Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Predicting peptide binding affinities to MHC molecules using a modified semi-empirical scoring function.

PloS one - 1 Jan 2011

Liao Webber W P, Arthur Jonathan W

Abstract excerpt

The Major Histocompatibility Complex (MHC) plays an important role in the human immune system. The MHC is involved in the antigen presentation system assisting T cells to identify foreign or pathogenic proteins. However, an MHC molecule binding a self-peptide may incorrectly trigger an immune response and cause an autoimmune disease, such as multiple sclerosis. Understanding the molecular mechanism of this...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Histocompatibility Antigens Class II
Share to HLA-A2 Antigen
Share to HLA-DR Serological Subtypes

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to HLA-DR1 Antigen

Share to Immune System

Share to Major Histocompatibility Complex

Share to Multiple Sclerosis

Share to Regression Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 21966412
DOI: 10.1371/journal.pone.0025055
PMCID: PMC3178607

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Predicting peptide binding affinities to MHC molecules using a modified semi-empirical scoring function.PMID 21966412Read ↗

Predicting Binding Affinities of Protein Ligands from Three-Dimensional Models: Application to Peptide Binding to Class I Major Histocompatibility ProteinsSemantic proximity Read ↗

Class II HLA-peptide binding prediction using structural principles.Semantic proximity Read ↗

Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets.Semantic proximity Read ↗

A practical guide to structure-based prediction of MHC-binding peptides.Semantic proximity Read ↗

Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy.Semantic proximity Read ↗

Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA0201Semantic proximity Read ↗

Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules.Semantic proximity Read ↗

Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment.Semantic proximity Read ↗

Integrating peptides' sequence and energy of contact residues information improves prediction of peptide and HLA-I binding with unknown alleles.Semantic proximity Read ↗

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships.Semantic proximity Read ↗

Efficient peptide-MHC-I binding prediction for alleles with few known binders.Semantic proximity Read ↗

Structural properties of MHC class II ligands, implications for the prediction of MHC class II epitopes.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.