Literature DB >> 19860671

Prediction of MHC-peptide binding: a systematic and comprehensive overview.

Esther M Lafuente1, Pedro A Reche.   

Abstract

T cell immune responses are driven by the recognition of peptide antigens (T cell epitopes) that are bound to major histocompatibility complex (MHC) molecules. T cell epitope immunogenicity is thus contingent on several events, including appropriate and effective processing of the peptide from its protein source, stable peptide binding to the MHC molecule, and recognition of the MHC-bound peptide by the T cell receptor. Of these three hallmarks, MHC-peptide binding is the most selective event that determines T cell epitopes. Therefore, prediction of MHC-peptide binding constitutes the principal basis for anticipating potential T cell epitopes. The tremendous relevance of epitope identification in vaccine design and in the monitoring of T cell responses has spurred the development of many computational methods for predicting MHC-peptide binding that improve the efficiency and economics of T cell epitope identification. In this report, we will systematically examine the available methods for predicting MHC-peptide binding and discuss their most relevant advantages and drawbacks.

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Year:  2009        PMID: 19860671     DOI: 10.2174/138161209789105162

Source DB:  PubMed          Journal:  Curr Pharm Des        ISSN: 1381-6128            Impact factor:   3.116


  56 in total

Review 1.  Major histocompatibility complex class I binding predictions as a tool in epitope discovery.

Authors:  Claus Lundegaard; Ole Lund; Søren Buus; Morten Nielsen
Journal:  Immunology       Date:  2010-05-26       Impact factor: 7.397

2.  Relationship between target antigens and major histocompatibility complex (MHC) class II genes in producing two pathogenic antibodies simultaneously.

Authors:  L R Zakka; D B Keskin; P Reche; A R Ahmed
Journal:  Clin Exp Immunol       Date:  2010-11       Impact factor: 4.330

3.  Prediction of epitopes using neural network based methods.

Authors:  Claus Lundegaard; Ole Lund; Morten Nielsen
Journal:  J Immunol Methods       Date:  2010-10-31       Impact factor: 2.303

4.  Efficacy of HLA-DRB1∗03:01 and H2E transgenic mouse strains to correlate pathogenic thyroglobulin epitopes for autoimmune thyroiditis.

Authors:  Yi-chi M Kong; Nicholas K Brown; Jeffrey C Flynn; Daniel J McCormick; Vladimir Brusic; Gerald P Morris; Chella S David
Journal:  J Autoimmun       Date:  2011-06-17       Impact factor: 7.094

5.  Mathematical modeling based on ordinary differential equations: A promising approach to vaccinology.

Authors:  Carla Rezende Barbosa Bonin; Guilherme Cortes Fernandes; Rodrigo Weber Dos Santos; Marcelo Lobosco
Journal:  Hum Vaccin Immunother       Date:  2016-12-27       Impact factor: 3.452

6.  Breadth of the CD4+ T cell response to Anaplasma marginale VirB9-1, VirB9-2 and VirB10 and MHC class II DR and DQ restriction elements.

Authors:  Kaitlyn Morse; Junzo Norimine; Jayne C Hope; Wendy C Brown
Journal:  Immunogenetics       Date:  2012-02-24       Impact factor: 2.846

7.  Resistance-associated epitopes of HIV-1C-highly probable candidates for a multi-epitope vaccine.

Authors:  Jagadish Chandrabose Sundaramurthi; Soumya Swaminathan; Luke Elizabeth Hanna
Journal:  Immunogenetics       Date:  2012-07-19       Impact factor: 2.846

8.  Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation.

Authors:  Takeshi Ishikawa
Journal:  J Comput Aided Mol Des       Date:  2016-09-13       Impact factor: 3.686

9.  An integrated approach to epitope analysis II: A system for proteomic-scale prediction of immunological characteristics.

Authors:  Robert D Bremel; E Jane Homan
Journal:  Immunome Res       Date:  2010-11-02

10.  Computer aided selection of candidate vaccine antigens.

Authors:  Darren R Flower; Isabel K Macdonald; Kamna Ramakrishnan; Matthew N Davies; Irini A Doytchinova
Journal:  Immunome Res       Date:  2010-11-03
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