ArticlePLoS computational biology2013
Scrutinizing MHC-I binding peptides and their limits of variation.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A Chemical Approach to Assess the Impact of Post-translational Modification on MHC Peptide Binding and Effector Cell Engagement.ACS chemical biology · 2024Article
- SARS-CoV-2-Induced Immunosuppression: A Molecular Mimicry Syndrome.Global medical genetics · 2022Article
- Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity.Pharmaceuticals (Basel, Switzerland) · 2022Review
- ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides.Briefings in bioinformatics · 2021Article
- Naïve CD8 T cell IFNγ responses to a vacuolar antigen are regulated by an inflammasome-independent NLRP3 pathway and Toxoplasma gondii ROP5.PLoS pathogens · 2020Article
- HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks.Scientific reports · 2020Article
- In silico design and optimization of selective membranolytic anticancer peptides.Scientific reports · 2019Article
- De novo design of anticancer peptides by ensemble artificial neural networks.Journal of molecular modeling · 2019Article
- Monitoring the Immunoproteasome in Live Cells Using an Activity-Based Peptide-Peptoid Hybrid Probe.Journal of the American Chemical Society · 2019Article
- Differential scanning fluorimetry based assessments of the thermal and kinetic stability of peptide-MHC complexes.Journal of immunological methods · 2016Article
- Gapped sequence alignment using artificial neural networks: application to the MHC class I system.Bioinformatics (Oxford, England) · 2016Article
- Matched Peptides: Tuning Matched Molecular Pair Analysis for Biopharmaceutical Applications.Journal of chemical information and modeling · 2015Article
- Article
- A novelOncoimmunologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Designed peptides that bind to major histocompatibility protein I (MHC-I) allomorphs bear the promise of representing epitopes that stimulate a desired immune response. A rigorous bioinformatical exploration of sequence patterns hidden in peptides that bind to the mouse MHC-I allomorph H-2K(b) is presented. We exemplify and validate these motif findings by systematically dissecting the epitope SIINFEKL and analyzing the resulting fragments for their binding potential to H-2K(b) in a thermal denaturation assay. The results demonstrate that only fragments exclusively retaining the carboxy- or amino-terminus of the reference peptide exhibit significant binding potential, with the N-terminal pentapeptide SIINF as shortest ligand. This study demonstrates that sophisticated machine-learning algorithms excel at extracting fine-grained patterns from peptide sequence data and predicting MHC-I binding peptides, thereby considerably extending existing linear prediction models and providing a fresh view on the computer-based molecular design of future synthetic vaccines. The server for prediction is available at http://modlab-cadd.ethz.ch (SLiDER tool, MHC-I version 2012).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.