免疫信息学预测在治疗蛋白研究中的应用与发展
Application and Development of Immunoinformatics Prediction in Therapeutic Protein Research
DOI: 10.12677/PI.2023.123022, PDF,   
作者: 张林涵, 陈建华*：中国药科大学生命科学技术学院，江苏 南京
关键词: 治疗蛋白；免疫原性；免疫信息学；Therapeutic Protein； Immunogenicity； Immunoinformatics
摘要: 治疗性蛋白存在的免疫原性问题，能够潜在地引起抗药物抗体的产生或细胞介导的免疫反应，因此免疫原性评价是治疗蛋白开发过程中至关重要的一步。由于免疫系统的复杂性，直接的实验方法是昂贵且耗时的，并且实验结果需要反复认证。目前国内外存在多种免疫信息学工具，可针对治疗蛋白的B细胞表位以及T细胞表位进行预测。利用多种免疫信息学工具进行治疗蛋白免疫原性研究，能够预测出治疗蛋白的高免疫原性位点并对其进行改造，降低免疫原性。免疫信息学的应用避免了传统免疫学验证的缺点，为蛋白药物免疫原性的探究提供了新思路。
Abstract: Therapeutic proteins present immunogenicity issues that can potentially cause the production of anti-drug antibodies or cell-mediated immune responses, making immunogenicity evaluation a critical step in the development of therapeutic proteins. Due to the complexity of the immune system, direct experimental methods are expensive and time-consuming, and the results need to be repeatedly certified. Various immunoinformatics tools exist both domestically and internationally to predict B-cell epitopes as well as T-cell epitopes for therapeutic proteins. Immunogenicity studies of therapeutic proteins using multiple immunoinformatics tools can predict highly immunogenic sites of therapeutic proteins and modify them to reduce immunogenicity. The application of immunoinformatics avoids the drawbacks of traditional immunological validation and provides new ideas for the exploration of protein drug immunogenicity.
文章引用：张林涵, 陈建华. 免疫信息学预测在治疗蛋白研究中的应用与发展[J]. 药物资讯, 2023, 12(3): 184-189. https://doi.org/10.12677/PI.2023.123022

参考文献

[1] Bahrami, A.A., Payandeh, Z., Khalili, S., et al. (2019) Immunoinformatics: In Silico Approaches and Computational De-sign of a Multi-Epitope, Immunogenic Protein. International Reviews of Immunology, 38, 307-322. [Google Scholar] [CrossRef] [PubMed]
[2] Doneva, N., Doytchinova, I. and Dimitrov, I. (2021) Pre-dicting Immunogenicity Risk in Biopharmaceuticals. Symmetry-Basel, 13, Article No. 388. [Google Scholar] [CrossRef] 
[3] Faraji, F., Karjoo, Z., Moghaddam, M.V., et al. (2018) Challenges Re-lated to the Immunogenicity of Parenteral Recombinant Proteins: Underlying Mechanisms and New Approaches to Overcome It. International Reviews of Immunology, 37, 301-315. [Google Scholar] [CrossRef] [PubMed]
[4] Treanor, B. (2012) B-Cell Receptor: From Resting State to Activate. Immunology, 136, 21-27. [Google Scholar] [CrossRef] [PubMed]
[5] Saha, S. and Raghava, G.P.S. (2004) BcePred: Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-Chemical Properties. In: Nicosia, G., Cutello, V., Bentley, P.J., et al., Eds., Artificial Immune Systems, Proceedings, Springer, Berlin, 197-204. [Google Scholar] [CrossRef] 
[6] Saha, S. and Raghava, G.P.S. (2006) Prediction of Continuous B-Cell Epitopes in an Antigen Using Recurrent Neural Network. Proteins-Structure Function and Bioinformatics, 65, 40-48. [Google Scholar] [CrossRef] [PubMed]
[7] Andersen, P.H., Nielsen, M. and Lund, O. (2006) Prediction of Residues in Discontinuous B-Cell Epitopes Using Protein 3D Structures. Protein Science, 15, 2558-2567. [Google Scholar] [CrossRef] [PubMed]
[8] Ponomarenko, J., Bui, H.H., Li, W., et al. (2008) ElliPro: A New Structure-Based Tool for the Prediction of Antibody Epitopes. BMC Bioinformatics, 9, Article No. 514. [Google Scholar] [CrossRef] [PubMed]
[9] Reche, P.A., Glutting, J.P. and Reinherz, E.L. (2002) Prediction of MHC Class I Binding Peptides Using Profile Motifs. Human Immunology, 63, 701-709. [Google Scholar] [CrossRef] 
[10] Oyarzún, P., Ellis, J.J., Bodén, M., et al. (2013) PREDIVAC: CD4+ T-Cell Epitope Prediction for Vaccine Design That Covers 95% of HLA Class II DR Protein Diversity. BMC Bi-oinformatics, 14, Article No. 52. [Google Scholar] [CrossRef] [PubMed]
[11] Perez-Ruiz, F., Calabozo, M., Erauskin, G.G., et al. (2002) Renal Underexcretion of Uric Acid Is Present in Patients with Apparent High Urinary Uric Acid Output. Arthritis and Rheu-matism, 47, 610-613. [Google Scholar] [CrossRef] [PubMed]
[12] Maiuolo, J., Oppedisano, F., Gratteri, S., et al. (2016) Regulation of Uric Acid Metabolism and Excretion. International Journal of Cardiology, 213, 8-14. [Google Scholar] [CrossRef] [PubMed]
[13] Cortes, J., Moore, J.O., Maziarz, R.T., et al. (2010) Control of Plasma Uric Acid in Adults at Risk for Tumor Lysis Syndrome: Efficacy and Safety of Rasburicase Alone and Rasbu-ricase Followed by Allopurinol Compared with Allopurinol Alone-Results of a Multicenter Phase III Study. Journal of Clinical Oncology, 28, 4207-4213. [Google Scholar] [CrossRef] 
[14] Navolanic, P.M., Pui, C.H., Larson, R.A., et al. (2003) Elitek (TM)-Rasburicase: An Effective Means to Prevent and Treat Hyperuricemia Associated with Tumor Lysis Syndrome, a Meeting Report, Dallas, Texas, January 2002. Leukemia, 17, 499-514. [Google Scholar] [CrossRef] [PubMed]
[15] Shannon, J.A. and Cole, S.W. (2012) Pegloticase: A Novel Agent for Treatment-Refractory Gout. Annals of Pharmacotherapy, 46, 368-76. [Google Scholar] [CrossRef] 
[16] Lipsky, P.E., Calabrese, L.H., Kavanaugh, A., et al. (2014) Pegloticase Immunogenicity: The Relationship between Efficacy and Antibody Development in Patients Treated for Refractory Chronic Gout. Arthritis Research & Therapy, 16, R60. [Google Scholar] [CrossRef] [PubMed]
[17] Nelapati, A.K., Das, B.K., Ettiyappan, J.B.P., et al. (2020) In-Silico Epitope Identification and Design of Uricase Mutein with Reduced Im-munogenicity. Process Biochemistry, 92, 288-302. [Google Scholar] [CrossRef] 
[18] Tripathi, S., Parmar, J. and Kumar, A. (2020) Structure-Based Immunogenicity Prediction of Uricase from Fungal (Aspergillus flavus), Bacterial (Bacillus subtillis) and Mammalian Sources Using Immunoinformatic Approach. Protein Journal, 39, 133-144. [Google Scholar] [CrossRef] [PubMed]
[19] Labrou, N.E., Papageorgiou, A.C. and Avramis, V.I. (2010) Structure-Function Relationships and Clinical Applications of L-Asparaginases. Current Medicinal Chemistry, 17, 2183-2195. [Google Scholar] [CrossRef] [PubMed]
[20] Yen, H.J., Chang, W.H., Liu, H.C., et al. (2016) Outcomes Following Discontinuation of E. coli L-Asparaginase upon Severe Allergic Reactions in Children with Acute Lymphoblastic Leukemia. Pediatric Blood & Cancer, 63, 665-670. [Google Scholar] [CrossRef] [PubMed]
[21] Ramya, L.N. and Pulicherla, K.K. (2015) Studies on Deimmunization of Antileukaemic L-Asparaginase to Have Reduced Clinical Immunogenicity—An in Silico Approach. Pathology and On-cology Research, 21, 909-920. [Google Scholar] [CrossRef] [PubMed]
[22] Mohd Asri, N.A., Ahmad, S., Mohamud, R., et al. (2021) Global Prevalence of Nosocomial Multidrug-Resistant Klebsiella pneumoniae: A Systematic Review and Meta-Analysis. Antibi-otics (Basel, Switzerland), 10, Article No. 1508. [Google Scholar] [CrossRef] [PubMed]
[23] Rostamian, M., Farasat, A., Lorestani, R.C., et al. (2022) Im-munoinformatics and Molecular Dynamics Studies to Predict T-Cell-Specific Epitopes of Four Klebsiella pneumonia Fimbriae Antigens. Journal of Biomolecular Structure & Dynamics, 40, 166-176. [Google Scholar] [CrossRef] [PubMed]
[24] Neefjes, J., Jongsma, M.L.M., Paul, P., et al. (2011) To-wards a Systems Understanding of MHC Class I and MHC Class II Antigen Presentation. Nature Reviews Immunology, 11, 823-836. [Google Scholar] [CrossRef] [PubMed]