Prospective scientific and technological analysis on the use of bioinformatics for the design of peptide vaccines
DOI:
https://doi.org/10.33448/rsd-v12i3.40287Keywords:
Peptide vaccine; Bioinformatics; Design vaccine.Abstract
Bacteria caused by bacteria have had several negative impacts on health and the economy. Due to its potential for transmissibility, it has aroused great interest from scientists, since most of these microorganisms are resistant to antibiotics and do not have treatments and effective prophylaxis. Therefore, science has been adding information technology to analyze important data in order to obtain information, and thus be able to carry out the vaccine design against these pathogens. The objective of this study was to search in the literature and in inventions, files that were related to peptide vaccines developed from the use of bioinformatics. Peptide vaccine, bioinformatics and vaccine design were the keywords used to search for articles and patents in the following databases: PubMed, INPI and WIPO. The survey of common data found a sample of 259 scientific articles and 31 patents existing in the last 11 years in the WIPO, beyond 31 patents on the INPI. The definition of scientific-technological prospections is extremely important to provide a greater acquisition of knowledge on the subject addressed and allow the scientist to better direct the study.
References
Abdelmageed, M. I., Abdelmoneim, A. H., Mustafa, M. I., Elfadol, N. M., Murshed, N. S., Shantier, S. W., & Makhawi, A. M. (2020a). Design of a Multiepitope-Based Peptide Vaccine against the e Protein of Human COVID-19: An Immunoinformatics Approach. BioMed Research International, 2020. https://doi.org/10.1155/2020/2683286
Abdelmageed, M. I., Abdelmoneim, A. H., Mustafa, M. I., Elfadol, N. M., Murshed, N. S., Shantier, S. W., & Makhawi, A. M. (2020b). Design of a Multiepitope-Based Peptide Vaccine against the e Protein of Human COVID-19: An Immunoinformatics Approach. BioMed Research International, 2020. https://doi.org/10.1155/2020/2683286
Bulley, A., & Irish, M. (2018). The functions of prospection - Variations in health and disease. Frontiers in Psychology, 9(NOV), 2328. https://doi.org/10.3389/FPSYG.2018.02328/BIBTEX
de Almeida Borges, P., de Araújo, L. P., Lima, L. A., Ghesti, G. F., & Souza Carmo, T. (2020). The triple helix model and intellectual property: The case of the University of Brasilia. World Patent Information, 60, 101945. https://doi.org/10.1016/J.WPI.2019.101945
Dey, S., De, A., & Nandy, A. (2016). Rational design of peptide vaccines against multiple types of human papillomavirus. Cancer Informatics, 15, 1–16. https://doi.org/10.4137/CIN.S39071/ASSET/IMAGES/LARGE/10.4137_CIN.S39071-FIG2.JPEG
Cross, D., Thomson, S., & Sinclair, Alexandra. (2017). Research in Brazil A report for CAPES by Clarivate Analytics.
Droppa-Almeida, D., Franceschi, E., & Padilha, F. F. (2018). Immune-informatic analysis and design of peptide vaccine from multi-epitopes against Corynebacterium pseudotuberculosis. Bioinformatics and Biology Insights, 12. https://doi.org/10.1177/1177932218755337/ASSET/IMAGES/LARGE/10.1177_1177932218755337-FIG2.JPEG
Ejigu, G. F., & Jung, J. (2020). Review on the Computational Genome Annotation of Sequences Obtained by Next-Generation Sequencing. Biology, 9(9), 1–27. https://doi.org/10.3390/BIOLOGY9090295
Fasi, M. A. (2022). An Overview on patenting trends and technology commercialization practices in the university Technology Transfer Offices in USA and China. World Patent Information, 68, 102097. https://doi.org/10.1016/J.WPI.2022.102097
Fonseca, E. M. da, Shadlen, K. C., & Bastos, F. I. (2021). The politics of COVID-19 vaccination in middle-income countries: Lessons from Brazil. Social Science & Medicine, 281, 114093. https://doi.org/10.1016/J.SOCSCIMED.2021.114093
Kalita, P., Padhi, A. K., Zhang, K. Y. J., & Tripathi, T. (2020). Design of a peptide-based subunit vaccine against novel coronavirus SARS-CoV-2. Microbial Pathogenesis, 145, 104236. https://doi.org/10.1016/J.MICPATH.2020.104236
Lima, C. C., Benjamim, S. C. C., & Santos, R. F. S. dos. (2017). Mecanismo de resistência bacteriana frente aos fármacos: uma revisão. CuidArte, Enferm, 105–113. http://www.webfipa.net/facfipa/ner/sumarios/cuidarte/2017v1/15%20Artigo_Mecanismo%20resist%C3%AAncia%20bacteriana%20a%20antibi%C3%B3ticos_27-07-17.pdf
Lohia, N., & Baranwal, M. (2020). An immunoinformatics approach in design of synthetic peptide vaccine against influenza virus. Methods in Molecular Biology, 2131, 229–243. https://doi.org/10.1007/978-1-0716-0389-5_11/COVER
Maxwell, I. A., & Maxwell, N. J. L. (2022). A review of Chinese-owned Australian patents. World Patent Information, 71, 102151. https://doi.org/10.1016/J.WPI.2022.102151
Montecchi, T., Russo, D., & Liu, Y. (2013a). Searching in Cooperative Patent Classification: Comparison between keyword and concept-based search. Advanced Engineering Informatics, 27(3), 335–345. https://doi.org/10.1016/J.AEI.2013.02.002
Montecchi, T., Russo, D., & Liu, Y. (2013b). Searching in Cooperative Patent Classification: Comparison between keyword and concept-based search. Advanced Engineering Informatics, 27(3), 335–345. https://doi.org/10.1016/J.AEI.2013.02.002
Mustafa, M. I., Shantier, S. W., Abdelmageed, M. I., & Makhawi, A. M. (2021). Epitope-based peptide vaccine against Bombali Ebolavirus viral protein 40: An immunoinformatics combined with molecular docking studies. Informatics in Medicine Unlocked, 25, 100694. https://doi.org/10.1016/J.IMU.2021.100694
Pereira, R., Oliveira, J., & Sousa, M. (2020). Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics. Journal of Clinical Medicine 2020, Vol. 9, Page 132, 9(1), 132. https://doi.org/10.3390/JCM9010132
Ray, M., Sable, M. N., Sarkar, S., & Hallur, V. (2021). Essential interpretations of bioinformatics in COVID-19 pandemic. Meta Gene, 27, 100844. https://doi.org/10.1016/J.MGENE.2020.100844
Rother, E. T. (2007). Revisão Sistemática x Revisão Narrativa. Acta Paul Enferm ; Vol. 20. https://doi.org/10.1590/S0103-21002007000200001
Safavi, A., Kefayat, A., Abiri, A., Mahdevar, E., Behnia, A. H., & Ghahremani, F. (2019). In silico analysis of transmembrane protein 31 (TMEM31) antigen to design novel multiepitope peptide and DNA cancer vaccines against melanoma. Molecular Immunology, 112, 93–102. https://doi.org/10.1016/J.MOLIMM.2019.04.030
Sánchez-Burgos, G. G., Montalvo-Marin, N. M., Díaz-Rosado, E. R., & Pérez-Rueda, E. (2021). In silico identification of chikungunya virus b-and t-cell epitopes with high antigenic potential for vaccine development. Viruses, 13(12), 2360. https://doi.org/10.3390/V13122360/S1
Sardar, R., Satish, D., Birla, S., & Gupta, D. (2020). Integrative analyses of SARS-CoV-2 genomes from different geographical locations reveal unique features potentially consequential to host-virus interaction, pathogenesis and clues for novel therapies. Heliyon, 6(9), e04658. https://doi.org/10.1016/J.HELIYON.2020.E04658
Shahid, F., Ashfaq, U. A., Javaid, A., & Khalid, H. (2020). Immunoinformatics guided rational design of a next generation multi epitope based peptide (MEBP) vaccine by exploring Zika virus proteome. Infection, Genetics and Evolution, 80, 104199. https://doi.org/10.1016/J.MEEGID.2020.104199
Sharma, A., Virmani, T., Pathak, V., Sharma, A., Pathak, K., Kumar, G., & Pathak, D. (2022). Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine. BioMed Research International, 2022. https://doi.org/10.1155/2022/7205241
Soudry, D., Hoffer, E., Nacson, M. S., & Srebro, N. (2018). The Implicit Bias of Gradient Descent on Separable Data. Journal of Machine Learning Research, 19, 1–57. http://jmlr.org/papers/v19/18-188.html.
Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. Engineering, 5(4), 653–661. https://doi.org/10.1016/J.ENG.2019.01.014
Torrisi, M., Pollastri, G., & Le, Q. (2020). Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal, 18, 1301–1310. https://doi.org/10.1016/J.CSBJ.2019.12.011
Wenham, C., Smith, J., & Morgan, R. (2020). COVID-19: the gendered impacts of the outbreak. The Lancet, 395(10227), 846–848. https://doi.org/10.1016/S0140-6736(20)30526-2
Wu, Q., Zaid, M., Xuan, Z., Wang, C., Gu, H., Shi, M., Zhu, J., Hu, Y., & Liu, J. (2021). Changes in epidemiological features of vaccine preventable infectious diseases among three eras of national vaccination strategies from 1953 to 2018 in Shanghai, China. The Lancet Regional Health - Western Pacific, 7, 100092. https://doi.org/10.1016/J.LANWPC.2021.100092
Wu, R., Wang, H., Lv, X., Shen, X., & Ye, G. (2020). Rapid action of mechanism investigation of Yixin Ningshen tablet in treating depression by combinatorial use of systems biology and bioinformatics tools. Journal of Ethnopharmacology, 257, 112827. https://doi.org/10.1016/J.JEP.2020.112827
Yazdani, Z., Rafiei, A., Yazdani, M., & Valadan, R. (2020). Design an Efficient Multi-Epitope Peptide Vaccine Candidate Against SARS-CoV-2: An in silico Analysis. Infection and Drug Resistance, 13, 3007. https://doi.org/10.2147/IDR.S264573
Zaini, W. M. F., Lai, D. T. C., & Lim, R. C. (2022). Identifying patent classification codes associated with specific search keywords using machine learning. World Patent Information, 71, 102153. https://doi.org/10.1016/J.WPI.2022.102153
Zerihun, M. B., Pucci, F., Peter, E. K., & Schug, A. (2020). pydca v1.0: a comprehensive software for direct coupling analysis of RNA and protein sequences. Bioinformatics, 36(7), 2264–2265. https://doi.org/10.1093/BIOINFORMATICS/BTZ892
Zhang, D., Yang, Y., Li, M., Lu, Y., Liu, Y., Jiang, J., Liu, R., Liu, J., Huang, X., Li, G., & Qu, J. (2022). Ecological Barrier Deterioration Driven by Human Activities Poses Fatal Threats to Public Health due to Emerging Infectious Diseases. Engineering, 10, 155–166. https://doi.org/10.1016/J.ENG.2020.11.002
Zhang, X. M., Liang, L., Liu, L., & Tang, M. J. (2021). Graph Neural Networks and Their Current Applications in Bioinformatics. Frontiers in Genetics, 12. https://doi.org/10.3389/FGENE.2021.690049
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Copyright (c) 2023 Luiz Natanael Monteiro Feitosa; Ítalo Samuel Gonçalves Rodrigues; Tatiane Batista dos Santos; Maria Patrícia de Jesus Santos; Rayssa do Couto Santos; Tatiane de Oliveira Xavier Machado; Daniela Droppa-Almeida
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