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COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.

ABSTRACT: To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also propose that an "Sp/Nsp cocktail vaccine" containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.

SUBMITTER: Ong E

PROVIDER: S-EPMC7350702 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.

Ong Edison E Wong Mei U MU Huffman Anthony A He Yongqun Y

Frontiers in immunology 20200703

To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safe ...[more]

PMID: 32719684

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Project description:BackgroundMultidrug-resistant Pseudomonas aeruginosa has become a major cause of severe infections. Due to the lack of approved vaccines, this study has presented putative vaccine candidates against it.MethodsP. aeruginosa 24Pae112 as a reference strain was retrieved from GenBank database. The surface-exposed, antigenic, non-allergenic, and non-homologous human proteins were selected. The conserved domains of selected proteins were evaluated, and the prevalence of proteins was assessed among 395 genomes. Next, linear and conformational B-cell epitopes, and human MHC II binding sites were determined. Finally, five conserved and highly antigenic B-cell epitopes from OMPs were implanted on the three platforms as multi-epitope vaccines, including FliC, the bacteriophage T7 tail, and the cell wall-associated transporter proteins. The immunoreactivity was investigated using molecular docking and immune simulation. Furthermore, molecular dynamics simulation was done to refine the chimeric cell-wall-associated transporter-TLR4 complex as the best interaction.ResultsAmong 6494 total proteins of P. aeruginosa 24Pae112, 16 proteins (seven OMPs and nine secreted) were ideal according to the defined criteria. These proteins had a molecular weight of 110 kDa and were prevalent in ≥ 75% of P. aeruginosa genomes. Among the presented multi-epitope vaccines, the chimeric cell-wall-associated transporter had the strongest interaction with TLR4. Moreover, the immune simulation response revealed that the bacteriophage T7 tail chimeric protein had the strongest ability to stimulate the immune system. In addition, molecular docking and molecular dynamic simulation indicated the proper and stable interactions between the chimeric cell-wall-associated transporter and TLR4.ConclusionThis study proposed 16 shortlisted proteins as promising immunogenic targets. Two novel platforms (e.g. cell-wall-associated transporter and bacteriophage T7 tail proteins) for designing of multi-epitope vaccines (MEVs), showed the better performance compared to FliC. In our future studies, these two MEVs will receive more scrutiny to evaluate their immunoreactivity.

Project description:The swift emergence of antibiotic resistance (AR) in bacterial pathogens to make themselves adaptable to changing environments has become an alarming health issue. To prevent AR infection, many ways can be accomplished such as by decreasing the misuse of antibiotics in human and animal medicine. Among these AR bacterial species, Plesiomonas shigelloides is one of the etiological agents of intestinal infection in humans. It is a gram-negative rod-shaped bacterium that is highly resistant to several classes of antibiotics, and no licensed vaccine against the aforementioned pathogen is available. Hence, substantial efforts are required to screen protective antigens from the pathogen whole genome that can be subjected easily to experimental evaluations. Here, we employed a reverse vaccinology (RV) approach to design a multi-antigenic epitopes based vaccine against P. shigelloides. The complete genomes of P. shigelloides were retrieved from the National Center for Biotechnological Information (NCBI) that on average consist of 5226 proteins. The complete proteomes were subjected to different subtractive proteomics filters, and in the results of that analysis, out of total proteins, 2399 were revealed as non-redundant and 2827 as redundant proteins. The non-redundant proteins were further checked for subcellular localization analysis, in which three were localized in the extracellular matrix, eight were outer membrane, and 13 were found in the periplasmic membrane. All surface localized proteins were found to be virulent. Out of a total of 24 virulent proteins, three proteins (flagellar hook protein (FlgE), hypothetical protein, and TonB-dependent hemoglobin/transferrin/lactoferrin family receptor protein) were considered as potential vaccine targets and subjected to epitopes prediction. The predicted epitopes were further examined for antigenicity, toxicity, and solubility. A total of 10 epitopes were selected (GFKESRAEF, VQVPTEAGQ, KINENGVVV, ENKALSQET, QGYASANDE, RLNPTDSRW, TLDYRLNPT, RVTKKQSDK, GEREGKNRP, RDKKTNQPL). The selected epitopes were linked with each other via specific GPGPG linkers in order to design a multi-epitopes vaccine construct, and linked with cholera toxin B subunit adjuvant to make the designed vaccine construct more efficient in terms of antigenicity. The 3D structure of the vaccine construct was modeled ab initio as no appropriate template was available. Furthermore, molecular docking was carried out to check the interaction affinity of the designed vaccine with major histocompatibility complex (MHC-)I (PDB ID: 1L1Y), MHC-II (1KG0), and toll-like receptor 4 ((TLR-4) (PDB: 4G8A). Molecular dynamic simulation was applied to evaluate the dynamic behavior of vaccine-receptor complexes. Lastly, the binding free energies of the vaccine with receptors were estimated by using MMPB/GBSA methods. All of the aforementioned analyses concluded that the designed vaccine molecule as a good candidate to be used in experimental studies to disclose its immune protective efficacy in animal models.

Dataset Information

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.

Publications

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.

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