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Updated: Jun 7, 2026

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay

Published on: October 30, 2015

Benchmarking TCR-pMHC structure prediction: a unified evaluation and CDR3-based functional insights.

Jiadong Lu^1,2, Xinyuan Zhu³, Xinting Hu¹

¹School of Artificial Intelligence and Data Science, University of Science and Technology of China, Huangshan Road, Shushan District, Hefei 230027, Anhui, China.

Briefings in Bioinformatics

|June 5, 2026

Summary

This study benchmarks T cell receptor (TCR) and peptide-major histocompatibility complex (pMHC) structure prediction models. MSA-based methods, particularly AlphaFold3, show superior accuracy, with the TCR CDR3 pLDDT score predicting structural and functional relevance.

Keywords:

AlphaFold TCR–pMHC benchmark protein structure prediction

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Last Updated: Jun 7, 2026

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay

Published on: October 30, 2015

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Area of Science:

Immunology
Structural Biology
Computational Biology

Background:

T cell receptor (TCR) and peptide-major histocompatibility complex (pMHC) interactions are crucial for adaptive immunity.
Accurate atomic-level modeling of these interactions is vital but lacks systematic evaluation of prediction tools.

Purpose of the Study:

To benchmark various T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) structure prediction models.
To identify the most accurate and reliable methods for modeling these critical immune interactions.

Main Methods:

A comprehensive benchmark was conducted using 70 previously unseen TCR-pMHC complexes.
13 structure prediction models were evaluated, including Multiple Sequence Alignment (MSA)-based, Predicted Local Distance Difference (PLD)-based, and docking-based approaches.

Main Results:

MSA-based methods, especially AlphaFold3, demonstrated superior modeling accuracy and docking quality.
The p-value of the difference in the TCR CDR3 region was identified as a reliable indicator of structural correctness and functional relevance.
Reranking using the TCR CDR3 pLDDT score improved Top-1 success by up to 4.3% and captured 75.3% of mutation-induced affinity changes.

Conclusions:

MSA-based approaches, particularly AlphaFold3, are the most effective for TCR-pMHC structure prediction.
The TCR CDR3 pLDDT score enhances the practical utility of predicted structures for assessing functional relevance and guiding further research.