1. Cesarman, E., et al., Kaposi sarcoma. Nature Reviews Disease Primers, 2019. 5(1): p. 9.
2. Barrett, L., et al., Role of Interleukin-1 Family Members and Signaling Pathways in KSHV Pathogenesis. Frontiers in Cellular and Infection Microbiology, 2020. 10: p. 679.
3. Iftode, N., et al., Update on Kaposi sarcoma-associated herpesvirus (KSHV or HHV8)–review. Romanian Journal of Internal Medicine, 2020. 58(4): p. 199-208.
4. Arranz-Caso, A., et al., Kaposi sarcoma presenting shortly after primary infection by HIV and human herpesvirus-8. Aids, 2018. 32(2): p. 271-275.
5. Rewane, A. and P. Tadi, Herpes Virus Type 8. 2020.
6. Adegbidi, H., et al., Epidemic, Endemic, or Stewart–Bluefarb? When Several Forms of Kaposi Seem to Dispute Paternity. Case reports in dermatological medicine, 2020. 2020.
7. Wu, L., et al., Three-dimensional structure of the human herpesvirus 8 capsid. Journal of virology, 2000. 74(20): p. 9646-9654.
8. Yan, L., et al., Towards better understanding of KSHV life cycle: from transcription and posttranscriptional regulations to pathogenesis. Virologica Sinica, 2019. 34(2): p. 135-161.
9. Plancoulaine, S. and A. Gessain, Epidemiological aspects of human herpesvirus 8 infection and of Kaposi's sarcoma. Medecine et maladies infectieuses, 2005. 35(5): p. 314-321.
10. Mbulaiteye, S.M., et al., Human herpesvirus 8 infection and transfusion history in children with sickle-cell disease in Uganda. Journal of the National Cancer Institute, 2003. 95(17): p. 1330-1335.
11. Cesaro, S., et al., Incidence and outcome of Kaposi sarcoma after hematopoietic stem cell transplantation: a retrospective analysis and a review of the literature, on behalf of infectious diseases working party of EBMT. Bone marrow transplantation, 2020. 55(1): p. 110-116.
12. Martín-Carbonero, L., et al., Pegylated liposomal doxorubicin plus highly active antiretroviral therapy versus highly active antiretroviral therapy alone in HIV patients with Kaposi's sarcoma. Aids, 2004. 18(12): p. 1737-1740.
13. Yarchoan, R., et al., Treatment of AIDS-related Kaposi's sarcoma with interleukin-12: rationale and preliminary evidence of clinical activity. Critical reviews in immunology, 2007. 27(5): p. 401.
14. Bernstein, Z.P., et al., A multicenter Phase II study of the intravenous administration of liposomal tretinoin in patients with acquired immunodeficiency syndrome‐associated Kaposi's sarcoma. Cancer: Interdisciplinary International Journal of the American Cancer Society, 2002. 95(12): p. 2555-2561.
15. Luppi, M., et al., Molecular evidence of organ-related transmission of Kaposi sarcoma–associated herpesvirus or human herpesvirus-8 in transplant patients. Blood, The Journal of the American Society of Hematology, 2000. 96(9): p. 3279-3281.
16. Nichols, L.A., L.A. Adang, and D.H. Kedes, Rapamycin blocks production of KSHV/HHV8: insights into the anti-tumor activity of an immunosuppressant drug. PloS one, 2011. 6(1): p. e14535.
17. Versteeg, L., et al., Enlisting the mRNA vaccine platform to combat parasitic infections. Vaccines, 2019. 7(4): p. 122.
18. Fadaka, A.O., et al., Immunoinformatics design of a novel epitope-based vaccine candidate against dengue virus. Scientific Reports, 2021. 11(1): p. 1-22.
19. Bahmani, B., et al., HPV16-E7 Protein T Cell Epitope Prediction and Global Therapeutic Peptide Vaccine Design Based on Human Leukocyte Antigen Frequency: An In-Silico Study. International Journal of Peptide Research and Therapeutics, 2021. 27(1): p. 365-378.
20. Kardani, K., A. Bolhassani, and A. Namvar, An overview of in silico vaccine design against different pathogens and cancer. Expert Review of Vaccines, 2020. 19(8): p. 699-726.
21. Pruitt, K.D., T. Tatusova, and D.R. Maglott, NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic acids research, 2005. 33(suppl_1): p. D501-D504.
22. Boutet, E., et al., Uniprotkb/swiss-prot, in Plant bioinformatics. 2007, Springer. p. 89-112.
23. Doytchinova, I.A. and D.R. Flower, VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics, 2007. 8(1): p. 1-7.
24. Larsen, M.V., et al., Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC bioinformatics, 2007. 8(1): p. 1-12.
25. Nielsen, M., et al., Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations. Protein Science, 2003. 12(5): p. 1007-1017.
26. Paul, S., et al., TepiTool: a pipeline for computational prediction of T cell epitope candidates. Current protocols in immunology, 2016. 114(1): p. 18.19. 1-18.19. 24.
27. Saha, I., G. Mazzocco, and D. Plewczynski, Consensus classification of human leukocyte antigen class II proteins. Immunogenetics, 2013. 65(2): p. 97-105.
28. EL‐Manzalawy, Y., D. Dobbs, and V. Honavar, Predicting linear B‐cell epitopes using string kernels. Journal of Molecular Recognition: An Interdisciplinary Journal, 2008. 21(4): p. 243-255.
29. 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.
30. Gupta, S., et al., Peptide toxicity prediction, in Computational peptidology. 2015, Springer. p. 143-157.
31. Krogh, A., et al., Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. Journal of molecular biology, 2001. 305(3): p. 567-580.
32. Wu, J., et al., DeepHLApan: a deep learning approach for neoantigen prediction considering both HLA-peptide binding and immunogenicity. Frontiers in immunology, 2019. 10: p. 2559.
33. Dhanda, S.K., P. Vir, and G.P. Raghava, Designing of interferon-gamma inducing MHC class-II binders. Biology direct, 2013. 8(1): p. 1-15.
34. Pahari, S., et al., Morbid sequences suggest molecular mimicry between microbial peptides and self-antigens: a possibility of inciting autoimmunity. Frontiers in microbiology, 2017. 8: p. 1938.
35. Nagpal, G., et al., Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Scientific reports, 2017. 7(1): p. 1-10.
36. Bui, H.H., et al., Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics, 2006. 7: p. 153.
37. Bui, H.-H., et al., Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC bioinformatics, 2007. 8(1): p. 1-6.
38. Kim, J., et al., Human β-defensin 2 plays a regulatory role in innate antiviral immunity and is capable of potentiating the induction of antigen-specific immunity. Virology journal, 2018. 15(1): p. 1-12.
39. Kim, D.T., et al., Introduction of soluble proteins into the MHC class I pathway by conjugation to an HIV tat peptide. The Journal of Immunology, 1997. 159(4): p. 1666-1668.
40. Nezafat, N., et al., A novel multi-epitope peptide vaccine against cancer: an in silico approach. Journal of theoretical biology, 2014. 349: p. 121-134.
41. Safavi, A., et al., In silico analysis of transmembrane protein 31 (TMEM31) antigen to design novel multiepitope peptide and DNA cancer vaccines against melanoma. Molecular immunology, 2019. 112: p. 93-102.
42. Livingston, B., et al., A rational strategy to design multiepitope immunogens based on multiple Th lymphocyte epitopes. The Journal of Immunology, 2002. 168(11): p. 5499-5506.
43. Shanmugam, A., et al., Synthetic Toll like receptor-4 (TLR-4) agonist peptides as a novel class of adjuvants. PLoS One, 2012. 7(2): p. e30839.
44. Hon, J., et al., SoluProt: prediction of soluble protein expression in Escherichia coli. Bioinformatics, 2021. 37(1): p. 23-28.
45. Buchan, D.W. and D.T. Jones, The PSIPRED protein analysis workbench: 20 years on. Nucleic acids research, 2019. 47(W1): p. W402-W407.
46. Kim, D.E., D. Chivian, and D. Baker, Protein structure prediction and analysis using the Robetta server. Nucleic acids research, 2004. 32(suppl_2): p. W526-W531.
47. Heo, L., H. Park, and C. Seok, GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic acids research, 2013. 41(W1): p. W384-W388.
48. Dym, O., D. Eisenberg, and T. Yeates, ERRAT. 2012.
49. Laskowski, R., M. MacArthur, and J. Thornton, PROCHECK: validation of protein-structure coordinates. 2006.
50. 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.
51. Burley, S.K., et al., Protein Data Bank (PDB): the single global macromolecular structure archive. Protein Crystallography, 2017: p. 627-641.
52. Kozakov, D., et al., The ClusPro web server for protein–protein docking. Nature protocols, 2017. 12(2): p. 255-278.
53. 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, 2019. 47(W1): p. W322-W330.
54. Laskowski, R.A., PDBsum: summaries and analyses of PDB structures. Nucleic acids research, 2001. 29(1): p. 221-222.
55. Pikkemaat, M.G., et al., Molecular dynamics simulations as a tool for improving protein stability. Protein Engineering, Design and Selection, 2002. 15(3): p. 185-192.
56. Van Der Spoel, D., et al., GROMACS: fast, flexible, and free. Journal of computational chemistry, 2005. 26(16): p. 1701-1718.
57. Vanommeslaeghe, K., et al., CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields. Journal of computational chemistry, 2010. 31(4): p. 671-690.
58. Berendsen, H.J., et al., Molecular dynamics with coupling to an external bath. The Journal of chemical physics, 1984. 81(8): p. 3684-3690.
59. Rapin, N., et al., Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PloS one, 2010. 5(4): p. e9862.
60. Faiza, M., et al., In silico multi-epitope vaccine against covid19 showing effective interaction with HLA-B* 15: 03. bioRxiv, 2020.
61. Grote, A., et al., JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic acids research, 2005. 33(suppl_2): p. W526-W531.
62. María, R., et al., The impact of bioinformatics on vaccine design and development. Vaccines, 2017. 2: p. 3-6.
63. Yasmin, T., et al., In silico proposition to predict cluster of B-and T-cell epitopes for the usefulness of vaccine design from invasive, virulent and membrane associated proteins of C. jejuni. In silico pharmacology, 2016. 4(1): p. 1-10.
64. Galluzzi, L., et al., Immunogenic cell death in cancer and infectious disease. Nature Reviews Immunology, 2017. 17(2): p. 97-111.
65. Galanis, K.A., et al., Linear B-cell epitope prediction for in silico vaccine design: a performance review of methods available via command-line interface. International journal of molecular sciences, 2021. 22(6): p. 3210.
66. Lu, L.L., et al., Beyond binding: antibody effector functions in infectious diseases. Nature Reviews Immunology, 2018. 18(1): p. 46-61.
67. Brusic, V., V.B. Bajic, and N. Petrovsky, Computational methods for prediction of T-cell epitopes—a framework for modelling, testing, and applications. Methods, 2004. 34(4): p. 436-443.
68. Lim, H.X., et al., Development of multi-epitope peptide-based vaccines against SARS-CoV-2. Biomedical Journal, 2021. 44(1): p. 18-30.
69. Rahmani, A., et al., Development of a conserved chimeric vaccine based on helper T-cell and CTL epitopes for induction of strong immune response against Schistosoma mansoni using immunoinformatics approaches. International journal of biological macromolecules, 2019. 141: p. 125-136.
70. Yadav, S., et al., Design of a multi-epitope subunit vaccine for immune-protection against Leishmania parasite. Pathogens and global health, 2020. 114(8): p. 471-481.
71. Meza, B., et al., A novel design of a multi-antigenic, multistage and multi-epitope vaccine against Helicobacter pylori: an in silico approach. Infection, Genetics and Evolution, 2017. 49: p. 309-317.
72. Khatoon, N., R.K. Pandey, and V.K. Prajapati, Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach. Scientific reports, 2017. 7(1): p. 1-12.
73. Sela-Culang, I., V. Kunik, and Y. Ofran, The structural basis of antibody-antigen recognition. Frontiers in immunology, 2013. 4: p. 302.
74. Ahmad, S.S., et al., Study of Caspase 8 inhibition for the management of Alzheimer’s disease: a molecular docking and dynamics simulation. Molecules, 2020. 25(9): p. 2071.
75. Choi, J.H., K.C. Keum, and S.Y. Lee, Production of recombinant proteins by high cell density culture of Escherichia coli. Chemical engineering science, 2006. 61(3): p. 876-885.