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

MHCII3D-Robust Structure Based Prediction of MHC II Binding Peptides.

International journal of molecular sciences - 22 Dec 2020

Laimer Josef, Lackner Peter

Abstract excerpt

Knowledge of MHC II binding peptides is highly desired in immunological research, particularly in the context of cancer, autoimmune diseases, or allergies. The most successful prediction methods are based on machine learning methods trained on sequences of experimentally characterized binding peptides. Here, we describe a complementary approach called MHCII3D, which is based on structural scaffolds of MHC...

Read the complete abstract on PubMed

Topics

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

Share to Epitopes
Share to Histocompatibility Antigens Class II
Share to HLA-DRB1 Chains

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Internet

Share to Machine Learning

Share to Peptides

Share to Reproducibility of Results

Share to Software

Source

Open in PubMed Open full text

Identifiers

PMID: 33374958
DOI: 10.3390/ijms22010012
PMCID: PMC7792572

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

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

MHCII3D-Robust Structure Based Prediction of MHC II Binding Peptides.PMID 33374958Read ↗

EpiDOCK: a molecular docking-based tool for MHC class II binding prediction.Semantic proximity Read ↗

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

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopesSemantic proximity Read ↗

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes.Semantic proximity Read ↗

Evaluating Cross-linking-driven integrative modeling in peptide-HLAII complexes prediction with insights for refining predictive accuracySemantic proximity Read ↗

Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopesSemantic proximity Read ↗

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

Prediction of HLA-DQ3.2β Ligands: evidence of multiple registers in class II binding peptidesSemantic proximity Read ↗

epitopepredict: A tool for integrated MHC binding predictionSemantic proximity Read ↗

Discordant results among major histocompatibility complex binding affinity prediction toolsSemantic proximity Read ↗

Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system.Semantic proximity Read ↗

A comprehensive analysis of the IEDB MHC class-I automated benchmark.Semantic proximity Read ↗

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