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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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Related Experiment Video

Updated: Jun 16, 2026

Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens

Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens

Published on: February 6, 2017

epiTCR: a highly sensitive predictor for TCR-peptide binding.

My-Diem Nguyen Pham¹, Thanh-Nhan Nguyen¹, Le Son Tran^1,2

¹Medical Genetics Institute, Ho Chi Minh City, Vietnam.

Bioinformatics (Oxford, England)

|April 24, 2023

Summary

This summary is machine-generated.

A new tool, epiTCR, improves T-cell receptor (TCR) and peptide binding prediction for immunotherapy by integrating large public datasets. This enhanced prediction aids in identifying neoantigens for precision cancer treatments.

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Last Updated: Jun 16, 2026

Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens

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Published on: February 6, 2017

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Published on: October 30, 2015

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells

Published on: October 22, 2012

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Predicting T-cell receptor (TCR) and peptide binding is crucial for immunotherapy development but remains challenging.
Existing prediction tools often show low true positive rates for clinically relevant epitopes.

Purpose of the Study:

To develop an improved TCR-peptide interaction prediction tool using a comprehensive dataset.
To enhance the identification of epitopes for effective T-cell responses in immunotherapy.

Main Methods:

Compiled a large dataset (>3 million pairs) from five public databases (IEDB, TBAdb, VDJdb, McPAS-TCR, 10X).
Developed epiTCR, a Random Forest-based method using TCR CDR3β and antigen sequences encoded by BLOSUM62.
Evaluated epiTCR against existing tools like NetTCR, Imrex, ATM-TCR, and pMTnet.

Main Results:

epiTCR achieved an area under the curve of 0.98 with high sensitivity (0.94) and comparable specificity (0.9).
Outperformed existing tools in predicting TCR-peptide interactions.
Identified key epitopes influencing false positives and highlighted the impact of peptide sequence diversity.

Conclusions:

epiTCR demonstrates superior performance in TCR-peptide binding prediction due to its large, integrated dataset.
The tool is valuable for identifying neoantigens in precision cancer immunotherapy.
Further research should focus on more diverse peptide data for improved model balance.