Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Conserved Binding Sites

Conserved Binding Sites

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...

Antigens Involved in Adaptive Immunity

Antigens Involved in Adaptive Immunity

An antigen is any substance the immune system identifies as foreign and potentially harmful to the body, prompting an immune response. Antigens have two functional properties: immunogenicity and reactivity. Immunogenicity is the ability of an antigen to stimulate a specific immune response. At the same time, reactivity describes the antigen's ability to react with the cells and antibodies produced in response to it.
Complete Antigens
Complete antigens possess both immunogenicity and...

You might also read

Related Articles

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

Sort by

Same author

Review: application and opportunities for machine learning and artificial intelligence in preclinical immunogenicity risk assessment.

Frontiers in immunology·2026

Same author

Cancer epitope prediction tools and analysis pipelines in CEDAR.

Nucleic acids research·2026

Same author

NetMHCIIphosPan: A Machine Learning Tool for Predicting HLA Class II Antigen Presentation of Phosphorylated Peptides.

Journal of proteome research·2026

Same author

Translational bioinformatics stalls at implementation.

Briefings in bioinformatics·2026

Same author

Standardising image registration and dose mapping for thoracic reirradiation: A national multi-centre benchmarking study.

Physics and imaging in radiation oncology·2026

Same author

NetMHCIIphosPan: a machine learning tool for predicting HLA class II antigen presentation of phosphorylated peptides.

bioRxiv : the preprint server for biology·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026

Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026

Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 28, 2026

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction.

Morten Nielsen¹, Ole Lund

¹Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark. mniel@cbs.dtu.dk

BMC Bioinformatics

|September 22, 2009

Summary

This summary is machine-generated.

A new artificial neural network method, NN-align, accurately predicts which peptides bind to MHC class II molecules. This tool improves understanding of immune responses and host-pathogen interactions.

More Related Videos

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Related Experiment Videos

Last Updated: Apr 28, 2026

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Immunology
Computational Biology
Bioinformatics

Background:

Major histocompatibility complex (MHC) molecules are crucial for adaptive immunity.
MHC class II molecules present extracellular peptides to helper T cells, influencing immune responses.
Predicting peptide-MHC binding is vital for understanding host-pathogen interactions.

Purpose of the Study:

To develop a novel method for predicting peptide binding to MHC class II molecules.
To improve the accuracy of MHC class II peptide binding predictions.

Main Methods:

Developed NN-align, an artificial neural network-based method.
Employed a novel training algorithm to correct for bias in training data.
Incorporated flanking residue information to enhance prediction accuracy.

Main Results:

NN-align simultaneously identifies the MHC class II binding core and binding affinity.
The method demonstrates improved prediction accuracy compared to existing algorithms.
NN-align outperforms state-of-the-art methods on a large benchmark dataset.

Conclusions:

NN-align is a competitive tool for MHC class II peptide binding prediction.
The method offers improved accuracy and is publicly available for use.