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Related Concept Videos

Antigen Processing Pathways01:31

Antigen Processing Pathways

MHC molecules are key players in the immune response, enabling T cells to recognize and respond to specific antigens. They are present on the surface of all nucleated cells in the body and are instrumental in presenting antigens to T cells and activating them. T cells recognize the MHC-antigen complex and initiate an immune response. MHC class I and MHC class II are two main types of MHC molecules, each associated with a distinct antigen processing pathway.
MHC Class I: Presenting Endogenous...

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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PMGen: from peptide-MHC structure prediction to peptide generation.

Amir H Asgary1, Amirreza Aleyasin1, Jonas A Mehl1

  • 1Quantitative and Computational Biology Group, Max Planck Institute for Multidisciplinary Sciences, Göttingen 37077, Germany.

Bioinformatics (Oxford, England)
|June 15, 2026
PubMed
Summary

PMGen accurately predicts peptide-MHC structures and designs novel peptides for immunotherapy. This framework improves upon existing methods for peptide-MHC modeling and design.

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

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Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
09:32

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Published on: October 15, 2021

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

Area of Science:

  • Computational biology
  • Structural biology
  • Immunoinformatics

Background:

  • Accurate peptide-MHC (pMHC) structural modeling is crucial for designing immunotherapies.
  • Current prediction tools have limitations in coverage, peptide length, and accuracy.
  • Existing design strategies often neglect spatial and biophysical insights from pMHC structures.

Purpose of the Study:

  • To introduce PMGen, an integrated framework for predicting and designing variable-length peptides in peptide-MHC complexes.
  • To enable structure-guided design of peptides for enhanced immunotherapy applications.
  • To overcome limitations of existing pMHC modeling tools.

Main Methods:

  • PMGen utilizes AlphaFold2 with enforced anchor constraints via Initial Guess and Template Engineering strategies.
  • The framework supports variable-length peptides across MHC class I and II.
  • ProteinMPNN sampling is employed on predicted backbones for peptide design.

Main Results:

  • PMGen achieves state-of-the-art structural fidelity without model fine-tuning.
  • Outperforms existing methods with median peptide-core Cα RMSDs of 0.62 Å (MHC-I) and 0.33 Å (MHC-II).
  • Successfully recovers incorrect anchor positions and captures mutation-induced conformational changes.
  • ProteinMPNN sampling on PMGen structures yields higher-affinity peptides.
  • Significantly improves ProteinMPNN's peptide sequence recovery for unseen MHC-I alleles.

Conclusions:

  • PMGen provides a powerful tool for accurate pMHC structure prediction and structure-guided peptide design.
  • The framework enhances immunotherapy design by leveraging spatial and biophysical insights.
  • Accurate predicted structures from PMGen are valuable for downstream machine learning tasks.