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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
Published on: March 25, 2014
Peptide binding to HLA class I molecules: homogenous, high-throughput screening, and affinity assays
Mikkel Harndahl1, Sune Justesen, Kasper Lamberth
1Laboratory of Experimental Immunology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
Journal of Biomolecular Screening
|February 7, 2009
Summary
A new Luminescent Oxygen Channeling Immunoassay (LOCI) assay accurately measures peptide-HLA class I binding. This robust, high-throughput method advances understanding of human immune responses and aids in developing predictive bioinformatics tools.
Area of Science:
- Immunology
- Biochemistry
- Bioinformatics
Background:
- The Human MHC Project seeks to map peptide-HLA binding across global populations.
- Accurate peptide-HLA binding data is crucial for understanding immune responses and developing predictive tools.
Purpose of the Study:
- To present a novel, homogenous, proximity-based assay for detecting peptide binding to HLA class I molecules.
- To offer a robust and scalable method for peptide-HLA binding screening.
Main Methods:
- Utilized Luminescent Oxygen Channeling Immunoassay (LOCI) technology, also known as AlphaScreen.
- Employed a conformation-dependent anti-HLA class I antibody (W6/32) and a biotinylated recombinant HLA class I molecule.
- Developed a proximity-based signal generation system.
Main Results:
- The LOCI assay demonstrated comparable affinity measurements to traditional ELISAs but with a broader dynamic range and improved signal-to-background ratios.
- Successfully applied the assay to over 60 HLA molecules, generating more than 2 million measurements.
- The assay is adaptable for both occasional users and high-throughput screening.
Conclusions:
- The LOCI assay provides a reliable, efficient, and scalable method for measuring peptide-HLA class I binding.
- This assay facilitates large-scale data generation for the Human MHC Project and aids in the development of predictive bioinformatics tools.

