Literature DB >> 15987883

Integrated modeling of the major events in the MHC class I antigen processing pathway.

Pierre Dönnes1, Oliver Kohlbacher.   

Abstract

Rational design of epitope-driven vaccines is a key goal of immunoinformatics. Typically, candidate selection relies on the prediction of MHC-peptide binding only, as this is known to be the most selective step in the MHC class I antigen processing pathway. However, proteasomal cleavage and transport by the transporter associated with antigen processing (TAP) are essential steps in antigen processing as well. While prediction methods exist for the individual steps, no method has yet offered an integrated prediction of all three major processing events. Here we present WAPP, a method combining prediction of proteasomal cleavage, TAP transport, and MHC binding into a single prediction system. The proteasomal cleavage site prediction employs a new matrix-based method that is based on experimentally verified proteasomal cleavage sites. Support vector regression is used for predicting peptides transported by TAP. MHC binding is the last step in the antigen processing pathway and was predicted using a support vector machine method, SVMHC. The individual methods are combined in a filtering approach mimicking the natural processing pathway. WAPP thus predicts peptides that are cleaved by the proteasome at the C terminus, transported by TAP, and show significant affinity to MHC class I molecules. This results in a decrease in false positive rates compared to MHC binding prediction alone. Compared to prediction of MHC binding only, we report an increased overall accuracy and a lower rate of false positive predictions for the HLA-A*0201, HLA-B*2705, HLA-A*01, and HLA-A*03 alleles using WAPP. The method is available online through our prediction server at http://www-bs.informatik.uni-tuebingen.de/WAPP

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Year:  2005        PMID: 15987883      PMCID: PMC2279325          DOI: 10.1110/ps.051352405

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  49 in total

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Journal:  Curr Opin Immunol       Date:  1999-04       Impact factor: 7.486

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Journal:  J Mol Biol       Date:  1999-03-05       Impact factor: 5.469

3.  Substrate selection by transporters associated with antigen processing occurs during peptide binding to TAP.

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Journal:  Mol Immunol       Date:  1998-06       Impact factor: 4.407

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Journal:  Proteins       Date:  1998-12-01

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Authors:  V Brusic; G Rudy; L C Harrison
Journal:  Nucleic Acids Res       Date:  1998-01-01       Impact factor: 16.971

6.  A giant protease with potential to substitute for some functions of the proteasome.

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Journal:  Science       Date:  1999-02-12       Impact factor: 47.728

7.  Immune recognition of a human renal cancer antigen through post-translational protein splicing.

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Journal:  Nature       Date:  2004-01-15       Impact factor: 49.962

8.  Neural network-based prediction of candidate T-cell epitopes.

Authors:  M C Honeyman; V Brusic; N L Stone; L C Harrison
Journal:  Nat Biotechnol       Date:  1998-10       Impact factor: 54.908

9.  Relationship between peptide selectivities of human transporters associated with antigen processing and HLA class I molecules.

Authors:  S Daniel; V Brusic; S Caillat-Zucman; N Petrovsky; L Harrison; D Riganelli; F Sinigaglia; F Gallazzi; J Hammer; P M van Endert
Journal:  J Immunol       Date:  1998-07-15       Impact factor: 5.422

10.  Interferon-gamma can stimulate post-proteasomal trimming of the N terminus of an antigenic peptide by inducing leucine aminopeptidase.

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Journal:  J Biol Chem       Date:  1998-07-24       Impact factor: 5.157

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  32 in total

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Authors:  Juan Cui; Lian Yi Han; Hong Huang Lin; Zhi Qun Tang; Li Jiang; Zhi Wei Cao; Yu Zong Chen
Journal:  Immunogenetics       Date:  2006-07-11       Impact factor: 2.846

2.  A modular concept of HLA for comprehensive peptide binding prediction.

Authors:  David S DeLuca; Barbara Khattab; Rainer Blasczyk
Journal:  Immunogenetics       Date:  2006-11-22       Impact factor: 2.846

3.  A systematic bioinformatics approach for selection of epitope-based vaccine targets.

Authors:  Asif M Khan; Olivo Miotto; A T Heiny; Jerome Salmon; K N Srinivasan; Eduardo J M Nascimento; Ernesto T A Marques; Vladimir Brusic; Tin Wee Tan; J Thomas August
Journal:  Cell Immunol       Date:  2007-04-16       Impact factor: 4.868

4.  Designing immunogenic peptides.

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

5.  Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor.

Authors:  Yuanqiang Wang; Yong Lin; Mao Shu; Rui Wang; Yong Hu; Zhihua Lin
Journal:  J Mol Model       Date:  2013-04-13       Impact factor: 1.810

6.  NetCTLpan: pan-specific MHC class I pathway epitope predictions.

Authors:  Thomas Stranzl; Mette Voldby Larsen; Claus Lundegaard; Morten Nielsen
Journal:  Immunogenetics       Date:  2010-04-09       Impact factor: 2.846

7.  TAP Hunter: a SVM-based system for predicting TAP ligands using local description of amino acid sequence.

Authors:  Tze Hau Lam; Hiroshi Mamitsuka; Ee Chee Ren; Joo Chuan Tong
Journal:  Immunome Res       Date:  2010-09-27

8.  Graph-theoretical formulation of the generalized epitope-based vaccine design problem.

Authors:  Emilio Dorigatti; Benjamin Schubert
Journal:  PLoS Comput Biol       Date:  2020-10-23       Impact factor: 4.475

Review 9.  Computational genomics tools for dissecting tumour-immune cell interactions.

Authors:  Hubert Hackl; Pornpimol Charoentong; Francesca Finotello; Zlatko Trajanoski
Journal:  Nat Rev Genet       Date:  2016-07-04       Impact factor: 53.242

10.  FRED--a framework for T-cell epitope detection.

Authors:  Magdalena Feldhahn; Pierre Dönnes; Philipp Thiel; Oliver Kohlbacher
Journal:  Bioinformatics       Date:  2009-07-04       Impact factor: 6.937

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