Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

808
Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
Before encountering any antigen, lymphocytes express these receptors. On B cells, the antigen receptor is a membrane-bound antibody molecule called BCR; on T cells, it is a T cell receptor or TCR. B and T cell receptors are composed of two...
808
Conserved Binding Sites01:49

Conserved Binding Sites

4.4K
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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.4K
T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

5.2K
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...
5.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

SARS-CoV-2 infection is associated with hypothalamic orexin suppression and persistent cortical NeuN attenuation.

Journal of neuroinflammation·2026
Same author

Ligand-receptor interaction profiling as a predictive biomarker for anti-PD-1 therapy response in melanoma.

Clinical and experimental medicine·2025
Same author

Correction: Differing Approaches to Pain Management for Intrauterine Device Insertion and Maintenance: A Scoping Review.

Cureus·2025
Same author

Integrative Analysis of ATAC-Seq and RNA-Seq through Machine Learning Identifies 10 Signature Genes for Breast Cancer Intrinsic Subtypes.

Biology·2024
Same author

Differing Approaches to Pain Management for Intrauterine Device Insertion and Maintenance: A Scoping Review.

Cureus·2024
Same author

Distinct cellular composition between normal surgical margins and tumor tissues in oral squamous cell carcinoma.

Genes & genomics·2023

Related Experiment Video

Updated: Sep 15, 2025

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay
19:05

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

Published on: October 30, 2015

12.5K

TCR-epiDiff: solving dual challenges of TCR generation and binding prediction.

Se Yeon Seo1, Je-Keun Rhee1

  • 1Department of Bioinformatics & Life Science, Soongsil University, Seoul 06978, Korea.

Bioinformatics (Oxford, England)
|July 15, 2025
PubMed
Summary
This summary is machine-generated.

We developed TCR-epiDiff, a deep learning model that generates epitope-specific T cell receptors (TCRs) and predicts TCR-epitope binding. This advances vaccine design and immunotherapy development.

More Related Videos

Streamlined Single Cell TCR Isolation and Generation of Retroviral Vectors for In Vitro and In Vivo Expression of Human TCRs
11:21

Streamlined Single Cell TCR Isolation and Generation of Retroviral Vectors for In Vitro and In Vivo Expression of Human TCRs

Published on: September 10, 2017

9.6K
Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
09:53

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

11.5K

Related Experiment Videos

Last Updated: Sep 15, 2025

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay
19:05

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

Published on: October 30, 2015

12.5K
Streamlined Single Cell TCR Isolation and Generation of Retroviral Vectors for In Vitro and In Vivo Expression of Human TCRs
11:21

Streamlined Single Cell TCR Isolation and Generation of Retroviral Vectors for In Vitro and In Vivo Expression of Human TCRs

Published on: September 10, 2017

9.6K
Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
09:53

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

11.5K

Area of Science:

  • Immunoinformatics
  • Computational Biology
  • Deep Learning

Background:

  • T cell receptors (TCRs) are key to adaptive immunity, recognizing specific antigens.
  • Designing epitope-specific TCRs for vaccines and immunotherapies is challenging due to sequence diversity and binding complexity.

Purpose of the Study:

  • To develop a deep learning model for generating epitope-specific TCRs.
  • To create a model for predicting TCR-epitope binding.
  • To provide a comprehensive tool for advancing targeted immunotherapies.

Main Methods:

  • Proposed TCR-epiDiff, a diffusion-based deep learning model.
  • Integrated epitope information using ProtT5-XL for TCR sequence embedding.
  • Employed a denoising diffusion probabilistic model for TCR sequence generation.

Main Results:

  • Generated biologically plausible, epitope-specific TCRs using external validation datasets.
  • Developed a TCR-epitope binding predictor with robust performance on validation data.
  • Demonstrated a comprehensive solution for TCR generation and binding prediction.

Conclusions:

  • TCR-epiDiff offers a powerful approach for de novo generation of epitope-specific TCRs.
  • The model enhances TCR-epitope binding prediction capabilities.
  • This work provides valuable insights into immune diversity and facilitates targeted immunotherapy development.