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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Related Experiment Video

Updated: Oct 22, 2025

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay
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A structural-based machine learning method to classify binding affinities between TCR and peptide-MHC complexes.

Kalyani Dhusia1, Zhaoqian Su1, Yinghao Wu1

  • 1Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, United States.

Molecular Immunology
|August 29, 2021
PubMed
Summary
This summary is machine-generated.

Scientists developed a machine learning model to predict T cell receptor (TCR) and peptide-MHC (pMHC) binding affinities. This approach accurately forecasts binding strength, aiding in the design of targeted immunotherapies.

Keywords:
Binding affinityRandom forest classifierTCR-pMHC complexes

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Area of Science:

  • Immunology and Computational Biology
  • T cell receptor (TCR) and peptide-MHC (pMHC) interactions
  • Machine learning applications in bioinformatics

Background:

  • T cell activation relies on TCR recognition of pMHC complexes on antigen-presenting cells.
  • TCR specificity and cross-reactivity are governed by binding affinities to pMHC.
  • Binding affinity is influenced by structural and sequence properties at TCR-pMHC interfaces.

Purpose of the Study:

  • To develop a predictive model for TCR-pMHC binding affinities using structural and sequence data.
  • To leverage publicly available structural and binding data for machine learning model training.
  • To enhance understanding of factors driving TCR-pMHC binding specificity and affinity.

Main Methods:

  • Utilized a random forest classifier trained on a large-scale benchmark dataset of TCR-pMHC complexes.
  • Input features derived from the structure and sequence of TCR-pMHC complexes.
  • Model performance evaluated using cross-validation and statistical analysis.

Main Results:

  • The machine learning model achieved approximately 75% accuracy in predicting binding affinity strength relative to a threshold.
  • Over 60% of binding affinities in the ATLAS database were classified within a 2 kcal/mol range.
  • Strong TCR-pMHC binding is associated with hydrophobic interactions involving aromatic amino acids, rather than electrostatic interactions.

Conclusions:

  • The developed computational method accurately predicts TCR-pMHC binding affinities.
  • Findings provide insights for designing engineered TCRs with enhanced epitope specificity.
  • This approach serves as a valuable tool for studying TCR-pMHC interactions.