Literature DB >> 23900783

NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ.

Edita Karosiene1, Michael Rasmussen, Thomas Blicher, Ole Lund, Søren Buus, Morten Nielsen.   

Abstract

Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide-MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0 .

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Year:  2013        PMID: 23900783      PMCID: PMC3809066          DOI: 10.1007/s00251-013-0720-y

Source DB:  PubMed          Journal:  Immunogenetics        ISSN: 0093-7711            Impact factor:   2.846


  33 in total

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2.  NetMHCcons: a consensus method for the major histocompatibility complex class I predictions.

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Journal:  Immunogenetics       Date:  2011-10-20       Impact factor: 2.846

3.  Selection of representative protein data sets.

Authors:  U Hobohm; M Scharf; R Schneider; C Sander
Journal:  Protein Sci       Date:  1992-03       Impact factor: 6.725

Review 4.  Antigen presentation by MHC class II molecules: invariant chain function, protein trafficking, and the molecular basis of diverse determinant capture.

Authors:  F Castellino; G Zhong; R N Germain
Journal:  Hum Immunol       Date:  1997-05       Impact factor: 2.850

Review 5.  Designing bovine T cell vaccines via reverse immunology.

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6.  Structure of a human insulin peptide-HLA-DQ8 complex and susceptibility to type 1 diabetes.

Authors:  K H Lee; K W Wucherpfennig; D C Wiley
Journal:  Nat Immunol       Date:  2001-06       Impact factor: 25.606

7.  NetMHCpan, a method for MHC class I binding prediction beyond humans.

Authors:  Ilka Hoof; Bjoern Peters; John Sidney; Lasse Eggers Pedersen; Alessandro Sette; Ole Lund; Søren Buus; Morten Nielsen
Journal:  Immunogenetics       Date:  2008-11-12       Impact factor: 2.846

8.  Allele frequency net: a database and online repository for immune gene frequencies in worldwide populations.

Authors:  Faviel F Gonzalez-Galarza; Stephen Christmas; Derek Middleton; Andrew R Jones
Journal:  Nucleic Acids Res       Date:  2010-11-09       Impact factor: 16.971

9.  Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method.

Authors:  Morten Nielsen; Claus Lundegaard; Ole Lund
Journal:  BMC Bioinformatics       Date:  2007-07-04       Impact factor: 3.169

10.  NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence.

Authors:  Morten Nielsen; Claus Lundegaard; Thomas Blicher; Kasper Lamberth; Mikkel Harndahl; Sune Justesen; Gustav Røder; Bjoern Peters; Alessandro Sette; Ole Lund; Søren Buus
Journal:  PLoS One       Date:  2007-08-29       Impact factor: 3.240

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  109 in total

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2.  Antigenic Determinants of the Bilobal Cockroach Allergen Bla g 2.

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3.  Improved methods for predicting peptide binding affinity to MHC class II molecules.

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4.  Designing immunogenic peptides.

Authors:  Darren R Flower
Journal:  Nat Chem Biol       Date:  2013-12       Impact factor: 15.040

Review 5.  Recent Trends in System-Scale Integrative Approaches for Discovering Protective Antigens Against Mycobacterial Pathogens.

Authors:  Aarti Rana; Shweta Thakur; Girish Kumar; Yusuf Akhter
Journal:  Front Genet       Date:  2018-11-27       Impact factor: 4.599

6.  Quantification of Uncertainty in Peptide-MHC Binding Prediction Improves High-Affinity Peptide Selection for Therapeutic Design.

Authors:  Haoyang Zeng; David K Gifford
Journal:  Cell Syst       Date:  2019-06-05       Impact factor: 10.304

7.  Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data.

Authors:  Christian Garde; Sri H Ramarathinam; Emma C Jappe; Morten Nielsen; Jens V Kringelum; Thomas Trolle; Anthony W Purcell
Journal:  Immunogenetics       Date:  2019-06-10       Impact factor: 2.846

8.  Evaluating a Multiscale Mechanistic Model of the Immune System to Predict Human Immunogenicity for a Biotherapeutic in Phase 1.

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Journal:  AAPS J       Date:  2019-07-24       Impact factor: 4.009

9.  Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules.

Authors:  Massimo Andreatta; Vanessa I Jurtz; Thomas Kaever; Alessandro Sette; Bjoern Peters; Morten Nielsen
Journal:  Immunology       Date:  2017-06-19       Impact factor: 7.397

Review 10.  Pathogenic CD4+ T cells in patients with asthma.

Authors:  Lyndsey M Muehling; Monica G Lawrence; Judith A Woodfolk
Journal:  J Allergy Clin Immunol       Date:  2017-04-22       Impact factor: 10.793

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