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Tagging and Fusion Proteins

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...

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

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Computational epitope mapping of class I fusion proteins using low complexity supervised learning methods.

Marion F S Fischer^1,2, James E Crowe^3,4,5, Jens Meiler^1,2,6

¹Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America.

Plos Computational Biology

|December 8, 2022

Summary

This summary is machine-generated.

A new method, Accelerated class I fusion protein Epitope Mapping (AxIEM), improves prediction of antibody epitopes on flexible viral fusion proteins. This aids in understanding viral vulnerability and designing effective vaccines.

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Area of Science:

Virology
Immunology
Structural Biology

Background:

Antibody epitope mapping is crucial for understanding viral protein interactions and immune responses.
Class I viral fusion proteins exhibit metastable conformations, exposing critical epitopes targeted by neutralizing antibodies.
Mapping these transient conformations is challenging but essential for identifying viral vulnerabilities.

Purpose of the Study:

To introduce a novel computational method, Accelerated class I fusion protein Epitope Mapping (AxIEM), for predicting discontinuous antibody epitopes.
To enhance the accuracy of epitope prediction by considering fusion protein flexibility and residue environment.
To provide structural insights for vaccine development against viral fusion proteins.

Main Methods:

Development of the Accelerated class I fusion protein Epitope Mapping (AxIEM) algorithm.
Utilizing experimental epitope mapping data from various class I fusion proteins.
Analyzing residue environment and conformational flexibility for epitope prediction.

Main Results:

AxIEM demonstrates improved out-of-sample prediction accuracy for discontinuous antibody epitopes.
Epitope prediction accuracy is significantly influenced by residue environment.
The method successfully predicts conformation-dependent antibody target residues.
AxIEM identifies common epitopes across different viral fusion proteins.

Conclusions:

AxIEM offers a powerful tool for mapping antibody epitopes on flexible class I fusion proteins.
Understanding conformation-dependent epitopes is key to developing effective antiviral strategies.
The method provides valuable structural insights for rational vaccine design.