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

535
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...
535
T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

680
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...
680

You might also read

Related Articles

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

Sort by
Same author

DDX3X is a Cl<sup>-</sup>-sensitive RNA helicase.

Science signaling·2026
Same author

Integrated reiterative pipeline for rapid epitope-based pan-alphavirus vaccines.

Science advances·2026
Same author

Antigenicity of key hepatitis C virus E1E2 glycoprotein neutralizing sites is genotype independent.

The Journal of general virology·2026
Same author

Structural insights into clonal restriction and diversity in T cell recognition of two immunodominant SARS-CoV-2 nucleocapsid epitopes.

Nature communications·2025
Same author

Engineering HIV antibodies with enhanced breadth and potency of neutralization through multistate affinity maturation.

bioRxiv : the preprint server for biology·2025
Same author

Computational Prioritization of T Cell Epitopes to Overcome HLA Restriction and Antigenic Diversity in <i>Plasmodium falciparum</i>.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Jun 11, 2025

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

11.4K

Exploring the potential of structure-based deep learning approaches for T cell receptor design.

Helder V Ribeiro-Filho1, Gabriel E Jara1, João V S Guerra1,2

  • 1Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, São Paulo, Brazil.

Plos Computational Biology
|September 30, 2024
PubMed
Summary

Deep learning models show promise for designing T cell receptors (TCRs) for cancer immunotherapy. These AI methods offer advantages over traditional approaches for engineering TCRs to target specific peptides.

More Related Videos

Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain
08:48

Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain

Published on: October 25, 2016

8.5K
Non-Viral Engineering of Primary Human T Cells via Homology-Mediated End-Joining Targeted Integration of Large DNA Templates
06:10

Non-Viral Engineering of Primary Human T Cells via Homology-Mediated End-Joining Targeted Integration of Large DNA Templates

Published on: May 9, 2025

140

Related Experiment Videos

Last Updated: Jun 11, 2025

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

11.4K
Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain
08:48

Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain

Published on: October 25, 2016

8.5K
Non-Viral Engineering of Primary Human T Cells via Homology-Mediated End-Joining Targeted Integration of Large DNA Templates
06:10

Non-Viral Engineering of Primary Human T Cells via Homology-Mediated End-Joining Targeted Integration of Large DNA Templates

Published on: May 9, 2025

140

Area of Science:

  • Computational biology
  • Immunoinformatics
  • Protein engineering

Background:

  • Deep learning has advanced protein modeling and design, enabling novel protein creation.
  • Engineering T cell receptors (TCRs) is a promising strategy for cancer immunotherapy.
  • Existing physics-based methods face challenges in designing TCRs due to interface complexities.

Purpose of the Study:

  • To explore the potential of deep learning methods (ProteinMPNN, ESM-IF1) for designing fixed-backbone TCRs.
  • To evaluate TCR designs for binding target antigenic peptides presented by MHC.
  • To compare deep learning approaches against classical physics-based methods.

Main Methods:

  • Utilized structure-based deep learning protein design tools: ProteinMPNN and ESM-IF1.
  • Designed fixed-backbone TCRs targeting specific peptide-MHC complexes.
  • Employed a comprehensive suite of sequence- and structure-based evaluation metrics.

Main Results:

  • Deep learning methods demonstrated potential in designing TCRs for specific peptide-MHC binding.
  • These AI-driven approaches showed benefits compared to traditional physics-based design.
  • Identified areas for improvement in current deep learning-based TCR design.

Conclusions:

  • Structure-based deep learning holds significant promise for advancing TCR-based immunotherapeutic design.
  • Further refinement of AI methods is needed to overcome current limitations in TCR engineering.
  • This study provides a foundation for developing next-generation TCR-targeting cancer therapies.