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

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Published on: October 15, 2021

PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes.

Dario F Marzella¹, Farzaneh M Parizi¹, Derek van Tilborg^1,2

¹Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen, Netherlands.

Frontiers in Immunology

|May 27, 2022

Summary

PANDORA is a new computational tool that models peptide:MHC complexes, crucial for understanding T-cell responses in cancer immunotherapy and vaccines. It offers fast and accurate 3D structural analysis for immunology research.

Keywords:

computational immunology computational structural biology integrative modelling large-scale 3D-modelling peptide:MHC

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

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

Published on: March 25, 2014

Area of Science:

Immunology and Structural Biology
Computational Biology and Bioinformatics

Background:

Understanding T-cell adaptive immune responses is vital for developing cancer immunotherapies and vaccines.
T-cell activation relies on recognizing foreign peptides presented by Major Histocompatibility Complexes (MHC) as peptide:MHC (pMHC) complexes.
Accurate 3D structures of pMHC complexes are essential for insights into T-cell recognition and immunotherapy design.

Purpose of the Study:

To introduce PANDORA, a computational pipeline for modeling peptide:MHC class I and II (pMHC-I and pMHC-II) complexes.
To present the performance and capabilities of PANDORA, specifically for pMHC-I modeling.
To enable efficient, whole-proteome structural analysis of pMHC complexes due to high diversity.

Main Methods:

PANDORA utilizes a database of structural templates and applies anchor restraints during energy minimization for modeling.
The pipeline is designed for speed and efficiency, leveraging restrained energy minimization.
It is implemented as a modular, user-configurable Python package for easy installation.

Main Results:

PANDORA achieved a median RMSD of 0.70 Å on a dataset of 835 pMHC-I complexes across 78 MHC types.
The pipeline demonstrated a 93% success rate in generating top 10 models.
PANDORA showed competitive performance against state-of-the-art methods, outperforming AlphaFold2 in accuracy and speed.

Conclusions:

PANDORA provides a fast and accurate computational method for modeling pMHC complexes.
The tool is well-suited for large-scale structural analysis required in immunology.
PANDORA is expected to facilitate advancements in deep learning for immunology by providing high-quality 3D structural models.