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A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes

Paper ID Volume ID Publish Year Pages File Format Full-Text
15130 1380 2013 6 PDF Available
Title
A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes
Abstract

•Combining epitope prediction methods with molecular docking techniques to identify MHC class I restricted T-cell epitopes.•Based on available experimental data, the prediction accuracy is up to 90%.•Providing a valuable step forward for the design of better vaccines.•Better understanding the activation of T-cell epitopes by MHC binding peptides.

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.

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Keywords
T-cell epitope; Computational prediction; Docking; Vaccine design
First Page Preview
A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes
Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 45, August 2013, Pages 30–35
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering