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Development of Immunocompetence

Development of Immunocompetence

The initiation of cell-mediated immunity can be observed as early as the third month of fetal growth, with active antibody-mediated immunity following approximately one month later.
The initial cells that migrate from the fetal thymus settle within the skin and epithelial tissues lining the mouth, digestive tract, and in females, the uterus and vagina. These cells, including skin-based dendritic cells, serve as antigen-presenting cells, playing a key role in T cell activation.
Subsequent T...

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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

A Cohesive and Integrated Platform for Immunogenicity Prediction.

Ivan Dimitrov¹, Mariyana Atanasova¹, Atanas Patronov^2,3

¹School of Pharmacy, Medical University of Sofia, Sofia, 1000, Bulgaria.

Methods in Molecular Biology (Clifton, N.J.)

|April 15, 2016

Summary

This study introduces a computational platform to predict protein immunogenicity and binding to MHC molecules. This tool aids in developing vaccines and minimizing immunogenicity in therapeutics.

Keywords:

Immunogenicity MHC class I binding prediction MHC class II binding prediction Proteasome cleavage TAP binding

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Area of Science:

Computational biology
Immunology
Bioinformatics

Background:

In silico methods analyze genomic and proteomic data to identify immunogenic proteins.
High immunogenicity is crucial for vaccines, while therapeutic proteins require minimal immunogenicity.
Predicting immunogenicity and MHC binding is vital for biopharmaceutical development.

Purpose of the Study:

To present a unified computational platform for predicting protein immunogenicity and MHC class I/II binding affinity.
To integrate three modular servers: VaxiJen, EpiJen, and EpiTOP.
To provide a user-friendly and freely accessible tool for researchers.

Main Methods:

The platform integrates VaxiJen for protein immunogenicity prediction.
EpiJen predicts peptide binding to MHC class I molecules.
EpiTOP predicts peptide binding to MHC class II molecules.

Main Results:

The platform successfully integrates VaxiJen, EpiJen, and EpiTOP for comprehensive immunogenicity assessment.
Demonstrated protocol using Mycobacterium tuberculosis proteins and their epitopes binding to MHC class I and II.
The developed platform is accessible and user-friendly.

Conclusions:

The presented platform offers a cohesive solution for in silico immunogenicity and MHC binding prediction.
This tool can significantly aid in the rational design of vaccines and therapeutic proteins.
Facilitates identification of immunogenic epitopes and assessment of potential adverse immune reactions.