds the promise of interpretable and general binding prediction (i.e., testing whether a given peptide binds to a given MHC). However, structural analysis is limited in part by the difficulty in modelling pMHCs given the size and flexibility of the peptides that can be presented by MHCs. This article describes APE-Gen (Anchored Peptide-MHC Ensemble Generator), a fast method for generating ensembles of bound pMHC conformations. APE-Gen generates an ensemble of bound conformations by iterated rounds of (i) anchoring the ends of a given peptide near known pockets in the binding site of the MHC, (ii) sampling peptide backbone conformations with loop modelling, and then (iii) performing energy minimization to fix steric clashes, accumulating conformations at each round. APE-Gen takes only minutes on a standard desktop to generate tens of bound conformations, and we show the ability of APE-Gen to sample conformations found in X-ray crystallography even when only sequence information is used as input. APE-Gen has the potential to be useful for its scalability (i.e., modelling thousands of pMHCs or even non-canonical longer peptides) and for its use as a flexible search tool. We demonstrate an example for studying cross-reactivity.
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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.