|
1. World Health Organization (WHO). Breast cancer fact sheet. Geneva: World Health Organization; 2023. Available from: https://www.who.int/news-room/fact-sheets/detail/breast-cancer 2. Parvizpour S, Razmara J, Omidi Y. Breast cancer vaccination comes to age: impacts of bioinformatics. Bioimpacts. 2018; 8(3):223-35. [ DOI:10.15171/bi.2018.25] [ PMID] [ ] 3. Burke EE, Srinivasan R, Wang J, Czerniecki BJ. Vaccine therapies for breast cancer. Surg Oncol Clin. 2019; 28(3):353-67. [ DOI:10.1016/j.soc.2019.02.004] [ PMID] 4. Tafreshi NK, Enkemann SA, Bui MM, Lloyd MC, Abrahams D, Huynh AS, et al. A mammaglobin-A targeting agent for noninvasive detection of breast cancer metastasis in lymph nodes. Cancer Res. 2011; 71(3):1050-9. [ DOI:10.1158/0008-5472.CAN-10-3091] [ PMID] [ ] 5. Kim SW, Goedegebuure P, Gillanders WE. Mammaglobin-A is a target for breast cancer vaccination. Oncoimmunology. 2016; 5(2):e1069940. [ DOI:10.1080/2162402X.2015.1069940] [ PMID] [ ] 6. Jaini R, Loya MG, Eng C. Immunotherapeutic target expression on breast tumors can be amplified by hormone receptor antagonism: a novel strategy for enhancing efficacy of targeted immunotherapy. Oncotarget. 2017; 8(20):32536-49. [ DOI:10.18632/oncotarget.15812] [ PMID] [ ] 7. Bastola R, Noh M, Kwon YJ. Vaccine adjuvants: smart components to boost the immune system. Arch Pharm Res. 2017; 40(11):1238-48. [ DOI:10.1007/s12272-017-0969-z] [ PMID] 8. Nooraei S, Bahrulolum H, Hoseini ZS, Katalani C, Hajizade A, Easton AJ, et al. Virus-like particles: preparation, immunogenicity and their roles as nanovaccines and drug nanocarriers. J Nanobiotechnology. 2021; 19(1):59. [ DOI:10.1186/s12951-021-00806-7] [ PMID] [ ] 9. Mohsen MO, Zha L, Cabral-Miranda G, Bachmann MF. Major findings and recent advances in virus-like particle (VLP)-based vaccines. Semin Immunol. 2017; 34:123-32. [ DOI:10.1016/j.smim.2017.08.014] [ PMID] 10. Fu Y, Li J. A novel delivery platform based on bacteriophage MS2 virus-like particles. Virus Res. 2016; 211:9-16. [ DOI:10.1016/j.virusres.2015.08.022] [ PMID] [ ] 11. Zhou P, Shi X, Xia J, Hu H. In silico design and in vitro validation of a multi-epitope peptide vaccine targeting triple-negative breast cancer. Front Oncol. 2025; 15:1611991. [ DOI:10.3389/fonc.2025.1611991] [ PMID] [ ] 12. Dhanushkumar T, Kamaraj B, Vasudevan K, Gopikrishnan M, Dasegowda KR, Rambabu M. Structural immunoinformatics approach for rational design of a multi-epitope vaccine against triple negative breast cancer. Int J Biol Macromol. 2023; 243:125209. [ DOI:10.1016/j.ijbiomac.2023.125209] [ PMID] 13. Ning W, Yan S, Song Y, Xu H, Zhang J, Wang X. Virus-like particle: a nano-platform that delivers cancer antigens to elicit an anti-tumor immune response. Front Immunol. 2025; 15:1504124. [ DOI:10.3389/fimmu.2024.1504124] [ PMID] [ ] 14. Naskalska A, Heddle JG. Virus-like particles derived from bacteriophage MS2 as antigen scaffolds and RNA protective shells. Nanomedicine. 2024; 19(12):1103-15. [ DOI:10.2217/nnm-2023-0362] [ PMID] [ ] 15. Shuaib M, Singh AK, Gupta S, Alasmari AF, Alqahtani F, Kumar S. Designing of neoepitopes based vaccine against breast cancer using integrated immuno and bioinformatics approach. J Biomol Struct Dyn. 2024; 42(16):8624-37. [ DOI:10.1080/07391102.2023.2247081] [ PMID] 16. UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021; 49(D1):D480-9. 17. Almagro Armenteros JJ, Tsirigos KD, Sønderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol. 2019; 37(4):420-3. [ DOI:10.1038/s41587-019-0036-z] [ PMID] 18. Gonzalez-Galarza FF, McCabe A, Santos EJ, Jones J, Takeshita L, Ortega-Rivera ND, et al. Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools. Nucleic Acids Res. 2020; 48(D1):D783-8. [ DOI:10.1093/nar/gkz1029] [ PMID] [ ] 19. Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, et al. Immune epitope database analysis resource. Nucleic Acids Res. 2012; 40(W1):W525-30. [ DOI:10.1093/nar/gks438] [ PMID] [ ] 20. Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M. Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data. J Proteome Res. 2020; 19(6):2304-15. [ DOI:10.1021/acs.jproteome.9b00874] [ PMID] 21. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007; 8:4. [ DOI:10.1186/1471-2105-8-4] [ PMID] [ ] 22. Dimitrov I, Flower DR, Doytchinova I. AllerTOP--a server for in silico prediction of allergens. BMC Bioinformatics. 2013; 14(Suppl 6):S4. [ DOI:10.1186/1471-2105-14-S6-S4] [ PMID] [ ] 23. Lamiable A, Thévenet P, Rey J, Vavrusa M, Derreumaux P, Tufféry P. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Res. 2016; 44(W1):W449-54. [ DOI:10.1093/nar/gkw329] [ PMID] [ ] 24. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleic Acids Res. 2000; 28(1):235-42. [ DOI:10.1093/nar/28.1.235] [ PMID] [ ] 25. Burley SK, Bhikadiya C, Bi C, Bittrich S, Chen L, Crichlow GV, et al. RCSB protein data bank: powerful new tools for exploring 3D structures of biological macromolecules. Nucleic Acids Res. 2021;49(D1):D437-51. [ DOI:10.1093/nar/gkaa1038] [ PMID] [ ] 26. Zhou P, Jin B, Li H, Huang SY. HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm. Nucleic Acids Res. 2018; 46(W1):W443-50. [ DOI:10.1093/nar/gky357] [ PMID] [ ] 27. Dhanda SK, Vir P, Raghava GPS. Designing of interferon-gamma inducing MHC class-II binders. Biol Direct. 2013; 8(1):30. [ DOI:10.1186/1745-6150-8-30] [ PMID] [ ] 28. Reddy Chichili VP, Kumar V, Sivaraman J. Linkers in the structural biology of protein-protein interactions. Protein Sci. 2013; 22(2):153-67. [ DOI:10.1002/pro.2206] [ PMID] [ ] 29. Zakeri B, Fierer JO, Celik E, Chittock EC, Schwarz-Linek U, Moy VT, et al. Peptide tag forming a rapid covalent bond to a protein, through engineering a bacterial adhesin. Proc Natl Acad Sci U S A. 2012; 109(12):E690-7. [ DOI:10.1073/pnas.1115485109] [ PMID] [ ] 30. Brune KD, Howarth M. New routes and opportunities for modular construction of particulate vaccines: stick, click, and glue. Front Immunol. 2018; 9:1432. [ DOI:10.3389/fimmu.2018.01432] [ PMID] [ ] 31. Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, Appel RD, et al. Protein identification and analysis tools in the ExPASy server. Methods Mol Biol. 1999; 112:531-52. [ DOI:10.1385/1-59259-584-7:531] [ PMID] 32. Magnan CN, Baldi P. SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics. 2014; 30(18):2592-7. [ DOI:10.1093/bioinformatics/btu352] [ PMID] [ ] 33. Hebditch M, Carballo-Amador MA, Charonis S, Curtis R, Warwicker J.. Protein-Sol: a web tool for predicting protein solubility from sequence. Bioinformatics. 2017; 33(19):3098-100. [ DOI:10.1093/bioinformatics/btx345] [ PMID] [ ] 34. Magnan CN, Randall A, Baldi P. SOLpro: accurate sequence-based prediction of protein solubility. Bioinformatics. 2009; 25(17):2200-7. [ DOI:10.1093/bioinformatics/btp386] [ PMID] 35. Chen J, Liu H, Yang J, Chou KC. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids. 2007; 33(3):423-8. [ DOI:10.1007/s00726-006-0485-9] [ PMID] 36. El-Manzalawy Y, Dobbs D, Honavar V. Predicting linear B-cell epitopes using string kernels. J Mol Recognit. 2008; 21(4):243-55. [ DOI:10.1002/jmr.893] [ PMID] [ ] 37. El-Manzalawy Y, Dobbs D, Honavar V. Predicting flexible length linear B-cell epitopes. Comput Syst Bioinformatics Conf. 2008; 7:121-32. [ DOI:10.1142/9781848162648_0011] 38. Ansari HR, Raghava GP. Identification of conformational B-cell epitopes in an antigen from its primary sequence. Immunome Res. 2010; 6:6. [ DOI:10.1186/1745-7580-6-6] [ PMID] [ ] 39. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10(6):845-58. [ DOI:10.1038/nprot.2015.053] [ PMID] [ ] 40. Feig M. Local protein structure refinement via molecular dynamics simulations with locPREFMD. J Chem Inf Model. 2016; 56(7):1304-12. [ DOI:10.1021/acs.jcim.6b00222] [ PMID] [ ] 41. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007; 35(suppl_2):W407-10. [ DOI:10.1093/nar/gkm290] [ PMID] [ ] 42. Bhattacharya A, Tejero R, Montelione GT. Evaluating protein structures determined by structural genomics consortia. Proteins. 2007; 66(4):778-95. [ DOI:10.1002/prot.21165] [ PMID] 43. Parkin J, Cohen B. An overview of the immune system. Lancet. 2001; 357(9270): 1777-89. [ DOI:10.1016/S0140-6736(00)04904-7] [ PMID] 44. Tan TJ, Sng JH, Chua CL. What is the role of immunotherapy in breast cancer? Chin Clin Oncol. 2018; 7(2):13. [ DOI:10.21037/cco.2018.04.01] [ PMID] 45. Toh U, Sakurai S, Saku S, Takao Y, Okabe M, Iwakuma N, et al. Early phase II study of mixed 19-peptide vaccine monotherapy for refractory triple-negative breast cancer. Cancer Sci. 2020; 111(8):2760-9. [ DOI:10.1111/cas.14510] [ PMID] [ ] 46. Nezafat N, Ghasemi Y, Javadi G, Khoshnoud MJ, Omidinia E. A novel multi-epitope peptide vaccine against cancer: an in silico approach. J Theor Biol. 2014; 349:121-34. [ DOI:10.1016/j.jtbi.2014.01.018] [ PMID] 47. Farafonov VS, Nerukh D. MS2 bacteriophage capsid studied using all-atom molecular dynamics. Interface Focus. 2019; 9(3):20180081. [ DOI:10.1098/rsfs.2018.0081] [ PMID] [ ] 48. Hashemi K, Ghahramani Seno MM, Ahmadian MR, Malaekeh-Nikouei B, Bassami MR, Dehghani H, et al. Optimizing the synthesis and purification of MS2 virus-like particles. Sci Rep. 2021; 11(1):19851. [ DOI:10.1038/s41598-021-98706-1] [ PMID] [ ] 49. Zalewska-Piatek B, Piatek R. Bacteriophages as potential tools for use in antimicrobial therapy and vaccine development. Pharmaceuticals. 2021; 14(4):331. [ DOI:10.3390/ph14040331] [ PMID] [ ] 50. Hu H, Steinmetz NF. Development of a virus-like particle-based anti-HER2 breast cancer vaccine. Cancers. 2021; 13(12):2907. [ DOI:10.3390/cancers13122909] [ PMID] [ ] 51. Mahmoodi S, Nezafat N, Barzegar A, Negahdaripour M, Nikanfar A, Zarghami N, et al. Harnessing bioinformatics for designing a novel multiepitope peptide vaccine against breast cancer. Curr Pharm Biotechnol. 2016; 17(12):1100-14. [ DOI:10.2174/1389201017666160914191106] [ PMID] 52. Dong R, Chu Z, Yu F, Zha Y. Contriving multi-epitope subunit of vaccine for COVID-19: immunoinformatics approaches. Front Immunol. 2020; 11:1784. [ DOI:10.3389/fimmu.2020.01784] [ PMID] [ ] 53. Goldberg AC, Rizzo LV. MHC structure and function - antigen presentation. Part 2. Einstein (Sao Paulo). 2015;13(1):157-62. [ DOI:10.1590/S1679-45082015RB3123] [ PMID] [ ] 54. Alspach E, Lussier DM, Schreiber RD. Interferon γ and its important roles in promoting and inhibiting spontaneous and therapeutic cancer immunity. Cold Spring Harb Perspect Biol. 2019; 11(3):a028480. [ DOI:10.1101/cshperspect.a028480] [ PMID] [ ] 55. Mukherjee R, Verma N, Bhagat A, Verma CK. Immunoinformatics: expanding frontiers and emerging tools in bioinformatics for immunology. Indian J Physiol Pharmacol. 2025:1-8. [ DOI:10.25259/IJPP_379_2024]
|