Vaccine Design by Reverse Vaccinology and Machine Learning.
Antigen
Machine learning
Reverse vaccinology
Vaccine
Vaxign
Vaxign-ML
Vaxitop
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
16
11
2021
pubmed:
17
11
2021
medline:
30
11
2021
Statut:
ppublish
Résumé
Reverse vaccinology (RV) is the state-of-the-art vaccine development strategy that starts with predicting vaccine antigens by bioinformatics analysis of the whole genome of a pathogen of interest. Vaxign is the first web-based RV vaccine prediction method based on calculating and filtering different criteria of proteins. Vaxign-ML is a new Vaxign machine learning (ML) method that predicts vaccine antigens based on extreme gradient boosting with the advance of new technologies and cumulation of protective antigen data. Using a benchmark dataset, Vaxign-ML showed superior performance in comparison to existing open-source RV tools. Vaxign-ML is also implemented within the web-based Vaxign platform to support easy and intuitive access. Vaxign-ML is also available as a command-based software package for more advanced and customizable vaccine antigen prediction. Both Vaxign and Vaxign-ML have been applied to predict SARS-CoV-2 (cause of COVID-19) and Brucella vaccine antigens to demonstrate the integrative approach to analyze and select vaccine candidates using the Vaxign platform.
Identifiants
pubmed: 34784028
doi: 10.1007/978-1-0716-1900-1_1
doi:
Substances chimiques
Brucella Vaccine
0
COVID-19 Vaccines
0
Vaccines
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-16Subventions
Organisme : NIAID NIH HHS
ID : R01 AI081062
Pays : United States
Organisme : NIAID NIH HHS
ID : UH2 AI132931
Pays : United States
Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Pizza M, Scarlato V, Masignani V, Giuliani MM, Aricò B, Comanducci M, Jennings GT, Baldi L, Bartolini E, Capecchi B, Galeotti CL, Luzzi E, Manetti R, Marchetti E, Mora M, Nuti S, Ratti G, Santini L, Savino S, Scarselli M, Storni E, Zuo P, Broeker M, Hundt E, Knapp B, Blair E, Mason T, Tettelin H, Hood DW, Jeffries AC, Saunders NJ, Granoff DM, Venter JC, Moxon ER, Grandi G, Rappuoli R (2000) Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing. Science 287:1816–1820. https://doi.org/10.1126/science.287.5459.1816
doi: 10.1126/science.287.5459.1816
pubmed: 10710308
Vernikos G, Medini D (2014) Bexsero H chronicle. Pathog Glob Health 108:305–311. https://doi.org/10.1179/2047773214Y.0000000162
doi: 10.1179/2047773214Y.0000000162
pubmed: 25417906
pmcid: 4241781
Folaranmi T, Rubin L, Martin SW, Patel M, MacNeil JR (2015) Use of serogroup B meningococcal vaccines in persons aged≥ 10 years at increased risk for serogroup B meningococcal disease: recommendations of the advisory committee on immunization practices, 2015. MMWR Morb Mortal Wkly Rep 64:608–612
pubmed: 26068564
pmcid: 4584923
Ong E, Wong MU, He Y (2017) Identification of new features from known bacterial protective vaccine antigens enhances rational vaccine design. Front Immunol 8:1–11. https://doi.org/10.3389/fimmu.2017.01382
doi: 10.3389/fimmu.2017.01382
Vivona S, Bernante F, Filippini F (2006) NERVE: new enhanced reverse vaccinology environment. BMC Biotechnol 6:35. https://doi.org/10.1186/1472-6750-6-35
doi: 10.1186/1472-6750-6-35
pubmed: 16848907
pmcid: 1570458
He Y, Xiang Z, Mobley HLT (2010) Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development. J Biomed Biotechnol 2010:1–15. https://doi.org/10.1155/2010/297505
doi: 10.1155/2010/297505
Rizwan M, Naz A, Ahmad J, Naz K, Obaid A, Parveen T, Ahsan M, Ali A (2017) VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology. BMC Bioinformatics 18:1–7. https://doi.org/10.1186/s12859-017-1540-0
doi: 10.1186/s12859-017-1540-0
Doytchinova IA, Flower DR (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8:4. https://doi.org/10.1186/1471-2105-8-4
doi: 10.1186/1471-2105-8-4
pubmed: 17207271
pmcid: 1780059
Bowman BN, McAdam PR, Vivona S, Zhang JX, Luong T, Belew RK, Sahota H, Guiney D, Valafar F, Fierer J, Woelk CH (2011) Improving reverse vaccinology with a machine learning approach. Vaccine 29:8156–8164. https://doi.org/10.1016/j.vaccine.2011.07.142
doi: 10.1016/j.vaccine.2011.07.142
pubmed: 21864619
Heinson AI, Gunawardana Y, Moesker B, Denman Hume CC, Vataga E, Hall Y, Stylianou E, McShane H, Williams A, Niranjan M, Woelk CH (2017) Enhancing the biological relevance of machine learning classifiers for reverse vaccinology. Int J Mol Sci 18:312. https://doi.org/10.3390/ijms18020312
doi: 10.3390/ijms18020312
pmcid: 5343848
Dalsass M, Brozzi A, Medini D, Rappuoli R (2019) Comparison of open-source reverse vaccinology programs for bacterial vaccine antigen discovery. Front Immunol 10:1–12. https://doi.org/10.3389/fimmu.2019.00113
doi: 10.3389/fimmu.2019.00113
Ong E, Wang H, Wong MU, Seetharaman M, Valdez N, He Y (2020) Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens. Bioinformatics 36:3185–3191. https://doi.org/10.1093/bioinformatics/btaa119
doi: 10.1093/bioinformatics/btaa119
pubmed: 32096826
pmcid: 7214037
Yang B, Sayers S, Xiang Z, He Y (2011) Protegen: a web-based protective antigen database and analysis system. Nucleic Acids Res 39:1073–1078. https://doi.org/10.1093/nar/gkq944
doi: 10.1093/nar/gkq944
Ong E, Wong MU, Huffman A, He Y (2020) COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Front Immunol 11:1581
doi: 10.3389/fimmu.2020.01581
Shin D, Mukherjee R, Grewe D, Bojkova D, Baek K, Bhattacharya A, Schulz L, Widera M, Mehdipour AR, Tascher G, Geurink PP, Wilhelm A, van der Heden van Noort GJ, Ovaa H, Müller S, Knobeloch K-P, Rajalingam K, Schulman BA, Cinatl J, Hummer G, Ciesek S, Dikic I (2020) Papain-like protease regulates SARS-CoV-2 viral spread and innate immunity. Nature 587(7835):657–662. https://doi.org/10.1038/s41586-020-2601-5
doi: 10.1038/s41586-020-2601-5
pubmed: 32726803
pmcid: 7116779
Xiang Z, Zheng W, He Y (2006) BBP: Brucella genome annotation with literature mining and curation. BMC Bioinformatics 7:347. https://doi.org/10.1186/1471-2105-7-347
doi: 10.1186/1471-2105-7-347
pubmed: 16842628
pmcid: 1539029
Bui HH, Sidney J, Dinh K, Southwood S, Newman MJ, Sette A (2006) Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics 7:1–5. https://doi.org/10.1186/1471-2105-7-153
doi: 10.1186/1471-2105-7-153
Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, Mende DR, Letunic I, Rattei T, Jensen LJ, Von Mering C, Bork P (2019) EggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 47:D309–D314. https://doi.org/10.1093/nar/gky1085
doi: 10.1093/nar/gky1085
pubmed: 30418610
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, Von Mering C, Bork P (2017) Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol 34:2115–2122. https://doi.org/10.1093/molbev/msx148
doi: 10.1093/molbev/msx148
pubmed: 28460117
pmcid: 5850834