Leveraging computer-aided design and artificial intelligence to develop a next-generation multi-epitope tuberculosis vaccine candidate.

ABSTRACT:

Background

Tuberculosis (TB) remains a global public health challenge. The existing Bacillus Calmette-Guérin vaccine has limited efficacy in preventing adult pulmonary TB, necessitating the development of new vaccines with improved protective effects.

Methods

Computer-aided design and artificial intelligence technologies, combined with bioinformatics and immunoinformatics approaches, were used to design a multi-epitope vaccine (MEV) against TB. Comprehensive bioinformatics analyses were conducted to evaluate the physicochemical properties, spatial structure, immunogenicity, molecular dynamics (MD), and immunological characteristics of the MEV.

Results

We constructed a MEV, designated ZL12138L, containing 13 helper T lymphocyte epitopes, 12 cytotoxic T lymphocyte epito

SUBMITTER: Zhuang L 

PROVIDER: S-EPMC11647498 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature