PreprintArticleVersion 1This version is not peer-reviewed
Artificial Intelligence and Machine Learning Tools Used for Mapping Some Immunogenic Epitopes within the Major Structural Proteins of the Bovine Coronavirus (BCoV) and for the In Silico Design of the Multiepitope Based Vaccines
Version 1
: Received: 21 July 2024 / Approved: 22 July 2024 / Online: 22 July 2024 (11:51:59 CEST)
How to cite:
Duraisamy, N.; Khan, M. Y.; Shah, A. U.; Nacif, R.; Cherkaoui, M.; Hemida, M. G. Artificial Intelligence and Machine Learning Tools Used for Mapping Some Immunogenic Epitopes within the Major Structural Proteins of the Bovine Coronavirus (BCoV) and for the In Silico Design of the Multiepitope Based Vaccines. Preprints2024, 2024071699. https://doi.org/10.20944/preprints202407.1699.v1
Duraisamy, N.; Khan, M. Y.; Shah, A. U.; Nacif, R.; Cherkaoui, M.; Hemida, M. G. Artificial Intelligence and Machine Learning Tools Used for Mapping Some Immunogenic Epitopes within the Major Structural Proteins of the Bovine Coronavirus (BCoV) and for the In Silico Design of the Multiepitope Based Vaccines. Preprints 2024, 2024071699. https://doi.org/10.20944/preprints202407.1699.v1
Duraisamy, N.; Khan, M. Y.; Shah, A. U.; Nacif, R.; Cherkaoui, M.; Hemida, M. G. Artificial Intelligence and Machine Learning Tools Used for Mapping Some Immunogenic Epitopes within the Major Structural Proteins of the Bovine Coronavirus (BCoV) and for the In Silico Design of the Multiepitope Based Vaccines. Preprints2024, 2024071699. https://doi.org/10.20944/preprints202407.1699.v1
APA Style
Duraisamy, N., Khan, M. Y., Shah, A. U., Nacif, R., Cherkaoui, M., & Hemida, M. G. (2024). Artificial Intelligence and Machine Learning Tools Used for Mapping Some Immunogenic Epitopes within the Major Structural Proteins of the Bovine Coronavirus (BCoV) and for the In Silico Design of the Multiepitope Based Vaccines. Preprints. https://doi.org/10.20944/preprints202407.1699.v1
Chicago/Turabian Style
Duraisamy, N., Mohammed Cherkaoui and Maged Gomaa Hemida. 2024 "Artificial Intelligence and Machine Learning Tools Used for Mapping Some Immunogenic Epitopes within the Major Structural Proteins of the Bovine Coronavirus (BCoV) and for the In Silico Design of the Multiepitope Based Vaccines" Preprints. https://doi.org/10.20944/preprints202407.1699.v1
Abstract
This study aimed (1) to map the immunogenic epitopes (B and T cells) within the major BCoV structural proteins. These epitopes are believed to induce a robust im-mune response through the interaction with major histocompatibility complex (MHC class II) molecules (2) to design some novel BCoV multi-epitope-based vac-cines. To achieve these goals, we applied several integrated In-silico prediction computational tools to map these epitopes within the major BCoV structural pro-teins. The predicted epitopes were linked to some immunostimulants such as Toll-like receptors-2 (TLR2) and TLR-4. The tertiary structure of each epitope was modeled through the Alpha fold-2 colab machine learning tools. The stability and purity of each epitope were assessed using the Ramachandran plot and the Z Score values. Each multiepitope-based vaccine candidate conjugated with the Chlorotox-in B toxin as an adjuvant. We designed the vaccine constructs using various expres-sion vectors. We also predicted the affinity binding of these vaccines with the target protein using molecular docking. Our designed multiepitope vaccine candidates per each BCoV structural protein showed high antigenicity, immunogenicity, non-allergic, non-toxic, and high water solubility. Further studies are highly en-couraged to validate the efficacy of these novel BCoV vaccines in the natural host.
Keywords
AI; In silico predction; BCoV; multiepitopes; vacciens; adjuvant; T Cell; B Cell; MHC-I; MHC-II; TLR2; TLR4
Subject
Biology and Life Sciences, Virology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
