Background: Tuberculosis remains a major global threat. Two billion of the world's population is latently infected with Mycobacterium tuberculosis and is at the risk of progression to active disease. Bacillus Calmette-Guerin (BCG), as the only licensed vaccine, has prophylaxis strategy, which protects children from disseminated form of tuberculosis. Therefore, postexposure vaccine strategy, which targets individuals with latent tuberculosis infection, is an important strategy to control this disease globally. Objectives: In the present study, we designed a novel postexposure multi-epitope DNA construct based on 3 latency-associated antigens of Rv2029c, Rv2031c, and Rv2627c and microtubule-associated protein light chain 3 (LC3) as a hallmark protein of the autophagy system. Methods: A mouse construct was designed based on predicted MHC class I-and class II-restricted T-cell epitopes that fused together tandemly. MHC class I-and class II-restricted epitopes were linked by AAY and GPGPG motifs, respectively. LC3 directly fused to the MHC class II-restricted epitopes at the C-terminus of the peptide. The varieties of expressed construct features were analyzed by bioinformatics tools. Finally, construct codons were optimized and mRNA structure of optimized construct was analyzed. Results: MHC class I-and class II-predicted epitopes showed a high potential to binding to human HLAs alleles, with global broad-spectrum population coverage. The construct had no allergenicity, and the analysis indicated a desirable antigenicity of the construct. The construct had several posttranslational modifications, no signal peptide, and cytoplasmic localization with high score. Also, mRNA analysis showed low Delta G which demonstrated high stability and efficient translation. Conclusions: The results revealed that the novel multi-epitope DNA construct could be an effective candidate in tuberculosis vaccine development, and it is qualified to investigate its potential to induce CD4 and CD8 T-cell immune response in the experimental animal model.