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Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches.

ABSTRACT: The Monkeypox virus, an Orthopoxvirus with zoonotic origins, has been responsible for a growing number of human infections reminiscent of smallpox since May 2022, as reported by the World Health Organization. As of now, there are no established medical treatments for managing Monkeypox infections. In this study, we used machine learning to select conserved epitopes. Proteins were determined using Reverse Vaccinology and Gene Ontology subcellular localization, and their epitopes were predicted. NextClade was used to calculate the number of mutations in each amino acid position using 2433 Monkeypox sequences. The Unsupervised Nearest Neighbor machine learning algorithm and ideal matrix [0 0] were used to calculate the conservancy score of epitopes. Six proteins were determined for epitope prediction. Finally, 47 MHC-I epitopes, 5 MHC-II epitopes, and 10 Linear B cell epitopes were discovered. Our method can select epitopes for vaccine design to prevent viruses with accelerated evolution and high mutation rate.

SUBMITTER: Izadi M

PROVIDER: S-EPMC10839993 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches.

Izadi Mohammad M Mirzaei Fatemeh F Bagherzadeh Mohammad Aref MA Ghiabi Shamim S Khalifeh Alireza A

Heliyon 20240123 3

The Monkeypox virus, an Orthopoxvirus with zoonotic origins, has been responsible for a growing number of human infections reminiscent of smallpox since May 2022, as reported by the World Health Organization. As of now, there are no established medical treatments for managing Monkeypox infections. In this study, we used machine learning to select conserved epitopes. Proteins were determined using Reverse Vaccinology and Gene Ontology subcellular localization, and their epitopes were predicted. N ...[more]

PMID: 38318007

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Project description:The world has faced unprecedented disruptions like global quarantine and the COVID-19 pandemic due to SARS-CoV-2. To combat these unsettling situations, several effective vaccines have been developed and are currently being used. However, the emergence of new variants due to the high mutation rate of SARS-CoV-2 challenges the efficacy of existing vaccines and has highlighted the need for novel vaccines that will be effective against various SARS-CoV-2 variants. In this study, we exploited the four structural proteins of SARS-CoV-2 to execute a potential multi-epitope vaccine against SARS-CoV-2 and its variants. The vaccine was designed by utilizing the antigenic, non-toxic, and non-allergenic B-cell and T-cell epitopes, which were selected from conserved regions of viral proteins. To build a vaccine construct, epitopes were connected through different linkers and an adjuvant was also attached at the start of the construct to enhance the immunogenicity and specificity of the epitopes. The vaccine construct was then screened through the aforementioned filters and it scored 0.6019 against the threshold of 0.4 on VexiJen 2.0 which validates its antigenicity. Toll-like receptors (i.e., TLR2, TLR3, TLR4, TLR5, and TLR8) and vaccine construct were docked by Cluspro 2.0, and TLR8 showed strong interaction with construct having a maximum negative binding energy of - 1577.1 kCal/mole. C-IMMSIM's immune simulations over three doses of the vaccine and iMODS' molecular dynamic simulations were executed to assess the reliability of the docked complexes. The stability of the vaccine construct was evaluated through the physicochemical analyses and the findings suggested that the manufactured vaccine is stable under a wide range of circumstances and can trigger immune responses against various SARS-CoV-2 variants (due to conserved epitopes). However, to strengthen the formulation of the vaccine and assess its safety and effectiveness, additional investigations and studies are required to support the computational data of this research at in-vitro and in-vivo levels.Supplementary informationThe online version contains supplementary material available at 10.1007/s40203-023-00156-2.

Dataset Information

Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches.

Publications

Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches.

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