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Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in <jats:italic>E. coli.<\/jats:italic> The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.<\/jats:p>","DOI":"10.1038\/s41598-024-59291-1","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T13:01:55Z","timestamp":1715000515000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction"],"prefix":"10.1038","volume":"14","author":[{"given":"Auwalu Saleh","family":"Mubarak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zubaida Said","family":"Ameen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdurrahman Shuaibu","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dilber Uzun","family":"Ozsahin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,6]]},"reference":[{"issue":"1","key":"59291_CR1","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1016\/S1473-3099(18)30625-X","volume":"18","author":"HH Kyu","year":"2018","unstructured":"Kyu, H. 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