Unknown

Dataset Information

0

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


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.

SUBMITTER: Zhang XW 

PROVIDER: S-EPMC7106517 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC4051127 | biostudies-literature
| S-EPMC6899439 | biostudies-literature
| S-EPMC2241739 | biostudies-literature
| S-EPMC7274474 | biostudies-literature
| S-EPMC3004863 | biostudies-literature
| S-EPMC4894389 | biostudies-literature
| S-EPMC10976095 | biostudies-literature
| S-EPMC3581685 | biostudies-literature
| S-EPMC7814002 | biostudies-literature
| S-EPMC2875469 | biostudies-literature