Please use this identifier to cite or link to this item:
|Title:||Prediction of class I T-cell epitopes: Evidence of presence of immunological hot spots inside antigens|
|Authors:||Srinivasan, K.N. |
|Citation:||Srinivasan, K.N., Zhang, G.L., Khan, A.M., August, J.T., Brusic, V. (2004). Prediction of class I T-cell epitopes: Evidence of presence of immunological hot spots inside antigens. Bioinformatics 20 (SUPPL. 1) : i297-i302. ScholarBank@NUS Repository.|
|Abstract:||Motivation: Processing and presentation of major histocompatibility complex class I antigens to cytotoxic T-lymphocytes is crucial for immune surveillance against intracellular bacteria, parasites, viruses and tumors. Identification of antigenic regions on pathogen proteins will play a pivotal role in designer vaccine immunotherapy. We have developed a system that not only identifies high binding T-cell antigenic epitopes, but also class I T-cell antigenic clusters termed immunological hot spots. Methods: MULTIPRED, a computational system for promiscuous prediction of HLA class I binders, uses artificial neural networks (ANN) and hidden Markov models (HMM) as predictive engines. The models were rigorously trained, tested and validated using experimentally identified HLA class I T-cell epitopes from human melanoma related proteins and human papillomavirus proteins E6 and E7. We have developed a scoring scheme for identification of immunological hot spots for HLA class I molecules, which is the sum of the highest four predictions within a window of 30 amino acids. Results: Our predictions against experimental data from four melanoma-related proteins showed that MULTIPRED ANN and HMM models could predict T-cell epitopes with high accuracy. The analysis of proteins E6 and E7 showed that ANN models appear to be more accurate for prediction of HLA-A3 hot spots and HMM models for HLA-A2 predictions. For illustration of its utility we applied MULTIPRED for prediction of promiscuous T-cell epitopes in all four SARS coronavirus structural proteins. MULTIPRED predicted HLA-A2 and HLA-A3 hot spots in each of these proteins. © Oxford University Press 2004; all rights reserved.|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 18, 2018
WEB OF SCIENCETM
checked on Dec 19, 2018
checked on Dec 28, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.