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Research Articles

In silico design of epitope-based peptide vaccine against non-typhoidal Salmonella through immunoinformatic approaches

, , , , , & ORCID Icon show all
Pages 10696-10714 | Received 05 Mar 2021, Accepted 20 Jun 2021, Published online: 11 Jul 2021

References

  • Adhikari, U. K., Tayebi, M., & Rahman, M. M. (2018). Immunoinformatics approach for epitope-based peptide vaccine design and active site prediction against polyprotein of emerging oropouche virus. Journal of Immunology Research, 2018, 6718083. https://doi.org/10.1155/2018/6718083
  • Anderson, R. J., Weng, Z., Campbell, R. K., & Jiang, X. (2005). Main-chain conformational tendencies of amino acids. Proteins, 60(4), 679–689. https://doi.org/10.1002/prot.20530
  • Andino, A., & Hanning, I. (2015). Salmonella enterica: Survival, colonization, and virulence differences among serovars. The Scientific World Journal, 2015, 1–16. https://doi.org/10.1155/2015/520179
  • Ao, T. T., Feasey, N. A., Gordon, M. A., Keddy, K. H., Angulo, F. J., & Crump, J. A. (2015). Global burden of invasive nontyphoidal Salmonella disease, 2010. Emerging Infectious Diseases, 21(6), 941–949. https://doi.org/10.3201/eid2106.140999
  • Aoki, Y., Kitazawa, K., Kobayashi, H., Senda, M., Arahata, Y., Homma, R., Watanabe, Y., & Honda, A. (2017). Clinical features of children with nontyphoidal Salmonella bacteremia: A single institution survey in rural Japan. PLoS One, 12(6), e0176990. https://doi.org/10.1371/journal.pone.0176990
  • Arifuzzaman, M., Mitra, S., Das, R., Hamza, A., Absar, N., & Dash, R. (2020). In silico analysis of nonsynonymous single-nucleotide polymorphisms (nsSNPs) of the SMPX gene. Annals of Human Genetics, 84(1), 54–71. https://doi.org/10.1111/ahg.12350
  • Bhattacharya, D., Nowotny, J., Cao, R., & Cheng, J. (2016). 3Drefine: An interactive web server for efficient protein structure refinement. Nucleic Acids Research, 44(W1), W406–W409. https://doi.org/10.1093/nar/gkw336
  • Biovia, D. S. (2015). Discovery studio visualizer v4.5. Release.
  • Braden, C. R. (2006). Salmonella enterica serotype Enteritidis and eggs: A national epidemic in the United States. Clinical Infectious Diseases, 43(4), 512–517. https://doi.org/10.1086/505973
  • Buchanan, S. K. (1999). β-Barrel proteins from bacterial outer membranes: Structure, function and refolding. Current Opinion in Structural Biology, 9(4), 455–461. https://doi.org/10.1016/S0959-440X(99)80064-5
  • Bui, H.-H., Sidney, J., Dinh, K., Southwood, S., Newman, M. J., & Sette, A. (2006). Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics, 7(1), 153. https://doi.org/10.1186/1471-2105-7-153
  • Bui, H.-H., Sidney, J., Li, W., Fusseder, N., & Sette, A. (2007). Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinformatics, 8(1), 361. https://doi.org/10.1186/1471-2105-8-361
  • Calis, J. J. A., Maybeno, M., Greenbaum, J. A., Weiskopf, D., De Silva, A. D., Sette, A., Keşmir, C., & Peters, B. (2013). Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Computational Biology, 9(10), e1003266. https://doi.org/10.1371/journal.pcbi.1003266
  • Cellucci, T., Seabrook, J. A., Chagla, Y., Bannister, S. L., & Salvadori, M. I. (2010). A 10-year retrospective review of Salmonella infections at the Children's Hospital in London, Ontario. The Canadian Journal of Infectious Diseases & Medical Microbiology/Journal Canadien Des Maladies Infectieuses et de la Microbiologie Medicale, 21(2), 78–82. https://doi.org/10.1155/2010/968960
  • Chandra, S., Singh, D., & Singh, T. R. (2010). Prediction and characterization of T-cell epitopes for epitope vaccine design from outer membrane protein of Neisseria meningitidis serogroup B. Bioinformation, 5(4), 155–161. https://doi.org/10.6026/97320630005155
  • Cheng, J., Randall, A. Z., Sweredoski, M. J., & Baldi, P. (2005). SCRATCH: A protein structure and structural feature prediction server. Nucleic Acids Research, 33(Web Server issue), W72–6. https://doi.org/10.1093/nar/gki396
  • Chiu, C.-H., Wu, T.-L., Su, L.-H., Chu, C., Chia, J.-H., Kuo, A.-J., Chien, M.-S., & Lin, T.-Y. (2002). The emergence in Taiwan of fluoroquinolone resistance in Salmonella enterica serotype Choleraesuis. The New England Journal of Medicine, 346(6), 413–419. https://doi.org/10.1056/NEJMoa012261
  • Chiu, C.-H., Wu, T.-L., Su, L.-H., Liu, J.-W., & Chu, C. (2004). Fluoroquinolone resistance in Salmonella enterica serotype Choleraesuis, Taiwan, 2000–2003. Emerging Infectious Diseases, 10(9), 1674–1676. https://doi.org/10.3201/eid1009.030596
  • Cojocaru, C., & Clima, L. (2019). Binding assessment of methylene blue to human serum albumin and poly (acrylic acid): Experimental and computer-aided modeling studies. Journal of Molecular Liquids, 285, 811–821. https://doi.org/10.1016/j.molliq.2019.04.144
  • Colovos, C., & Yeates, T. O. (1993). Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Science, 2(9), 1511–1519. https://doi.org/10.1002/pro.5560020916
  • Consortium, U. (2014). UniProt: A hub for protein information. Nucleic Acids Research, 43(D1), D204–D212.
  • Crump, J. A., Medalla, F. M., Joyce, K. W., Krueger, A. L., Hoekstra, R. M., Whichard, J. M., & Barzilay, E. J. (2011). Antimicrobial resistance among invasive nontyphoidal Salmonella enterica isolates in the United States: National Antimicrobial Resistance Monitoring System, 1996 to 2007. Antimicrobial Agents and Chemotherapy, 55(3), 1148–1154. https://doi.org/10.1128/AAC.01333-10
  • Dash, R., Ali, M. C., Dash, N., Azad, M. A. K., Hosen, S. M. Z., Hannan, M. A., & Moon, I. S. (2019a). Structural and dynamic characterizations highlight the deleterious role of SULT1A1 R213H polymorphism in substrate binding. International Journal of Molecular Sciences, 20(24), 6256. https://doi.org/10.3390/ijms20246256
  • Dash, R., Arifuzzaman, M., Mitra, S., Abdul Hannan, M., Absar, N., & Hosen, S. M. Z. (2019b). Unveiling the structural insights into the selective inhibition of protein kinase D1. Current Pharmaceutical Design, 25(10), 1059–1074. https://doi.org/10.2174/1381612825666190527095510
  • Dash, R., Choi, H. J., & Moon, I. S. (2020). Mechanistic insights into the deleterious roles of Nasu-Hakola disease associated TREM2 variants. Scientific Reports, 10(1), 3663. https://doi.org/10.1038/s41598-020-60561-x
  • Dash, R., Das, R., Junaid, M., Akash, M. F. C., Islam, A., & Hosen, S. Z. (2017). In silico-based vaccine design against Ebola virus glycoprotein. Advances and Applications in Bioinformatics and Chemistry: AABC, 10, 11–28. https://doi.org/10.2147/AABC.S115859
  • Dash, R., Hosen, S. M. Z., Sultana, T., Junaid, M., Majumder, M., Ishat, I. A., & Uddin, M. M. N. (2016). Computational analysis and binding site identification of type III secretion system ATPase from Pseudomonas aeruginosa. Interdisciplinary Sciences, Computational Life Sciences, 8(4), 403–411. https://doi.org/10.1007/s12539-015-0121-z
  • Dash, R., Junaid, M., Mitra, S., Arifuzzaman, M., & Hosen, S. M. Z. (2019c). Structure-based identification of potent VEGFR-2 inhibitors from in vivo metabolites of a herbal ingredient. Journal of Molecular Modeling, 25(4), 98. https://doi.org/10.1007/s00894-019-3979-6
  • Dash, R., Mitra, S., Arifuzzaman, M., & Zahid Hosen, S. M. (2018). In silico quest of selective naphthyl-based CREBBP bromodomain inhibitor. In Silico Pharmacology, 6(1), 1. https://doi.org/10.1007/s40203-018-0038-4
  • Dempsey, L. A. (2020). Vaccines vs antibiotics. Nature Immunology, 21(6), 596–596. https://doi.org/10.1038/s41590-020-0701-x
  • Dickson, C. J., Madej, B. D., Skjevik, A. A., Betz, R. M., Teigen, K., Gould, I. R., & Walker, R. C. (2014). Lipid14: The amber lipid force field. Journal of Chemical Theory and Computation, 10(2), 865–879. https://doi.org/10.1021/ct4010307
  • Dimitrov, I., Bangov, I., Flower, D. R., & Doytchinova, I. (2014). AllerTOP v.2-a server for in silico prediction of allergens. Journal of Molecular Modeling, 20(6), 2278. https://doi.org/10.1007/s00894-014-2278-5
  • Doytchinova, I. A., & Flower, D. R. (2007). VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics, 8(1), 4. https://doi.org/10.1186/1471-2105-8-4
  • Elkenany, R., Elsayed, M. M., Zakaria, A. I., El-Sayed, S. A-E-S., & Rizk, M. A. (2019). Antimicrobial resistance profiles and virulence genotyping of Salmonella enterica serovars recovered from broiler chickens and chicken carcasses in Egypt. BMC Veterinary Research, 15(1), 124. https://doi.org/10.1186/s12917-019-1867-z
  • EL‐Manzalawy, Y., Dobbs, D., & Honavar, V. (2008). Predicting linear B-cell epitopes using string kernels. Journal of Molecular Recognition: JMR, 21(4), 243–255. https://doi.org/10.1002/jmr.893
  • Eng, S.-K., Pusparajah, P., Ab Mutalib, N.-S., Ser, H.-L., Chan, K.-G., & Lee, L.-H. (2015). Salmonella: A review on pathogenesis, epidemiology and antibiotic resistance. Frontiers in Life Science, 8(3), 284–293. https://doi.org/10.1080/21553769.2015.1051243
  • Esser, M. T., Marchese, R. D., Kierstead, L. S., Tussey, L. G., Wang, F., Chirmule, N., & Washabaugh, M. W. (2003). Memory T cells and vaccines. Vaccine, 21(5–6), 419–430. https://doi.org/10.1016/S0264-410X(02)00407-3
  • Fang, D. (2019). T-bet expression is fine-tuned for balancing IFN-γ-producing Th1 and Tfh cell differentiation and IgG2a(c) production. The Journal of Immunology, 202(1 Suppl), 124.13.
  • Fleri, W., Paul, S., Dhanda, S. K., Mahajan, S., Xu, X., Peters, B., & Sette, A. (2017). The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. Frontiers in Immunology, 8, 278. https://doi.org/10.3389/fimmu.2017.00278
  • Fu, Y., Zhao, J., & Chen, Z. (2018). Insights into the molecular mechanisms of protein-ligand interactions by molecular docking and molecular dynamics simulation: A case of oligopeptide binding protein. Computational and Mathematical Methods in Medicine, 2018, 3502514. https://doi.org/10.1155/2018/3502514
  • Gerritzen, M. J. H., Martens, D. E., Wijffels, R. H., van der Pol, L., & Stork, M. (2017). Bioengineering bacterial outer membrane vesicles as vaccine platform. Biotechnology Advances, 35(5), 565–574. https://doi.org/10.1016/j.biotechadv.2017.05.003
  • Gu, H., Liao, Y., Zhang, J., Wang, Y., Liu, Z., Cheng, P., Wang, X., Zou, Q., & Gu, J. (2018). Rational design and evaluation of an artificial Escherichia coli K1 protein vaccine candidate based on the structure of OmpA. Frontiers in Cellular and Infection Microbiology, 8, 172. https://doi.org/10.3389/fcimb.2018.00172
  • Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., & Raghava, G. P. S. (2013). In silico approach for predicting toxicity of peptides and proteins. PLoS One, 8(9), e73957. https://doi.org/10.1371/journal.pone.0073957
  • Haeusler, G. M., & Curtis, N. (2013). Non-typhoidal Salmonella in children: Microbiology, epidemiology and treatment, in Hot Topics in Infection and Immunity in Children IX (pp. 13–26). Springer.
  • Helms, M., Ethelberg, S., Mølbak, K., & DT104 Study Group. (2005). International Salmonella typhimurium DT104 infections, 1992–2001. Emerging Infectious Diseases, 11(6), 859–867. https://doi.org/10.3201/eid1106.041017
  • Hosen, S. M. Z., Dash, R., Junaid, M., Mitra, S., & Absar, N. (2019). Identification and structural characterization of deleterious non-synonymous single nucleotide polymorphisms in the human SKP2 gene. Computational Biology and Chemistry, 79, 127–136. https://doi.org/10.1016/j.compbiolchem.2019.02.003
  • Hosen, S. M. Z., Rubayed, M., Dash, R., Junaid, M., Mitra, S., Alam, M. S., & Dey, R. (2018). Prospecting and structural insight into the binding of novel plant-derived molecules of Leea indica as inhibitors of BACE1. Current Pharmaceutical Design, 24(33), 3972–3979. https://doi.org/10.2174/1381612824666181106111020
  • Hou, T., Wang, J., Li, Y., & Wang, W. (2011). Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. Journal of Chemical Information and Modeling, 51(1), 69–82. https://doi.org/10.1021/ci100275a
  • Hurley, D., McCusker, M. P., Fanning, S., & Martins, M. (2014). Salmonella-host interactions - modulation of the host innate immune system. Frontiers in Immunology, 5, 481. https://doi.org/10.3389/fimmu.2014.00481
  • Islam, M. A., Choi, H. J., Dash, R., Sharif, S. R., Oktaviani, D. F., Seog, D.-H., & Moon, I. S. (2020). N-acetyl-D-glucosamine kinase interacts with NudC and Lis1 in dynein motor complex and promotes cell migration. International Journal of Molecular Sciences, 22(1), 129. https://doi.org/10.3390/ijms22010129
  • Jacobson, M. P., Friesner, R. A., Xiang, Z., & Honig, B. (2002). On the role of the crystal environment in determining protein side-chain conformations. Journal of Molecular Biology, 320(3), 597–608. https://doi.org/10.1016/S0022-2836(02)00470-9
  • Jacobson, M. P., Pincus, D. L., Rapp, C. S., Day, T. J. F., Honig, B., Shaw, D. E., & Friesner, R. A. (2004). A hierarchical approach to all-atom protein loop prediction. Proteins, 55(2), 351–367. https://doi.org/10.1002/prot.10613
  • Jahangiri, A., Rasooli, I., Owlia, P., Fooladi, A. A. I., & Salimian, J. (2017). In silico design of an immunogen against Acinetobacter baumannii based on a novel model for native structure of outer membrane protein A. Microbial Pathogenesis, 105, 201–210. https://doi.org/10.1016/j.micpath.2017.02.028
  • Jajere, S. M. (2019). A review of Salmonella enterica with particular focus on the pathogenicity and virulence factors, host specificity and antimicrobial resistance including multidrug resistance. Veterinary World, 12(4), 504–521. https://doi.org/10.14202/vetworld.2019.504-521
  • Jespersen, M. C., Peters, B., Nielsen, M., & Marcatili, P. (2017). BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Research, 45(W1), W24–W29. https://doi.org/10.1093/nar/gkx346
  • John, M., Gaudieri, S., & Mallal, S. (2017). Chapter 6 - Immunogenetics and vaccination. In K. Modjarrad and W. C. Koff (Eds.), Human vaccines (pp. 113–133). Academic Press.
  • Karon, A. E., Archer, J. R., Sotir, M. J., Monson, T. A., & Kazmierczak, J. J. (2007). Human multidrug-resistant Salmonella newport infections, Wisconsin, 2003–2005. Emerging Infectious Diseases, 13(11), 1777–1780. https://doi.org/10.3201/eid1311.061138
  • Katz, D., Ben-Chetrit, E., Sherer, S.-S., Cohen, D., & Muhsen, K. (2019). Correlates of non-typhoidal Salmonella bacteraemia: A case-control study. International Journal of Infectious Diseases: IJID, 81, 170–175. https://doi.org/10.1016/j.ijid.2019.01.028
  • Kaur, H., Garg, A., & Raghava, G. (2007). PEPstr: A de novo method for tertiary structure prediction of small bioactive peptides. Protein and Peptide Letters, 14(7), 626–631.
  • Kim, Y., Ponomarenko, J., Zhu, Z., Tamang, D., Wang, P., Greenbaum, J., Lundegaard, C., Sette, A., Lund, O., Bourne, P. E., Nielsen, M., & Peters, B. (2012). Immune epitope database analysis resource. Nucleic Acids Research, 40(Web Server issue), W525–W530. https://doi.org/10.1093/nar/gks438
  • Koebnik, R., Locher, K. P., & Van Gelder, P. (2000). Structure and function of bacterial outer membrane proteins: Barrels in a nutshell. Molecular Microbiology, 37(2), 239–253. https://doi.org/10.1046/j.1365-2958.2000.01983.x
  • Kopp, J., & Schwede, T. (2004). Automated protein structure homology modeling: A progress report. Pharmacogenomics, 5(4), 405–416. https://doi.org/10.1517/14622416.5.4.405
  • Krieger, E., & Vriend, G. (2015). New ways to boost molecular dynamics simulations. Journal of Computational Chemistry, 36(13), 996–1007. https://doi.org/10.1002/jcc.23899
  • Krieger, E., Nielsen, J. E., Spronk, C. A. E. M., & Vriend, G. (2006). Fast empirical pKa prediction by Ewald summation. Journal of Molecular Graphics & Modelling, 25(4), 481–486. https://doi.org/10.1016/j.jmgm.2006.02.009
  • Land, H., & Humble, M. S. (2018). YASARA: A tool to obtain structural guidance in biocatalytic investigations, in Protein Engineering (pp. 43–67). Springer.
  • Larsen, M. V., Lundegaard, C., Lamberth, K., Buus, S., Lund, O., & Nielsen, M. (2007). Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics, 8(1), 424. https://doi.org/10.1186/1471-2105-8-424
  • Laskowski, R. A. (2001). PDBsum: Summaries and analyses of PDB structures. Nucleic Acids Research, 29(1), 221–222. https://doi.org/10.1093/nar/29.1.221
  • Laskowski, R. A., Rullmannn, J. A., MacArthur, M. W., Kaptein, R., & Thornton, J. M. (1996). AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR. Journal of Biomolecular NMR, 8(4), 477–486. https://doi.org/10.1007/BF00228148
  • Leung, L. K., & Wang, T. T. (2000). Bcl-2 is not reduced in the death of MCF-7 cells at low genistein concentration. The Journal of Nutrition, 130(12), 2922–2926. https://doi.org/10.1093/jn/130.12.2922
  • Lin, J., Huang, S., & Zhang, Q. (2002). Outer membrane proteins: Key players for bacterial adaptation in host niches. Microbes and Infection, 4(3), 325–331. https://doi.org/10.1016/S1286-4579(02)01545-9
  • Lipsitch, M., & Siber, G. R. (2016). How can vaccines contribute to solving the antimicrobial resistance problem? mBio, 7(3), e00428–16. https://doi.org/10.1128/mBio.00428-16
  • Majowicz, S. E., Musto, J., Scallan, E., Angulo, F. J., Kirk, M., O'Brien, S. J., Jones, T. F., Fazil, A., Hoekstra, R. M., & International Collaboration on Enteric Disease 'Burden of Illness' Studies. (2010). The global burden of nontyphoidal Salmonella gastroenteritis. Clinical Infectious Diseases, 50(6), 882–889. https://doi.org/10.1086/650733
  • María, R. A. R. (2017). The impact of bioinformatics on vaccine design and development. InTech.
  • McKeever, T. M., Lewis, S. A., Smith, C., & Hubbard, R. (2004). Vaccination and allergic disease: A birth cohort study. American Journal of Public Health, 94(6), 985–989. https://doi.org/10.2105/ajph.94.6.985
  • Mitra, S., & Dash, R. (2018). Structural dynamics and quantum mechanical aspects of shikonin derivatives as CREBBP bromodomain inhibitors. Journal of Molecular Graphics & Modelling, 83, 42–52. https://doi.org/10.1016/j.jmgm.2018.04.014
  • Monterrubio-López, G. P., González-Y-Merchand, J. A., & Ribas-Aparicio, R. M. (2015). Identification of novel potential vaccine candidates against tuberculosis based on reverse vaccinology. BioMed Research International, 2015, 483150. https://doi.org/10.1155/2015/483150
  • Munni, Y. A., Ali, M. C., Selsi, N. J., Sultana, M., Hossen, M., Bipasha, T. H., Rahman, M., Uddin, M. N., Hosen, S. M. Z., & Dash, R. (2021). Molecular simulation studies to reveal the binding mechanisms of shikonin derivatives inhibiting VEGFR-2 kinase. Computational Biology and Chemistry, 90, 107414. https://doi.org/10.1016/j.compbiolchem.2020.107414
  • Nain, Z., Abdulla, F., Rahman, M. M., Karim, M. M., Khan, M. S. A., Sayed, S. B., Mahmud, S., Rahman, S. M. R., Sheam, M. M., Haque, Z., & Adhikari, U. K. (2020a). Proteome-wide screening for designing a multi-epitope vaccine against emerging pathogen Elizabethkingia anophelis using immunoinformatic approaches. Journal of Biomolecular Structure & Dynamics, 38(16), 4850–4867. https://doi.org/10.1080/07391102.2019.1692072
  • Nain, Z., Karim, M. M., Sen, M. K., & Adhikari, U. K. (2020b). Structural basis and designing of peptide vaccine using PE-PGRS family protein of Mycobacterium ulcerans - An integrated vaccinomics approach. Molecular Immunology, 120, 146–163. https://doi.org/10.1016/j.molimm.2020.02.009
  • Nair, V. T., Venkitanarayanan, D., & Kollanoor Johny, A. (2018). Antibiotic-resistant Salmonella in the food supply and the potential role of antibiotic alternatives for control. Foods, 7(10), 167. https://doi.org/10.3390/foods7100167
  • Nielsen, M., Lundegaard, C., & Lund, O. (2007). Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics, 8(1), 238. https://doi.org/10.1186/1471-2105-8-238
  • Oladipo, E. K., Ajayi, A. F., Onile, O. S., Ariyo, O. E., Jimah, E. M., Ezediuno, L. O., Adebayo, O. I., Adebayo, E. T., Odeyemi, A. N., Oyeleke, M. O., Oyewole, M. P., Oguntomi, A. S., Akindiya, O. E., Aremu, V. O., Aboderin, D. O., & Oloke, J. K. (2021). Designing a conserved peptide-based subunit vaccine against SARS-CoV-2 using immunoinformatics approach. In Silico Pharmacology, 9(1), 8. https://doi.org/10.1007/s40203-020-00062-x
  • Pan, J., Li, C., & Ye, Z. (2016). Immunoproteomic approach for screening vaccine candidates from bacterial outer membrane proteins. In Vaccine design (pp. 519–528). Springer.
  • Peters, B., & Sette, A. (2005). Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics, 6(1), 132. https://doi.org/10.1186/1471-2105-6-132
  • Peters, B., Bulik, S., Tampe, R., Van Endert, P. M., & Holzhütter, H.-G. (2003). Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors. Journal of Immunology (Baltimore, MD: 1950), 171(4), 1741–1749. https://doi.org/10.4049/jimmunol.171.4.1741
  • Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera - a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. https://doi.org/10.1002/jcc.20084
  • Piccini, G., & Montomoli, E. (2020). Pathogenic signature of invasive non-typhoidal Salmonella in Africa: Implications for vaccine development. Human Vaccines & Immunotherapeutics, 16(9), 2056–2071. https://doi.org/10.1080/21645515.2020.1785791
  • Pontius, J., Richelle, J., & Wodak, S. J. (1996). Deviations from standard atomic volumes as a quality measure for protein crystal structures. Journal of Molecular Biology, 264(1), 121–136. https://doi.org/10.1006/jmbi.1996.0628
  • Rapin, N., Lund, O., Bernaschi, M., & Castiglione, F. (2010). Computational immunology meets bioinformatics: The use of prediction tools for molecular binding in the simulation of the immune system. PLoS One, 5(4), e9862. https://doi.org/10.1371/journal.pone.0009862
  • Rauta, P. R., Ashe, S., Nayak, D., & Nayak, B. (2016). In silico identification of outer membrane protein (Omp) and subunit vaccine design against pathogenic Vibrio cholerae. Computational Biology and Chemistry, 65, 61–68. https://doi.org/10.1016/j.compbiolchem.2016.10.004
  • Ripon, M. K. H., Lee, H., Dash, R., Choi, H. J., Oktaviani, D. F., Moon, I. S., & Haque, M. N. (2020). N-acetyl-D-glucosamine kinase binds dynein light chain roadblock 1 and promotes protein aggregate clearance. Cell Death & Disease, 11(8), 619. https://doi.org/10.1038/s41419-020-02862-7
  • Saha, S., & Raghava, G. (2006). Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins, 65(1), 40–48. https://doi.org/10.1002/prot.21078
  • Sanchez-Trincado, J. L., Gomez-Perosanz, M., & Reche, P. A. (2017). Fundamentals and methods for T-and B-cell epitope prediction. Journal of Immunology Research, 2017, 2680160. https://doi.org/10.1155/2017/2680160
  • Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013). Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. Journal of Computer-Aided Molecular Design, 27(3), 221–234. https://doi.org/10.1007/s10822-013-9644-8
  • Scallan, E., Hoekstra, R. M., Angulo, F. J., Tauxe, R. V., Widdowson, M.-A., Roy, S. L., Jones, J. L., & Griffin, P. M. (2011). Foodborne illness acquired in the United States—major pathogens. Emerging Infectious Diseases, 17(1), 7–15. https://doi.org/10.3201/eid1701.P11101
  • Schrödinger Release. (2018). Release 2018 version 1: Maestro. Schrödinger, LLC.
  • Singh, S., Singh, H., Tuknait, A., Chaudhary, K., Singh, B., Kumaran, S., & Raghava, G. P. S. (2015). PEPstrMOD: Structure prediction of peptides containing natural, non-natural and modified residues. Biology Direct, 10(1), 73. https://doi.org/10.1186/s13062-015-0103-4
  • Singletary, L. A., Karlinsey, J. E., Libby, S. J., Mooney, J. P., Lokken, K. L., Tsolis, R. M., Byndloss, M. X., Hirao, L. A., Gaulke, C. A., Crawford, R. W., Dandekar, S., Kingsley, R. A., Msefula, C. L., Heyderman, R. S., & Fang, F. C. (2016). Loss of multicellular behavior in epidemic African nontyphoidal Salmonella enterica serovar Typhimurium ST313 strain D23580. mBio, 7(2), e02265. https://doi.org/10.1128/mBio.02265-15
  • Stranzl, T., Larsen, M. V., Lundegaard, C., & Nielsen, M. (2010). NetCTLpan: Pan-specific MHC class I pathway epitope predictions. Immunogenetics, 62(6), 357–368. https://doi.org/10.1007/s00251-010-0441-4
  • Swanson, J. M., Henchman, R. H., & McCammon, J. A. (2004). Revisiting free energy calculations: A theoretical connection to MM/PBSA and direct calculation of the association free energy. Biophysical Journal, 86(1), 67–74. https://doi.org/10.1016/S0006-3495(04)74084-9
  • Tahir Ul Qamar, M., Rehman, A., Tusleem, K., Ashfaq, U. A., Qasim, M., Zhu, X., Fatima, I., Shahid, F., & Chen, L.-L. (2020a). Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches. PLoS One, 15(12), e0244176. https://doi.org/10.1371/journal.pone.0244176
  • Tahir Ul Qamar, M., Shahid, F., Aslam, S., Ashfaq, U. A., Aslam, S., Fatima, I., Fareed, M. M., Zohaib, A., & Chen, L.-L. (2020b). Reverse vaccinology assisted designing of multiepitope-based subunit vaccine against SARS-CoV-2. Infectious Diseases of Poverty, 9(1), 132. https://doi.org/10.1186/s40249-020-00752-w
  • Tenzer, S., Peters, B., Bulik, S., Schoor, O., Lemmel, C., Schatz, M. M., Kloetzel, P.-M., Rammensee, H.-G., Schild, H., & Holzhütter, H.-G. (2005). Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding. Cellular and Molecular Life Sciences: CMLS, 62(9), 1025–1037. https://doi.org/10.1007/s00018-005-4528-2
  • Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334
  • Tükel, C., Raffatellu, M., Humphries, A. D., Wilson, R. P., Andrews-Polymenis, H. L., Gull, T., Figueiredo, J. F., Wong, M. H., Michelsen, K. S., Akçelik, M., Adams, L. G., & Bäumler, A. J. (2005). CsgA is a pathogen-associated molecular pattern of Salmonella enterica serotype Typhimurium that is recognized by Toll-like receptor 2. Molecular Microbiology, 58(1), 289–304. https://doi.org/10.1111/j.1365-2958.2005.04825.x
  • Uche, I. V., MacLennan, C. A., & Saul, A. (2017). A systematic review of the incidence, risk factors and case fatality rates of invasive nontyphoidal Salmonella (iNTS) disease in Africa (1966 to 2014). PLoS Neglected Tropical Diseases, 11(1), e0005118. https://doi.org/10.1371/journal.pntd.0005118
  • Varma, J. K., Molbak, K., Barrett, T. J., Beebe, J. L., Jones, T. F., Rabatsky-Ehr, T., Smith, K. E., Vugia, D. J., Chang, H.-G H., & Angulo, F. J. (2005). Antimicrobial-resistant nontyphoidal Salmonella is associated with excess bloodstream infections and hospitalizations. The Journal of Infectious Diseases, 191(4), 554–561. https://doi.org/10.1086/427263
  • Verma, S., Sugadev, R., Kumar, A., Chandna, S., Ganju, L., & Bansal, A. (2018). Multi-epitope DnaK peptide vaccine against S.Typhi: An in silico approach. Vaccine, 36(28), 4014–4022. https://doi.org/10.1016/j.vaccine.2018.05.106
  • Weng, G., Wang, E., Wang, Z., Liu, H., Zhu, F., Li, D., & Hou, T. (2019). 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. https://doi.org/10.1093/nar/gkz397
  • Wiederstein, M., & Sippl, M. J. (2007). ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Research, 35(Web Server issue), W407–W410. https://doi.org/10.1093/nar/gkm290
  • Wilkins, M. R., Gasteiger, E., Bairoch, A., Sanchez, J. C., Williams, K. L., Appel, R. D., & Hochstrasser, D. F. (1999). Protein identification and analysis tools in the ExPASy server. Methods in Molecular Biology (Clifton, N.J.), 112, 531–552.
  • Xu, D., & Zhang, Y. (2011). Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophysical Journal, 101(10), 2525–2534. https://doi.org/10.1016/j.bpj.2011.10.024
  • Yan, C., Xu, X., & Zou, X. (2016). Fully blind docking at the atomic level for protein-peptide complex structure prediction. Structure (London, England: 1993), 24(10), 1842–1853. https://doi.org/10.1016/j.str.2016.07.021

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