Literature DB >> 19560824

Proteins accessible to immune surveillance show significant T-cell epitope depletion: Implications for vaccine design.

Mark Halling-Brown1, Raheel Shaban, Dan Frampton, Clare E Sansom, Matthew Davies, Darren Flower, Melanie Duffield, Richard W Titball, Vladimir Brusic, David S Moss.   

Abstract

T cell activation is the final step in a complex pathway through which pathogen-derived peptide fragments can elicit an immune response. For it to occur, peptides must form stable complexes with Major Histocompatibility Complex (MHC) molecules and be presented on the cell surface. Computational predictors of MHC binding are often used within in silico vaccine design pathways. We have previously shown that, paradoxically, most bacterial proteins known experimentally to elicit an immune response in disease models are depleted in peptides predicted to bind to human MHC alleles. The results presented here, derived using software proven through benchmarking to be the most accurate currently available, show that vaccine antigens contain fewer predicted MHC-binding peptides than control bacterial proteins from almost all subcellular locations with the exception of cell wall and some cytoplasmic proteins. This effect is too large to be explained from the undoubted lack of precision of the software or from the amino acid composition of the antigens. Instead, we propose that pathogens have evolved under the influence of the host immune system so that surface proteins are depleted in potential MHC-binding peptides, and suggest that identification of a protein likely to contain a single immuno-dominant epitope is likely to be a productive strategy for vaccine design.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19560824     DOI: 10.1016/j.molimm.2009.05.027

Source DB:  PubMed          Journal:  Mol Immunol        ISSN: 0161-5890            Impact factor:   4.407


  3 in total

1.  Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens.

Authors:  Edison Ong; Haihe Wang; Mei U Wong; Meenakshi Seetharaman; Ninotchka Valdez; Yongqun He
Journal:  Bioinformatics       Date:  2020-05-01       Impact factor: 6.937

2.  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

Review 3.  Understanding infectious agents from an in silico perspective.

Authors:  Joo Chuan Tong; Lisa F P Ng
Journal:  Drug Discov Today       Date:  2010-10-23       Impact factor: 7.851

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.