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 Experiment Videos

Learning MHC I--peptide binding.

Nebojsa Jojic1, Manuel Reyes-Gomez, David Heckerman

  • 1Microsoft Research, Redmond, WA 98052, USA. jojic@microsoft.com

Bioinformatics (Oxford, England)
|July 29, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Strategic Template Filtering Accelerates Fragment-Based Peptide Docking.

Journal of chemical information and modeling·2026
Same author

Systematic discovery of motif-based interactions of the auxiliary domains of USP family deubiquitinases.

Nature communications·2026
Same author

Assessing VirScan serosurvey epitope profiling variability between in-clinic venous blood draw and capillary blood self-sampling device.

Microbiology spectrum·2026
Same author

CliPepPI: Scalable prediction of domain-peptide specificity using contrastive learning.

bioRxiv : the preprint server for biology·2026
Same author

Identification of ZFTA as a Novel KLHL20 Substrate and Mechanistic Insights Into Fuzzy Binding of Disordered Peptides via Biosensor Analysis and Computational Modelling.

Chembiochem : a European journal of chemical biology·2026
Same author

Echoes in AI: Quantifying lack of plot diversity in LLM outputs.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

A new adaptive double threading model improves prediction of MHC class I--peptide binding by integrating diverse data sources. This structure-based approach enhances understanding of viral evolution and immune system adaptation.

Area of Science:

  • Immunoinformatics
  • Computational Biology
  • Molecular Modeling

Background:

  • Predicting Major Histocompatibility Complex (MHC) class I--peptide binding is crucial for understanding immune responses.
  • Existing threading approaches offer a foundation but have limitations in data integration and predictive power.

Purpose of the Study:

  • To develop an improved structure-based model for predicting MHC class I--peptide binding.
  • To enhance the model's ability to generalize predictions to new MHC alleles with limited available data.

Main Methods:

  • Developed an adaptive double threading model incorporating MHC class I sequences, binding energies, and epitope/non-binder datasets.
  • Utilized 3D structures of MHC-peptide complexes alongside sequence and binding data for parameter estimation.

Related Experiment Videos

  • Applied the model to analyze HIV evolution in patient cohorts and public databases.

    • Main Results:

    • The adaptive double threading model significantly outperforms standard threading approaches in binding energy prediction.
    • The model demonstrates robust generalization capabilities for predicting binding in uncharacterized MHC alleles.
    • Analysis revealed short-, medium-, and long-term adaptations of HIV to the human immune system.

    Conclusions:

    • The adaptive double threading model offers a powerful tool for predicting MHC class I--peptide binding, especially for alleles with limited data.
    • This approach advances the study of viral evolution and host-pathogen interactions.
    • The model's ability to generalize is key for studying diverse immune responses and pathogens.