Chapter title |
A Cohesive and Integrated Platform for Immunogenicity Prediction.
|
---|---|
Chapter number | 50 |
Book title |
Vaccine Design
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3389-1_50 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3388-4, 978-1-4939-3389-1
|
Authors |
Ivan Dimitrov, Mariyana Atanasova, Atanas Patronov, Darren R. Flower, Irini Doytchinova |
Editors |
Sunil Thomas |
Abstract |
In silico methods for immunogenicity prediction mine the enormous quantity of data arising from deciphered genomes and proteomes to identify immunogenic proteins. While high and productive immunogenicity is essential for vaccines, therapeutic proteins and monoclonal antibodies should be minimally immunogenic. Here, we present a cohesive platform for immunogenicity and MHC class I and/or II binding affinity prediction. The platform integrates three quasi-independent modular servers: VaxiJen, EpiJen, and EpiTOP. VaxiJen ( http://www.ddg-pharmfac.net/vaxijen ) predicts immunogenicity of proteins of different origin; EpiJen ( http://www.ddg-pharmfac.net/epijen ) predicts peptide binding to MHC class I proteins; and EpiTOP ( http://www.ddg-pharmfac.net/epitop ) predicts peptide binding to MHC class II proteins. The platform is freely accessible and user-friendly. The protocol for immunogenicity prediction is demonstrated by selecting immunogenic proteins from Mycobacterium tuberculosis and predicting how the peptide epitopes within them bind to MHC class I and class II proteins. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 21 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 4 | 19% |
Professor | 2 | 10% |
Student > Master | 2 | 10% |
Professor > Associate Professor | 2 | 10% |
Student > Ph. D. Student | 1 | 5% |
Other | 2 | 10% |
Unknown | 8 | 38% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 4 | 19% |
Medicine and Dentistry | 3 | 14% |
Agricultural and Biological Sciences | 2 | 10% |
Chemistry | 2 | 10% |
Computer Science | 1 | 5% |
Other | 0 | 0% |
Unknown | 9 | 43% |