Home LiteratureArticle Details
PMID: 19519456 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't Review

Predicting affinity and specificity of antigenic peptide binding to major histocompatibility class I molecules.

Current protein & peptide science ·Vol. 10 ·No. 3 ·2009-06-00 ·Pages 286-96

Sieker F, May A, Zacharias M

Abstract

Major Histo-Compatibility (MHC) class I molecules are major agents of the mammalian adaptive immune system. Class I molecules bind short antigenic peptides with a length of 8-10 residues in the Endoplasmatic Reticulum (ER) and after transport to the cell surface the peptides are presented to T-lymphocytes. The binding site of class I molecules is formed by a deep cleft between two alpha-helices at top of an extended beta-sheet. Only tightly bound high-affinity peptides have a chance to reach the cell surface and trigger an immune response. It is therefore of great interest to identify possible high-affinity antigenic peptides that could be used as vaccines to help the immune system to detect viral infections or kill malignant cells. A large number of crystal structures of antigenic peptides in complex with class I alleles have been determined that allow to understand the structural details important for peptide binding. Biophysical and biochemical analysis of peptide-class I complexes has resulted in a number of rules concerning the selection of high-affinity peptides. However, an accurate prediction of allele specific peptide-binding is still not possible. This issue is currently addressed by various computational tools developed by the bioinformatics community. The computational efforts range from statistical analysis of peptide motifs stored in databases to application of neural network methods and support vector machine approaches. In addition, structure based approaches to predict class I binding specificity including molecular modeling and molecular dynamics (MD) simulations will also be presented.

MeSH Terms
Epitopes/immunology Histocompatibility Antigens Class I/chemistry,immunology Humans Markov Chains Neural Networks, Computer Peptides/chemistry,immunology Protein Binding
Chemicals
Epitopes Histocompatibility Antigens Class I Peptides
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Sieker Florian
School of Engineering and Science, Jacobs University Bremen, D-28759 Bremen, Germany.
May Andreas
Zacharias Martin
Article Info
Journal
Current protein & peptide science
Abbr.
Curr Protein Pept Sci
ISSN
1389-2037
Published
2009-06-00
Pages
286-96
Language
English
Region
United Arab Emirates
NLM ID
100960529
Subset
IM
Analysis Services
Analysis Services

Contact

No. 2 Wenbo Road, Zhangqiu District, Jinan, Shandong

Qilu Normal University · Genelibs Bioinformatics Lab

750 Shunhua Rd, Jinan

2F, Bldg F, University Science Park

Tel: 0531-88819269

WeChat Official Account

Follow our WeChat subscription account for real-time updates and the latest in medical and biological research.


Business Email

E-mail: product@genelibs.com