O’Sullivan, C. P. et al. Group B streptococcal disease in UK and Irish infants younger than 90 days, 2014–15: a prospective surveillance study. Lancet. Infect. Dis 19(1), 83–90 (2019).
Raabe, V.N. and A.L. Shane, Group B streptococcus (Streptococcus agalactiae). Microbiology spectrum, 2019. 7(2): p. 7.2. 17.
Russell, N.J., et al., Maternal colonization with group B Streptococcus and serotype distribution worldwide: systematic review and meta-analyses. Clinical infectious diseases, 2017. 65(suppl_2): p. S100-S111.
Russell, N.J., et al., Risk of early-onset neonatal group B streptococcal disease with maternal colonization worldwide: systematic review and meta-analyses. Clinical infectious diseases, 2017. 65(suppl_2): p. S152-S159.
Pena, J. M. S., Lannes-Costa, P. S. & Nagao, P. E. Vaccines for Streptococcus agalactiae: current status and future perspectives. Front. Immunol. 15, 1430901 (2024).
Libster, R. et al. Long-term outcomes of group B streptococcal meningitis. Pediatrics 130(1), e8–e15 (2012).
Hughes, R., et al., on behalf of the Royal College of Obstetricians and Gynaecologists. Prevention of early-onset neonatal group B streptococcal disease. Green-top Guideline No. 36. BJOG, 2017. 124(12): p. e280-e305.
Cagno, C. K., Pettit, J. M. & Weiss, B. D. Prevention of perinatal group B streptococcal disease: updated CDC guideline. American family physician 86(1), 59–65 (2012).
Ledger, W.J. and M.J. Blaser, Are we using too many antibiotics during pregnancy? BJOG: An International Journal of Obstetrics & Gynaecology, 2013. 120(12): p. 1450–1452.
Cox, L. M. et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell 158(4), 705–721 (2014).
Leroux-Roels, G. et al. Safety and immunogenicity of a second dose of an investigational maternal trivalent group B Streptococcus vaccine in nonpregnant women 4–6 years after a first dose: results from a phase 2 trial. Clinical Infectious Diseases 70(12), 2570–2579 (2020).
Vekemans, J. et al. Maternal immunization against Group B streptococcus: World Health Organization research and development technological roadmap and preferred product characteristics. Vaccine 37(50), 7391–7393 (2019).
Seale, A.C., et al., Estimates of the burden of group B streptococcal disease worldwide for pregnant women, stillbirths, and children. Clinical infectious diseases, 2017. 65(suppl_2): p. S200-S219.
Tandrup Schmidt, S. et al. Liposome-based adjuvants for subunit vaccines: formulation strategies for subunit antigens and immunostimulators. Pharmaceutics 8(1), 7 (2016).
Trotter, C. L. et al. Vaccine value profile for Group B streptococcus. Vaccine 41, S41–S52 (2023).
Absalon, J. et al. Safety and immunogenicity of a novel hexavalent group B streptococcus conjugate vaccine in healthy, non-pregnant adults: a phase 1/2, randomised, placebo-controlled, observer-blinded, dose-escalation trial. The Lancet Infectious Diseases 21(2), 263–274 (2021).
Suschak, J. J., Williams, J. A. & Schmaljohn, C. S. Advancements in DNA vaccine vectors, non-mechanical delivery methods, and molecular adjuvants to increase immunogenicity. Human vaccines & immunotherapeutics 13(12), 2837–2848 (2017).
Li, W. et al. Peptide vaccine: progress and challenges. Vaccines 2(3), 515–536 (2014).
Fotin-Mleczek, M. et al. Messenger RNA-based vaccines with dual activity induce balanced TLR-7 dependent adaptive immune responses and provide antitumor activity. Journal of immunotherapy 34(1), 1–15 (2011).
Pascolo, S. Vaccination with messenger RNA. Methods Mol Med 127, 23–40 (2006).
Chetverin, A. B. Replicable and recombinogenic RNAs. FEBS letters 567(1), 35–41 (2004).
María, R. et al. The impact of bioinformatics on vaccine design and development. Vaccines 2, 3–6 (2017).
Martin, D. et al. Protection from group B streptococcal infection in neonatal mice by maternal immunization with recombinant Sip protein. Infection and immunity 70(9), 4897–4901 (2002).
Maeland, J. A. et al. Survey of immunological features of the alpha-like proteins of Streptococcus agalactiae. Clinical and Vaccine Immunology 22(2), 153–159 (2015).
Bevanger, L. & Néss, A. I. Mouse-protective antibodies against the Ibc proteins of group B streptococci. Acta Pathologica Microbiologica Scandinavica Series B: Microbiology 93(1–6), 121–124 (1985).
Fischer, P. et al. Safety and immunogenicity of a prototype recombinant alpha-like protein subunit vaccine (GBS-NN) against Group B Streptococcus in a randomised placebo-controlled double-blind phase 1 trial in healthy adult women. Vaccine 39(32), 4489–4499 (2021).
Kim, J.-S. et al. Mycobacterium tuberculosis RpfB drives Th1-type T cell immunity via a TLR4-dependent activation of dendritic cells. Journal of leukocyte biology 94(4), 733–749 (2013).
Gruber, A.R., et al., The Vienna RNA websuite. Nucleic Acids Res, 2008. 36(Web Server issue): p. W70–4.
Buchan, D.W., et al., Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic Acids Res, 2013. 41(Web Server issue): p. W349–57.
Heo, L., Park, H. & Seok, C. GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic acids research 41(W1), W384–W388 (2013).
Laskowski, R. A. & Swindells, M. B. LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 51(10), 2778–2786 (2011).
Miselli, F. et al. Transmission of Group B Streptococcus in late-onset neonatal disease: a narrative review of current evidence. Ther Adv Infect Dis 9, 20499361221142732 (2022).
Caro-Gomez, E. et al. Discovery of novel cross-protective Rickettsia prowazekii T-cell antigens using a combined reverse vaccinology and in vivo screening approach. Vaccine 32(39), 4968–4976 (2014).
Mehla, K. & Ramana, J. Identification of epitope-based peptide vaccine candidates against enterotoxigenic Escherichia coli: a comparative genomics and immunoinformatics approach. Molecular BioSystems 12(3), 890–901 (2016).
Mahapatra, S. R. et al. Designing a next-generation multiepitope-based vaccine against Staphylococcus aureus using reverse vaccinology approaches. Pathogens 12(3), 376 (2023).
Shinde, K. et al. The mRNA vaccine heralds a new era in vaccinology. Asian Journal of Pharmacy and Technology 12(3), 257–265 (2022).
Tsui, N. B., Ng, E. K. & Lo, Y. D. Stability of endogenous and added RNA in blood specimens, serum, and plasma. Clinical chemistry 48(10), 1647–1653 (2002).
Chen, N. et al. RNA sensors of the innate immune system and their detection of pathogens. IUBMB life 69(5), 297–304 (2017).
Pardi, N. et al. mRNA vaccines—a new era in vaccinology. Nature reviews Drug discovery 17(4), 261–279 (2018).
Gandhi, R. T. et al. Immunization of HIV-1-Infected Persons With Autologous Dendritic Cells Transfected With mRNA Encoding HIV-1 Gag and Nef: Results of a Randomized, Placebo-Controlled Clinical Trial. J Acquir Immune Defic Syndr 71(3), 246–253 (2016).
Richner, J. M. et al. Modified mRNA Vaccines Protect against Zika Virus Infection. Cell 168(6), 1114-1125.e10 (2017).
Bahl, K. et al. Preclinical and Clinical Demonstration of Immunogenicity by mRNA Vaccines against H10N8 and H7N9 Influenza Viruses. Mol Ther 25(6), 1316–1327 (2017).
Alberer, M. et al. Safety and immunogenicity of a mRNA rabies vaccine in healthy adults: an open-label, non-randomised, prospective, first-in-human phase 1 clinical trial. Lancet 390(10101), 1511–1520 (2017).
Dobrut, A. & Brzychczy-Włoch, M. Immunogenic Proteins of Group B Streptococcus—Potential Antigens in Immunodiagnostic Assay for GBS Detection. Pathogens 11(1), 43 (2022).
Carboni, F., et al., Proof of concept for a single-dose Group B Streptococcus vaccine based on capsular polysaccharide conjugated to Qβ virus-like particles. npj Vaccines, 2023. 8(1): p. 152.
Paul, P. et al. 20 million pregnant women with group B streptococcus carriage: consequences, challenges, and opportunities for prevention. Curr Opin Pediatr 35(2), 223–230 (2023).
Sanchez-Trincado, J. L., Gomez-Perosanz, M. & Reche, P. A. Fundamentals and methods for T-and B-cell epitope prediction. Journal of immunology research 2017(1), 2680160 (2017).
Foster, T. J. et al. Adhesion, invasion and evasion: the many functions of the surface proteins of Staphylococcus aureus. Nature reviews microbiology 12(1), 49–62 (2014).
Mohammadi, S. et al. Designing of a Novel Candidate Multi-epitope Vaccine to boost Immune Responses against SARS128; COV128; 2 using Immunoinformatics and Machine Learning based Approach. Letters in Drug Design & Discovery 21(2), 356–375 (2024).
Batista, F. D., Iber, D. & Neuberger, M. S. B cells acquire antigen from target cells after synapse formation. Nature 411(6836), 489–494 (2001).
Fleri, W. et al. The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. Frontiers in immunology 8, 278 (2017).
Saha, S. and G.P.S. Raghava, Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins: Structure, Function, and Bioinformatics, 2006. 65(1): p. 40–48.
Carmona, J. et al. Mycobacterium tuberculosis strains are differentially recognized by TLRs with an impact on the immune response. PloS one 8(6), e67277 (2013).
Sarkar, B. et al. Immunoinformatics-guided designing of epitope-based subunit vaccines against the SARS Coronavirus-2 (SARS-CoV-2). Immunobiology 225(3), 151955 (2020).
Livingston, B. et al. A rational strategy to design multiepitope immunogens based on multiple Th lymphocyte epitopes. The Journal of Immunology 168(11), 5499–5506 (2002).
Bhatnager, R. et al. Epitope based peptide vaccine against SARS-COV2: an immune-informatics approach. Journal of Biomolecular Structure and Dynamics 39(15), 5690–5705 (2021).
Dey, J. et al. Designing of multi-epitope peptide vaccine against Acinetobacter baumannii through combined immunoinformatics and protein interaction-based approaches. Immunol Res 71(4), 639–662 (2023).
Li, X. et al. Design and evaluation of a multi-epitope peptide of human metapneumovirus. Intervirology 58(6), 403–412 (2016).
Grudzien-Nogalska, E. et al. Phosphorothioate cap analogs stabilize mRNA and increase translational efficiency in mammalian cells. Rna 13(10), 1745–1755 (2007).
Liu, Q. Comparative analysis of base biases around the stop codons in six eukaryotes. Biosystems 81(3), 281–289 (2005).
Bernstein, P., Peltz, S. & Ross, J. The poly (A)-poly (A)-binding protein complex is a major determinant of mRNA stability in vitro. Molecular and cellular biology 9(2), 659–670 (1989).
Kozak, M. Point mutations define a sequence flanking the AUG initiator codon that modulates translation by eukaryotic ribosomes. Cell 44(2), 283–292 (1986).
Kou, Y. et al. Tissue plasminogen activator (tPA) signal sequence enhances immunogenicity of MVA-based vaccine against tuberculosis. Immunology letters 190, 51–57 (2017).
Gergen, J. & Petsch, B. mRNA-Based Vaccines and Mode of Action. Current topics in microbiology and immunology 440, 1–30 (2022).
VerPlank, J. J. S. & Goldberg, A. L. Regulating protein breakdown through proteasome phosphorylation. Biochem J 474(19), 3355–3371 (2017).
Corradin, G., V. Villard, and A.V. Kajava, Protein structure based strategies for antigen discovery and vaccine development against malaria and other pathogens. Endocrine, Metabolic & Immune Disorders-Drug Targets (Formerly Current Drug Targets-Immune, Endocrine & Metabolic Disorders), 2007. 7(4): p. 259–265.
Thess, A. et al. Sequence-engineered mRNA without chemical nucleoside modifications enables an effective protein therapy in large animals. Molecular Therapy 23(9), 1456–1464 (2015).
Krieg, P. A. & Melton, D. In vitro RNA synthesis with SP6 RNA polymerase. In Methods in enzymology 397–415 (Elsevier, 1987).
Karikó, K. et al. Generating the optimal mRNA for therapy: HPLC purification eliminates immune activation and improves translation of nucleoside-modified, protein-encoding mRNA. Nucleic acids research 39(21), e142–e142 (2011).
Mahapatra, S. R. et al. Immunoinformatics-guided designing of epitope-based subunit vaccine from Pilus assembly protein of Acinetobacter baumannii bacteria. Journal of Immunological Methods 508, 113325 (2022).
Choi, H. G. et al. Mycobacterium tuberculosis RpfE promotes simultaneous Th1-and Th17-type T-cell immunity via TLR4-dependent maturation of dendritic cells. European journal of immunology 45(7), 1957–1971 (2015).
Nielsen, M. et al. MHC class II epitope predictive algorithms. Immunology 130(3), 319–328 (2010).
Kim, Y. et al. Immune epitope database analysis resource. Nucleic acids research 40(W1), W525–W530 (2012).
Dey, J. et al. Designing a novel multi-epitope vaccine to evoke a robust immune response against pathogenic multidrug-resistant Enterococcus faecium bacterium. Gut Pathogens 14(1), 21 (2022).
Lundegaard, C., Lund, O. & Nielsen, M. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics 24(11), 1397–1398 (2008).
Nielsen, M. et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Science 12(5), 1007–1017 (2003).
Naveed, M., et al., A Vaccine Construction against COVID-19-Associated Mucormycosis Contrived with Immunoinformatics-Based Scavenging of Potential Mucoralean Epitopes. Vaccines (Basel), 2022. 10(5).
Dar, M. A. et al. Designing of Peptide Based Multi-Epitope Vaccine Construct against Gallbladder Cancer Using Immunoinformatics and Computational Approaches. Vaccines 10(11), 1850 (2022).
Johnson, M., et al., NCBI BLAST: a better web interface. Nucleic acids research, 2008. 36(suppl_2): p. W5-W9.
Can, H. et al. In silico discovery of antigenic proteins and epitopes of SARS-CoV-2 for the development of a vaccine or a diagnostic approach for COVID-19. Scientific reports 10(1), 22387 (2020).
Doytchinova, I. A. & Flower, D. R. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics 8(1), 1–7 (2007).
Dey, J. et al. Exploring Klebsiella pneumoniae capsule polysaccharide proteins to design multiepitope subunit vaccine to fight against pneumonia. Expert Rev Vaccines 21(4), 569–587 (2022).
Calis, J. J. et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol 9(10), e1003266 (2013).
Dimitrov, I., et al., AllerTOP v. 2—a server for in silico prediction of allergens. Journal of molecular modeling, 2014. 20(6): p. 1–6.
Gupta, S. et al. In silico approach for predicting toxicity of peptides and proteins. PloS one 8(9), e73957 (2013).
Yuan, S., Chan, H. S. & Hu, Z. Using PyMOL as a platform for computational drug design. Wiley Interdisciplinary Reviews: Computational Molecular Science 7(2), e1298 (2017).
Lamiable, A., Thevenet, P. & Tufféry, P. A critical assessment of hidden markov model sub-optimal sampling strategies applied to the generation of peptide 3D models. J Comput Chem 37(21), 2006–2016 (2016).
Lee, H. et al. GalaxyPepDock: a protein–peptide docking tool based on interaction similarity and energy optimization. Nucleic Acids Research 43(W1), W431–W435 (2015).
Weng, G. et al. HawkDock: a web server to predict and analyze the protein–protein complex based on computational docking and MM/GBSA. Nucleic acids research 47(W1), W322–W330 (2019).
Saleem, A., et al., HPLC, FTIR and GC-MS Analyses of Thymus vulgaris Phytochemicals Executing In Vitro and In Vivo Biological Activities and Effects on COX-1, COX-2 and Gastric Cancer Genes Computationally. Molecules, 2022. 27(23).
Bui, H.-H. et al. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC bioinformatics 7(1), 1–5 (2006).
Sharma, R. et al. An immunoinformatics approach to design a multi-epitope vaccine against Mycobacterium tuberculosis exploiting secreted exosome proteins. Scientific Reports 11(1), 13836 (2021).
Kim, S.C., et al., Modifications of mRNA vaccine structural elements for improving mRNA stability and translation efficiency. Molecular & cellular toxicology, 2022: p. 1–8.
Kreiter, S. et al. Increased antigen presentation efficiency by coupling antigens to MHC class I trafficking signals. J Immunol 180(1), 309–318 (2008).
Tcherepanova, I. Y. et al. Ectopic expression of a truncated CD40L protein from synthetic post-transcriptionally capped RNA in dendritic cells induces high levels of IL-12 secretion. BMC Molecular Biology 9, 1–13 (2008).
Guo, H. et al. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466(7308), 835–840 (2010).
Wong, N. & Wang, X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic acids research 43(D1), D146–D152 (2015).
Magnan, C. N. et al. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics 26(23), 2936–2943 (2010).
Wilkins, M., et al., Protein Identification and Analysis Tools in the ExPASy Server. 2008. p. 531–552.
Eisenhaber, B., Bork, P. & Eisenhaber, F. Prediction of potential GPI-modification sites in proprotein sequences. Journal of molecular biology 292(3), 741–758 (1999).
Maurer-Stroh, S., Eisenhaber, B. & Eisenhaber, F. N-terminal N-myristoylation of proteins: refinement of the sequence motif and its taxon-specific differences. Journal of molecular biology 317(4), 523–540 (2002).
Kim, D.E., D. Chivian, and D. Baker, Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res, 2004. 32(Web Server issue): p. W526–31.
Colovos, C. & Yeates, T. O. Verification of protein structures: patterns of nonbonded atomic interactions. Protein science 2(9), 1511–1519 (1993).
Wiederstein, M. and M.J. Sippl, ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic acids research, 2007. 35(suppl_2): p. W407-W410.
Eisenberg, D., Lüthy, R. & Bowie, J. U. VERIFY3D: assessment of protein models with three-dimensional profiles. In Methods in enzymology 396–404 (Elsevier, 1997).
Laskowski, R. A. et al. PROCHECK: a program to check the stereochemical quality of protein structures. Journal of applied crystallography 26(2), 283–291 (1993).
Ponomarenko, J. et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC bioinformatics 9, 1–8 (2008).
Solanki, V. & Tiwari, V. Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii. Scientific reports 8(1), 9044 (2018).
Yan, Y. et al. The HDOCK server for integrated protein–protein docking. Nature protocols 15(5), 1829–1852 (2020).
Xue, L. C. et al. PRODIGY: a web server for predicting the binding affinity of protein–protein complexes. Bioinformatics 32(23), 3676–3678 (2016).
Laskowski, R. A. et al. PDBsum: Structural summaries of PDB entries. Protein Sci 27(1), 129–134 (2018).
Páll, S., et al., Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS. The Journal of Chemical Physics, 2020. 153(13).
Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. Journal of molecular graphics 14(1), 33–38 (1996).
Rapin, N., Lund, O. & Castiglione, F. Immune system simulation online. Bioinformatics 27(14), 2013–2014 (2011).
Castiglione, F. et al. How the interval between prime and boost injection affects the immune response in a computational model of the immune system. Computational and mathematical methods in medicine 2012(1), 842329 (2012).
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