References
-
Brown, J. D. et al. Shigella species epidemiology and antimicrobial susceptibility: the implications of emerging Azithromycin resistance for guiding treatment, guidelines and breakpoints. J. Antimicrob. Chemother. 72, 3181–3186 (2017).
-
Hmar, E. B. L., Paul, S. & Sharma, H. K. The role of Shigella spp. In propagating bacillary dysentery In humans and the prominence of nanotechnology In disease prevention. Futur J. Pharm. Sci. 10, 97 (2024).
-
Ranjbar, R., Farahani, A. & Shigella Antibiotic-Resistance Mechanisms And New Horizons For Treatment. IDR Volume 12, 3137–3167 (2019).
-
Afrad, M. H. et al. Antibiotic resistance and serotype distribution of Shigella strains in Bangladesh over the period of 2014–2022: evidence from a nationwide hospital-based surveillance for cholera and other diarrheal diseases. Microbiol. Spectr. 12, e00739–e00724 (2024).
-
Baharvand, A. et al. The increasing antimicrobial resistance of Shigella species among Iranian pediatrics: a systematic review and meta-analysis. Pathogens Global Health. 117, 611–622 (2023).
-
Martinez-Becerra, F. J. et al. Broadly protective Shigella vaccine based on type III secretion apparatus proteins. Infect. Immun. 80, 1222–1231 (2012).
-
Schroeder, G. N. & Hilbi, H. Molecular pathogenesis of Shigella spp.: controlling host cell Signaling, Invasion, and death by type III secretion. Clin. Microbiol. Rev. 21, 134–156 (2008).
-
Yang, S. C., Hung, C. F., Aljuffali, I. A. & Fang, J. Y. The roles of the virulence factor IpaB In Shigella spp. In the escape from immune cells and Invasion of epithelial cells. Microbiol. Res. 181, 43–51 (2015).
-
Sandlin, R. C. et al. Avirulence of rough mutants of Shigella flexneri: requirement of O antigen for correct unipolar localization of IcsA in the bacterial outer membrane. Infect. Immun. 63, 229–237 (1995).
-
Mattock, E. & Blocker, A. J. How do the virulence factors of Shigella work together to cause disease? Front Cell. Infect. Microbiol 7, (2017).
-
Qasim, M., Wrage, M., Nüse, B. & Mattner, J. Shigella outer membrane vesicles as promising targets for vaccination. IJMS 23, 994 (2022).
-
Lu, T., Das, S., Howlader, D. R., Picking, W. D. & Picking, W. L. Shigella vaccines: the continuing unmet challenge. IJMS 25, 4329 (2024).
-
Dingding, H. et al. Subtractive proteomics and reverse-vaccinology approaches for novel drug targets and designing a chimeric vaccine against Ruminococcus gnavus strain RJX1120. Front. Immunol. 16, 1555741 (2025).
-
Ghaffar, S. A. et al. Designing of a multi-epitopes based vaccine against haemophilius parainfluenzae and its validation through integrated computational approaches. Front. Immunol. 15, 1380732 (2024).
-
Rahman, S. et al. Subtractive Proteomics and Reverse-Vaccinology Approaches for Novel Drug Target Identification and Chimeric Vaccine Development against Bartonella henselae Strain Houston-1. Bioengineering 11, 505 (2024).
-
Khan, A., Khanzada, M. H., Khan, K., Jalal, K. & Uddin, R. Integrating core subtractive proteomics and reverse vaccinology for multi-epitope vaccine design against rickettsia prowazekii endemic typhus. Immunol. Res. 72, 82–95 (2024).
-
Kar, T. et al. A candidate multi-epitope vaccine against SARS-CoV-2. Sci. Rep. 10, 10895 (2020).
-
Mamun, T. I. et al. Designing a multi-epitope vaccine candidate against human rhinovirus C utilizing immunoinformatics approach. Front. Immunol. 15, 1364129 (2025).
-
Ross, S. H. & Cantrell, D. A. Signaling and function of Interleukin-2 in T lymphocytes. Annu. Rev. Immunol. 36, 411–433 (2018).
-
Aiman, S. et al. Vaccinomics-aided next-generation novel multi-epitope-based vaccine engineering against multidrug resistant Shigella sonnei: immunoinformatics and chemoinformatics approaches. PLoS ONE. 18, e0289773 (2023).
-
Salam, M. A. et al. Antimicrobial resistance: A growing serious threat for global public health. Healthcare 11, 1946 (2023).
-
Basmenj, E. R. et al. Computational epitope-based vaccine design with bioinformatics approach; a review. Heliyon 11, e41714 (2025).
-
The UniProt Consortium. The universal protein resource (UniProt). Nucleic Acids Res. 36, D190–D195 (2007).
-
The UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015).
-
Wei, W., Ning, L. W., Ye, Y. N., Guo, F. B. & Geptop A gene essentiality prediction tool for sequenced bacterial genomes based on orthology and phylogeny. PLoS ONE. 8, e72343 (2013).
-
Wen, Q. F. et al. Geptop 2.0: an Updated, more Precise, and faster Geptop server for identification of prokaryotic essential genes. Front. Microbiol. 10, 1236 (2019).
-
McGinnis, S. & Madden, T. L. BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res. 32, W20–W25 (2004).
-
Pertsemlidis, A. & Fondon, J. W. Having a BLAST with bioinformatics (and avoiding BLASTphemy). Genome Biol 2, reviews1 (2001). (2002).
-
Gardy, J. L. PSORT-B: improving protein subcellular localization prediction for Gram-negative bacteria. Nucleic Acids Res. 31, 3613–3617 (2003).
-
Yu, N. Y. et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26, 1608–1615 (2010).
-
Grund, M. E., Soo, J., Cote, C. K., Berisio, R. & Lukomski, S. Thinking outside the bug: targeting outer membrane proteins for burkholderia vaccines. Cells 10, 495 (2021).
-
Zahid, A., Ismail, H., Wilson, J. C. & Grice, I. D. Bioengineering Outer-Membrane vesicles for vaccine development: Strategies, Advances, and perspectives. Vaccines 13, 767 (2025).
-
Tabibpour, N. S., Doosti, A. & Sharifzadeh, A. Putative novel outer membrane antigens multi-epitope DNA vaccine candidates identified by immunoinformatic approaches to control acinetobacter baumannii. BMC Immunol. 24, 46 (2023).
-
Doytchinova, I. A. & Flower, D. R. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 8, 4 (2007).
-
Ong, E. et al. Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning. Nucleic Acids Res. 49, W671–W678 (2021).
-
Bevacqua, A., Bakshi, S. & Xia, Y. Principal component analysis of alpha-helix deformations in transmembrane proteins. PLoS ONE. 16, e0257318 (2021).
-
Bianchi, F., Textor, J. & Van Den Bogaart, G. Transmembrane helices are an overlooked source of major histocompatibility complex class I epitopes. Front. Immunol. 8, 1118 (2017).
-
De Araújo, L. P., Silva, E. N., De Alencar, S. M. & Corsetti, P. P. De Almeida, L. A. Multivalent vaccine candidate from conserved Immunogenic peptides in entry or exit proteins of orthopoxvirus genus. Sci. Rep. 15, 12503 (2025).
-
Hewitt, E. W. The MHC class I antigen presentation pathway: strategies for viral immune evasion. Immunology 110, 163–169 (2003).
-
Wieczorek, M. et al. Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen presentation. Front Immunol 8, (2017).
-
Waqas, M. et al. Immunoinformatics design of multivalent epitope vaccine against Monkeypox virus and its variants using membrane-bound, enveloped, and extracellular proteins as targets. Front. Immunol. 14, 1091941 (2023).
-
Lundegaard, C., Lund, O., Buus, S. & Nielsen, M. Major histocompatibility complex class I binding predictions as a tool in epitope discovery. Immunology 130, 309–318 (2010).
-
Vilela Rodrigues, T. C. et al. An immunoinformatics-based designed multi-epitope candidate vaccine (mpme-VAC/STV-1) against Mycoplasma pneumoniae. Comput. Biol. Med. 142, 105194 (2022).
-
Dimitrov, I., Bangov, I., Flower, D. R. & Doytchinova, I. AllerTOP v.2—a server for in Silico prediction of allergens. J. Mol. Model. 20, 2278 (2014).
-
Rezaei, M., Habibi, M., Ehsani, P., Asadi Karam, M. R. & Bouzari, S. Design and computational analysis of an effective multi-epitope vaccine candidate using subunit B of cholera toxin as a build-in adjuvant against urinary tract infections. Bioimpacts 14, 27513 (2023).
-
Roohparvar Basmenj, E., Omidvar, B., Kiumarsy, A., Izadkhah, H. & Ghiabi, S. Design of a multi-epitope-based peptide vaccine against the SARS-CoV-2 Omicron variant using bioinformatics approach. J. Biomol. Struct. Dynamics. 42, 7945–7956 (2024).
-
Gomes, L. G. R. et al. In Silico designed Multi-Epitope immunogen Tpme-VAC/LGCM-2022 May induce both cellular and humoral immunity against Treponema pallidum infection. Vaccines 10, 1019 (2022).
-
Wang, P. et al. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput. Biol. 4, e1000048 (2008).
-
Maleki, A., Russo, G., Parasiliti Palumbo, G. A. & Pappalardo, F. In Silico design of Recombinant multi-epitope vaccine against influenza A virus. BMC Bioinform. 22, 617 (2022).
-
EL-Manzalawy, Y., Dobbs, D. & Honavar, V. Predicting linear B‐cell epitopes using string kernels. J. Mol. Recognit. 21, 243–255 (2008).
-
Srivastava, K. & Srivastava, V. Prediction of conformational and linear B-Cell epitopes on envelop protein of Zika virus using immunoinformatics approach. Int. J. Pept. Res. Ther. 29, 17 (2023).
-
Liu, F., Yuan, C., Chen, H. & Yang, F. Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings. Sci. Rep. 14, 2464 (2024).
-
Sarkar, B., Ullah, M. A., Johora, F. T., Taniya, M. A. & Araf, Y. Immunoinformatics-guided designing of epitope-based subunit vaccines against the SARS Coronavirus-2 (SARS-CoV-2). Immunobiology 225, 151955 (2020).
-
Jamali, S., Zamanzadeh, Z., Asadzadeh, A., Owji, F. & Abkar, M. Design of a multi-epitope vaccine using HA and M1 proteins from influenza and S, E, and M proteins from SARS-CoV-2 by in Silico tools. Inf. Med. Unlocked. 43, 101397 (2023).
-
Stratmann, T. Cholera toxin subunit B as Adjuvant––An accelerator in protective immunity and a break in autoimmunity. Vaccines 3, 579–596 (2015).
-
Shamriz, S., Ofoghi, H. & Moazami, N. Effect of linker length and residues on the structure and stability of a fusion protein with malaria vaccine application. Comput. Biol. Med. 76, 24–29 (2016).
-
Sanchez, A. E., Aquino, G., Ostoa-Saloma, P. & Laclette, J. P. Rocha-Zavaleta, L. Cholera toxin B-Subunit gene enhances mucosal Immunoglobulin A, Th1-Type, and CD8+ cytotoxic responses when coadministered intradermally with a DNA vaccine. Clin. Vaccine Immunol. 11, 711–719 (2004).
-
Liljeroos, L., Malito, E., Ferlenghi, I. & Bottomley, M. J. Structural and computational biology in the design of Immunogenic vaccine antigens. J. Immunol. Res. 2015, 1–17 (2015).
-
Bui, H. H. et al. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinform. 7, 153 (2006).
-
Nguyen, T. L. & Kim, H. Discovering peptides and computational investigations of a multiepitope vaccine target Mycobacterium tuberculosis. Synth. Syst. Biotechnol. 9, 391–405 (2024).
-
Department of Computational Biology & & Bioinformatics Jacob school of biotechnology & Bioengineering, Sam Higginbottom Institute of agriculture technology & Sciences, Allahabad-211007, Uttar Pradesh, Bharat (India). MFPPI – Multi FASTA ProtParam Interface. Bioinformation. 12, 74–77 (2016).
-
Geourjon, C. & Deléage, G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics 11, 681–684 (1995).
-
Bertoline, L. M. F., Lima, A. N., Krieger, J. E. & Teixeira, S. K. Before and after AlphaFold2: an overview of protein structure prediction. Front. Bioinform. 3, 1120370 (2023).
-
Heo, L., Park, H. & Seok, C. GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Res. 41, W384–W388 (2013).
-
Wang, W. et al. Data set for phylogenetic tree and RAMPAGE Ramachandran plot analysis of SODs in Gossypium raimondii and G. arboreum. Data Brief. 9, 345–348 (2016).
-
Binbay, F. A., Rathod, D. C., George, A. A. P. & Imhof, D. Quality assessment of selected protein structures derived from homology modeling and alphafold. Pharmaceuticals 16, 1662 (2023).
-
Ponomarenko, J. et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinform. 9, 514 (2008).
-
Tahir, U. et al. Multiepitope-Based subunit vaccine design and evaluation against respiratory syncytial virus using reverse vaccinology approach. Vaccines 8, 288 (2020).
-
Tahir, U. et al. Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: immunoinformatics and in Silico approaches. PLoS ONE. 15, e0244176 (2020).
-
Maeshima, N. & Fernandez, R. C. Recognition of lipid A variants by the TLR4-MD-2 receptor complex. Front Cell. Inf. Microbio 3, (2013).
-
Ain, Q. U., Batool, M. & Choi, S. TLR4-Targeting therapeutics: structural basis and Computer-Aided drug discovery approaches. Molecules 25, 627 (2020).
-
Kozakov, D. et al. The cluspro web server for protein–protein Docking. Nat. Protoc. 12, 255–278 (2017).
-
Raj, K. H. et al. A robust comprehensive immunoinformatics approach for designing a potential multi-epitope based vaccine against a reiterated Monkeypox virus. Biochem. Biophys. Rep. 43, 102075 (2025).
-
Schmit, J. D., Kariyawasam, N. L., Needham, V. & Smith, P. E. SLTCAP: A simple method for calculating the number of ions needed for MD simulation. J. Chem. Theory Comput. 14, 1823–1827 (2018).
-
Abraham, M. J. & Gready, J. E. Optimization of parameters for molecular dynamics simulation using smooth particle-mesh Ewald in GROMACS 4.5. J. Comput. Chem. 32, 2031–2040 (2011).
-
Quezada, G. R. et al. Molecular Dynamics Study of Polyacrylamide and Polysaccharide-Derived Flocculants Adsorption on Mg(OH)2 Surfaces at pH 11. Polymers 17, 227 (2025).
-
Childers, M. C. & Daggett, V. Validating molecular dynamics simulations against experimental observables in light of underlying conformational ensembles. J. Phys. Chem. B. 122, 6673–6689 (2018).
-
Hunt-Isaak, I., Russell, J. & Hekstra, D. mpl-interactions: A python package for interactivematplotlib figures. JOSS 9, 5651 (2024).
-
Shakibay Senobari, Z. et al. Chromone-embedded peptidomimetics and furopyrimidines as highly potent SARS-CoV-2 infection inhibitors: Docking and MD simulation study. BMC Res. Notes. 16, 224 (2023).
-
Elshafei, S. O., Mahmoud, N. A. & Almofti, Y. A. Immunoinformatics, molecular Docking and dynamics simulation approaches unveil a multi epitope-based potent peptide vaccine candidate against avian leukosis virus. Sci. Rep. 14, 2870 (2024).
-
López-Blanco, J. R., Aliaga, J. I., Quintana-Ortí, E. S. & Chacón, P. iMODS: internal coordinates normal mode analysis server. Nucleic Acids Res. 42, W271–W276 (2014).
-
Ma, J. Usefulness and limitations of normal mode analysis in modeling dynamics of biomolecular complexes. Structure 13, 373–380 (2005).
-
Singh, K. V. Eigenvalue and eigenvector computation for discrete and continuous structures composed of viscoelastic materials. Int. J. Mech. Sci. 110, 127–137 (2016).
-
Yu, C. C., Raj, N. & Chu, J. W. Statistical learning of protein elastic network from positional covariance matrix. Comput. Struct. Biotechnol. J. 21, 2524–2535 (2023).
-
Ashgar, S. S. et al. Integrated immunoinformatics and subtractive proteomics approach for multi-epitope vaccine designing to combat S. pneumoniae TIGR4. Front. Mol. Biosci. 10, 1212119 (2023).
-
Kumar, K. M. et al. Immunoinformatic exploration of a multi-epitope-based peptide vaccine candidate targeting emerging variants of SARS-CoV-2. Front. Microbiol. 14, 1251716 (2023).
-
Bergström, T. F., Josefsson, A., Erlich, H. A. & Gyllensten, U. Recent origin of HLA-DRB1 alleles and implications for human evolution. Nat. Genet. 18, 237–242 (1998).
-
Russo, G. et al. In Silico trial to test COVID-19 candidate vaccines: a case study with UISS platform. BMC Bioinform. 21, 527 (2020).
-
Grote, A. et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. 33, W526–W531 (2005).
-
Parvathy, S. T., Udayasuriyan, V. & Bhadana, V. Codon usage bias. Mol. Biol. Rep. 49, 539–565 (2022).
-
Paremskaia, A. I. et al. Codon-optimization in gene therapy: promises, prospects and challenges. Front. Bioeng. Biotechnol. 12, 1371596 (2024).
-
Kelpšas, V. & Wachenfeldt, C. V. Strain improvement of Escherichia coli K-12 for Recombinant production of deuterated proteins. Sci. Rep. 9, 17694 (2019).
-
Chick, J. A., Abongdia, N. N., Shey, R. A. & Apinjoh, T. O. Computational design, expression, and characterization of a Plasmodium falciparum multi-epitope, multi-stage vaccine candidate (PfCTMAG). Heliyon 11, e42014 (2025).
-
Niyogi, S. K. & Shigellosis J. Microbiol. 43, 133–143 (2005).
-
Zaidi, M. B., Estrada-García, T. & Shigella A highly virulent and elusive pathogen. Curr. Trop. Med. Rep. https://doi.org/10.1007/s40475-014-0019-6 (2014).
-
Botos, I. et al. Structural and functional characterization of the LPS transporter LptDE from Gram-Negative pathogens. Structure 24, 965–976 (2016).
-
Bisson-Filho, A. W. et al. Treadmilling by FtsZ filaments drives peptidoglycan synthesis and bacterial cell division. Science 355, 739–743 (2017).
-
Hou, J. et al. Cholera toxin B subunit acts as a potent systemic adjuvant for HIV-1 DNA vaccination intramuscularly in mice. Hum. Vaccines Immunotherapeutics. 10, 1274–1283 (2014).
-
Srinivasan, S. et al. Epitope identification and designing a potent multi-epitope vaccine construct against SARS-CoV-2 including the emerging variants. J. Global Infect. Dis. 14, 24 (2022).
-
Khan, M. T. et al. Immunoinformatics and molecular modeling approach to design universal multi-epitope vaccine for SARS-CoV-2. Inf. Med. Unlocked. 24, 100578 (2021).
-
Shang, K. et al. Development of a novel multi-epitope vaccine for brucellosis prevention. Heliyon 10, e34721 (2024).
-
Tan, C. et al. Immunoinformatics approach toward the introduction of a novel Multi-Epitope vaccine against clostridium difficile. Front. Immunol. 13, 887061 (2022).
-
Woelke, A. L. et al. Development of Immune-Specific interaction potentials and their application in the Multi-Agent-System vaccimm. PLoS ONE. 6, e23257 (2011).
-
Mahmoodi, S., Amirzakaria, J. Z. & Ghasemian, A. In Silico design and validation of a novel multi-epitope vaccine candidate against structural proteins of Chikungunya virus using comprehensive immunoinformatics analyses. PLoS ONE. 18, e0285177 (2023).
-
Qureshi, H. et al. Designing a multi-epitope vaccine against Shigella dysenteriae using immuno-informatics approach. Front. Genet. 15, 1361610 (2024).
-
Farhani, I., Nezafat, N. & Mahmoodi, S. Designing a novel Multi-epitope peptide vaccine against pathogenic Shigella spp. Based immunoinformatics approaches. Int. J. Pept. Res. Ther. 25, 541–553 (2019).
-
Hashemi, P. et al. A multi-epitope protein vaccine encapsulated in alginate nanoparticles as a candidate vaccine against Shigella sonnei. Sci. Rep. 14, 22484 (2024).
-
Jalal, K. et al. Identification of vaccine and drug targets in Shigella dysenteriae sd197 using reverse vaccinology approach. Sci. Rep. 12, 251 (2022).
-
Raso, M. M., Arato, V., Gasperini, G. & Micoli, F. Toward a Shigella vaccine: opportunities and challenges to fight an Antimicrobial-Resistant pathogen. IJMS 24, 4649 (2023).
-
Martić-Kehl, M. I., Schibli, R. & Schubiger, P. A. Can animal data predict human outcome? Problems and pitfalls of translational animal research. Eur. J. Nucl. Med. Mol. Imaging. 39, 1492–1496 (2012).
-
Ullah, H., Mahmud, S., Hossain, M. J., Islam, M. S. B. & Kibria, K. M. K. Immunoinformatic identification of the epitope-based vaccine candidates from Maltoporin, FepA and OmpW of Shigella Spp, with molecular Docking confirmation. Infect. Genet. Evol. 96, 105129 (2021).
