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T Cell Activation and Clonal Selection

T Cell Activation and Clonal Selection

T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...

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Updated: May 6, 2026

Use of Interferon-γ Enzyme-linked Immunospot Assay to Characterize Novel T-cell Epitopes of Human Papillomavirus

Use of Interferon-γ Enzyme-linked Immunospot Assay to Characterize Novel T-cell Epitopes of Human Papillomavirus

Published on: March 8, 2012

SILVI, an open-source pipeline for T-cell epitope selection.

Joana Pissarra¹, Franck Dorkeld², Etienne Loire³

¹UMR INTERTRYP, IRD, CIRAD, University of Montpellier (I-MUSE), Montpellier, France.

|September 7, 2022

Summary

This summary is machine-generated.

SILVI is an R script that simplifies selecting potent T-cell epitopes for vaccines. It integrates and compares data from multiple prediction tools, aiding in the development of novel immunotherapies.

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Last Updated: May 6, 2026

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

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Published on: March 25, 2014

Area of Science:

Immunoinformatics
Vaccine Development
Computational Biology

Background:

High-throughput genomic screening identifies potential antigenic targets for vaccines and therapeutics.
Existing immunoinformatic tools for epitope prediction are numerous but integrating their results is challenging and time-consuming.

Purpose of the Study:

To develop an R script, SILVI, that streamlines the selection of immunogenic T-cell epitopes.
To merge and compare data from various HLA-binding prediction servers, integrating additional epitope information.

Main Methods:

Development of the open-source R script SILVI.
Integration of HLA-binding prediction data from multiple servers.
Incorporation of BLASTp alignments and physicochemical properties for epitope filtering.
Application of default and customizable filters for epitope selection.

Main Results:

SILVI facilitates the rapid selection of conserved, promiscuous, and strong-binding T-cell epitopes.
Filtering based on predicted IC50, hydrophobicity, and host protein mismatches significantly reduced epitope lists.
Demonstrated workflow using viral, bacterial, parasitic proteins, and HPV proteome with validated epitopes.

Conclusions:

SILVI assists in T-cell epitope selection for peptide-based vaccines and immunotherapies.
The script's open-source nature allows for community-driven refinement and improvement.
SILVI aids in designing more effective epitope-based vaccines and immunotherapeutic strategies.