Giuseppe
Emeritus
[Source: US National Library of Medicine, full text: (LINK). Abstract, edited.]
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Comput Biol Chem. 2013 Apr 18;45C:30-35. doi: 10.1016/j.compbiolchem.2013.03.003. [Epub ahead of print]
A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes.
Zhang XW.
Source: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou, China. Electronic address: snow_dance@sina.com.
Abstract
In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.
Copyright ? 2013 Elsevier Ltd. All rights reserved.
PMID: 23666426
[PubMed - as supplied by publisher]
-A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes.
Zhang XW.
Source: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou, China. Electronic address: snow_dance@sina.com.
Abstract
In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.
Copyright ? 2013 Elsevier Ltd. All rights reserved.
PMID: 23666426
[PubMed - as supplied by publisher]
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