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中国生物工程杂志

CHINA BIOTECHNOLOGY
中国生物工程杂志  2023, Vol. 43 Issue (7): 88-100    DOI: 10.13523/j.cb.2301022
综述     
免疫信息学在表位疫苗研发中的应用与研究进展*
冯宏盛1,2,金行1,2,高永宇3,鲜钰涵1,2,李海洋1,2,杨思宇1,2,贾爱明4,**(),高凤山1,2,**()
1 大连大学生命健康学院 大连 116622
2 大连市基因和蛋白质工程药物筛选及研发重点实验室 大连 116622);(3 吉林农业大学动物医学院 长春 130118
3 大连医科大学附属第二医院 大连 116027
Application of Immunoinformatics in Epitope Vaccine Development
Hong-sheng FENG1,2,Hang JIN1,2,Yong-yu GAO3,Yu-han XIAN1,2,Hai-yang LI1,2,Si-yu YANG1,2,Ai-ming JIA4,**(),Feng-shan GAO1,2,**()
1 College of Life and Health, Dalian University, Dalian 116622, China
2 The Dalian Gene and Protein Engineering for Drug Screening Key Laboratory, Dalian 116622, China
3 College of Veterinary Medicine, Jilin Agricultural University, Changchun 130118, China
4 The Second Hospital of Dalian Medical University, Dalian 116027, China
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摘要:

对于表位疫苗的设计,通常是利用免疫信息学工具和相关技术方法对已获取或已知的核苷酸序列和氨基酸序列进行分析,以此确定和预筛选出可能的优势表位,之后通过人工合成或基因工程技术等制备具有优势表位的多肽疫苗。免疫信息学的快速发展使其被成功应用于疫苗学领域,成为开发基于表位多肽疫苗最有效的方法。从免疫信息学在表位疫苗设计中的一般流程及疫苗验证、表位疫苗研发涉及的免疫信息学工具以及免疫信息学在表位疫苗设计中的具体应用等方面进行综述,以期为合理设计和研发有效的表位疫苗提供参考。

关键词: 免疫信息学表位多肽表位疫苗    
Abstract:

Presently, to design epitope vaccines, the research and development process is generally to use computer-aided immunoinformatics tools and related technical methods to analyze acquired or known nucleotide and amino acid sequences to determine and pre-screen out possible dominant epitopes, and then prepare polypeptide vaccines with dominant epitopes through synthetic or genetic engineering techniques. The rapid development of immunoinformatics has been successfully applied to the field of vaccinology, and the immunoinformatics method is the most effective method to develop vaccines based on epitope polypeptide. Immunoinformatics, the general process of immunoinformatics in epitope vaccine design and validation, the immunoinformatics tools involved in the design of epitope vaccines, and the specific application of immunoinformatics in the design of epitope vaccines are reviewed, which will provide reference for reasonable design and development of effective epitope vaccines.

Key words: Immunoinformatics    Epitope peptides    Epitope vaccines
收稿日期: 2023-01-15 出版日期: 2023-08-03
ZTFLH:  Q816  
基金资助: 国家自然科学基金(32273022)
通讯作者: **电子信箱:jiaam200310@163.com;gfsh0626@126.com   
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冯宏盛
金行
高永宇
鲜钰涵
李海洋
杨思宇
贾爱明
高凤山

引用本文:

冯宏盛, 金行, 高永宇, 鲜钰涵, 李海洋, 杨思宇, 贾爱明, 高凤山. 免疫信息学在表位疫苗研发中的应用与研究进展*[J]. 中国生物工程杂志, 2023, 43(7): 88-100.

Hong-sheng FENG, Hang JIN, Yong-yu GAO, Yu-han XIAN, Hai-yang LI, Si-yu YANG, Ai-ming JIA, Feng-shan GAO. Application of Immunoinformatics in Epitope Vaccine Development. China Biotechnology, 2023, 43(7): 88-100.

链接本文:

https://manu60.magtech.com.cn/biotech/CN/10.13523/j.cb.2301022        https://manu60.magtech.com.cn/biotech/CN/Y2023/V43/I7/88

图1  利用免疫信息学设计表位疫苗的基本流程
验证方法 验证内容
酶联免疫吸附测定(enzyme-linked immunosorbent assay,ELISA) 免疫模型体内抗体滴度测定;细胞免疫原性检测,如IgA、IgG、IgE等抗体检测
酶联免疫斑点技术(enzyme-linked immunospot assay,ELISPOT) 评估抗原特异的T细胞功效,即IFN-γ释放检测
聚丙烯酰胺凝胶电泳(sodium dodecyl sulfate polyacrylamide gel electrophoresis,SDS-PAGE) 抗体纯度鉴定
MTT比色法 细胞增殖检测,如脾淋巴细胞增殖测定
病毒TCID50测定 免疫动物目的器官内的病毒滴度测定
组织病理学评估 分析免疫模型受病原攻击后其靶器官的病理损伤
表1  疫苗研发后常用的验证方法
工具 网址 功能(MHC类型)
Propred-1 http://www.imtech.res.in/raghava/propred1/ I
Propred http://www.imtech.res.in/raghava/propred/ II
IEDB-MHCI http://tools.immuneepitope.org/mhci/ I
IEDB-MHCII http://tools.immuneepitope.org/mhcii/ II
NetMHC http://www.cbs.dtu.dk/services/NetMHC/ I
NetMHCII http://www.cbs.dtu.dk/services/NetMHCII/ II
NetMHCpan http://www.cbs.dtu.dk/services/NetMHCpan/ I
NetMHCIIpan http://www.cbs.dtu.dk/services/NetMHCIIpan/ II
SVMHC http://abi.inf.uni-tuebingen.de/Services/SVMHC/ I and II
SVRMHC http://us.accurascience.com/SVRMHCdb/ I and II
EPISOPT http://bio.med.ucm.es/episopt.html I
EpiTOP http://www.pharmfac.net/EpiTOP II
EpiJen http://www.ddg-pharmfac.net/epijen/EpiJen/EpiJen.htm I
Vaxign http://www.violinet.org/vaxign/ I and II
MHCPred http://www.ddg-pharmfac.net/mhcpred/MHCPred/ I and II
表2  T细胞表位预测的部分在线使用工具
工具 网址 特点
ABCPred[8] http://www.imtech.res.in/raghava/abcpred/ 使用人工神经网络预测
BCPred[9] http://ailab.ist.psu.edu/bcpred/ 使用亚序列核预测
ElliPro[10] http://tools.iedb.org/ellipro/ 基于蛋白质结构的几何特性运行
DiscoTope[11] http://www.cbs.dtu.dk/services/DiscoTope-2.0 获取蛋白质三维结构数据,通过表面测量进行预测
PEPITO[12] http://pepito.proteomics.ics.uci.edu/ 使用氨基酸倾向得分和多距离的半球形暴露值组合预测
SEPPA[13] http://lifecenter.sgst.cn/seppa/ 利用残基和相邻残基的空间紧凑性预测
EPSVR[14] http://sysbio.unl.edu/EPSVR/ 预测涉及向量回归方法
DRREP[15] https://github.com/gsher1/DRREP 泛化能力在所有测试数据集中稳定且具有不同水平的表位密度
PEPITOPE[16] http://pepitope.tau.ac.il/ 基于一组针对目标单克隆抗体亲和选择的肽进行预测
表3  B细胞表位预测的部分在线使用工具
工具 网址 功能
VaxiJen v.2.0[18] http://ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html 蛋白/表位抗原性鉴定
AllerTOP v.2.0[19] http://ddg-pharmfac.net/AllerTOP/ 致敏性分析
ProtParam[20] http://expasy.org/tools/protparam.html 蛋白理化性质分析
ToxinPred[21] http://crdd.osdd.net/raghava/toxinpred/ 毒性分析
Innovagen https://pepcalc.com/peptide-solubility-calculator.php 蛋白水溶性分析
Protein Sol https://protein-sol.manchester.ac.uk/ 疫苗溶解度分析
Prosa Web https://prosa.services.came.sbg.ac.at/prosa.php 疫苗Z-score分析
NCBI BLAST https://blast.ncbi.nlm.nih.gov/Blast.cgi 宿主同源性分析
表4  表位分析的部分在线使用工具
工具 网址 功能
SOPMA[22] https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=npsa_sopma.html 二级结构分析与预测
PSIPRED 4.0[23] http://bioinf.cs.ucl.ac.uk/psipred/ 预测二级结构、跨膜拓扑结构、跨膜螺旋、折叠和结构域识别
AlphaFold2[24] https://alphafold.ebi.ac.uk/ 预测蛋白质的3D结构
PEP-FOLD3[25] http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3 线性肽从头结构3D预测
I-TASSER[26] http://zhanglab.ccmb.med.umich.edu/I-TASSER/ 蛋白质3D结构预测的最佳服务器
RaptorX[27] http://raptorx.uchicago.edu/ 预测蛋白质的3D结构
3Drefine[28] http://sysbio.rnet.missouri.edu/3Drefine/ 完善蛋白质的3D结构
GalaxyRefine[29] http://galaxy.seoklab.org 完善蛋白质的3D结构
RAMPage [30] http://mordred.bioc.cam.ac.uk/~rapper/rampage.php 分析预测模型的拉马钱德兰图,验证预测模型的质量和准确性
Pymol[31] https://pymol.org/ 用于进行可视化的3D结构建模
SignalP 4.1[32] https://services.healthtech.dtu.dk/service.php?SignalP-4.1 发现疫苗中的任何潜在信号肽
TMHMM 2.0[33] https://services.healthtech.dtu.dk/service.php?TMHMM-2.0 搜索最终疫苗构建体中潜在的跨膜螺旋
表5  疫苗构建体的结构预测在线使用工具
工具 网址 功能
UCSF ChimeraX[34] https://www.rbvi.ucsf.edu/chimerax 分子结构制备
ClusPro 2.0[35] https://cluspro.org/help.php 对接目的模拟
HADDOCK 2.4[36] https://wenmr.science.uu.nl/haddock2.4/ 对接目的模拟
PDBePISA https://www.ebi.ac.uk/msd-srv/prot_int/pistart.html 分析结合能、界面面积和氢键
PDBsum http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/ 分析结合能、界面面积和氢键
表6  分子对接在线使用工具
图2  针对DENV的多表位肽疫苗的设计流程
图3  针对布鲁氏菌的多表位候选疫苗的设计流程
图4  针对弓形虫感染的多表位候选疫苗的设计流程
图5  新型PD-1 B细胞肽表位疫苗的设计流程
图6  基于Amb a 11的多表位疫苗设计流程
[1] Li W D, Joshi M D, Singhania S, et al. Peptide vaccine: progress and challenges. Vaccines, 2014, 2(3): 515-536.
doi: 10.3390/vaccines2030515 pmid: 26344743
[2] Oli A N, Obialor W O, Ifeanyichukwu M O, et al. Immunoinformatics and vaccine development: an overview. Immuno Targets and Therapy, 2020, 9: 13-30.
[3] Parvizpour S, Razmara J, Omidi Y. Breast cancer vaccination comes to age: impacts of bioinformatics. BioImpacts: BI, 2018, 8(3): 223-235.
[4] Brusic V, Petrovsky N. Immunoinformatics: the new kid in town. Novartis Foundation Symposium, 2003, 254: 3-13, discussion13-22, 98-101, 250-252.
[5] 王月丹, 陈慰峰. 免疫信息学. 自然科学进展, 2004, 14(10): 2-6.
Wang Y D, Chen W F. Immunoinformatics. Progress of Natural Science, 2004, 14(10): 2-6.
[6] Ahmed R K S, Maeurer M J. T-cell epitope mapping. Epitope Mapping Protocols, 2009, 524: 427-438.
[7] Zhang J, Zhao X W, Sun P P, et al. Conformational B-cell epitopes prediction from sequences using cost-sensitive ensemble classifiers and spatial clustering. BioMed Research International, 2014, 2014: 689219.
[8] Saha S, Raghava G P S. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins, 2006, 65(1): 40-48.
doi: 10.1002/prot.21078
[9] EL-Manzalawy Y, Dobbs D, Honavar V. Predicting linear B-cell epitopes using string kernels. Journal of Molecular Recognition, 2008, 21(4): 243-255.
doi: 10.1002/jmr.v21:4
[10] Ponomarenko J, Bui H H, Li W, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics, 2008, 9: 514.
doi: 10.1186/1471-2105-9-514 pmid: 19055730
[11] Kringelum J V, Lundegaard C, Lund O, et al. Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Computational Biology, 2012, 8(12): e1002829.
doi: 10.1371/journal.pcbi.1002829
[12] Sweredoski M J, Baldi P. PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure. Bioinformatics, 2008, 24(12): 1459-1460.
doi: 10.1093/bioinformatics/btn199 pmid: 18443018
[13] Sun J, Wu D, Xu T L, et al. SEPPA: a computational server for spatial epitope prediction of protein antigens. Nucleic Acids Research, 2009, 37(Web Server issue): W612-W616.
[14] Liang S D, Zheng D D, Standley D, et al. EPSVR and EPMeta: prediction of antigenic epitopes using support vector regression and multiple server results. BMC Bioinformatics, 2010, 11: 381.
doi: 10.1186/1471-2105-11-381 pmid: 20637083
[15] Sher G, Zhi D G, Zhang S J. DRREP: deep ridge regressed epitope predictor. BMC Genomics, 2017, 18(Suppl 6): 676.
doi: 10.1186/s12864-017-4024-8 pmid: 28984193
[16] Mayrose I, Penn O, Erez E, et al. Pepitope: epitope mapping from affinity-selected peptides. Bioinformatics, 2007, 23(23): 3244-3246.
doi: 10.1093/bioinformatics/btm493 pmid: 17977889
[17] Shashkova T I, Umerenkov D, Salnikov M, et al. SEMA: antigen B-cell conformational epitope prediction using deep transfer learning. Frontiers in Immunology, 2022, 13: 960985.
doi: 10.3389/fimmu.2022.960985
[18] Doytchinova I A, Flower D R. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics, 2007, 8: 4.
pmid: 17207271
[19] Dimitrov I, Bangov I, Flower D R, et al. AllerTOP v.2-a server for in silico prediction of allergens. Journal of Molecular Modeling, 2014, 20(6): 2278.
doi: 10.1007/s00894-014-2278-5 pmid: 24878803
[20] Gasteiger E, Hoogland C, Gattiker A, et al. Protein ide. pngication and analysis tools on the ExPASy server. The Proteomics Protocols Handbook. Totowa: Humana Press, 2005: 571-607.
[21] Gupta S, Kapoor P, Chaudhary K, et al. In silico approach for predicting toxicity of peptides and proteins. PLoS One, 2013, 8(9): e73957.
doi: 10.1371/journal.pone.0073957
[22] Geourjon C, Deléage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics, 1995, 11(6): 681-684.
doi: 10.1093/bioinformatics/11.6.681
[23] McGuffin L J, Bryson K, Jones D T. The PSIPRED protein structure prediction server. Bioinformatics, 2000, 16(4): 404-405.
doi: 10.1093/bioinformatics/16.4.404 pmid: 10869041
[24] Senior A W, Evans R, Jumper J, et al. Improved protein structure prediction using potentials from deep learning. Nature, 2020, 577(7792): 706-710.
doi: 10.1038/s41586-019-1923-7
[25] Lamiable A, Thévenet P, Rey J, et al. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Research, 2016, 44(W1): W449-W454.
doi: 10.1093/nar/gkw329
[26] Bibi S, Ullah I, Zhu B D, et al. In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology. Scie.pngic Reports, 2021, 11: 1249.
[27] Källberg M, Wang H P, Wang S, et al. Template-based protein structure modeling using the RaptorX web server. Nature Protocols, 2012, 7(8): 1511-1522.
doi: 10.1038/nprot.2012.085 pmid: 22814390
[28] Bhattacharya D, Nowotny J, Cao R Z, et al. 3Drefine: an interactive web server for efficient protein structure refinement. Nucleic Acids Research, 2016, 44(W1): W406-W409.
doi: 10.1093/nar/gkw336
[29] Heo L, Park H, Seok C. GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Research, 2013, 41(W1): W384-W388.
doi: 10.1093/nar/gkt458
[30] Tahir Ul Qamar M, Shokat Z, Muneer I, et al. Multiepitope-based subunit vaccine design and evaluation against respiratory syncytial virus using reverse vaccinology approach. Vaccines (Basel), 2020, 8(2): 288.
[31] Ullah M A, Sarkar B, Islam S S. Exploiting the reverse vaccinology approach to design novel subunit vaccines against Ebola virus. Immunobiology, 2020, 225(3): 151949.
doi: 10.1016/j.imbio.2020.151949
[32] Nielsen H. Predicting secretory proteins with SignalP. Methods in Molecular Biology, 2017, 1611: 59-73.
doi: 10.1007/978-1-4939-7015-5_6 pmid: 28451972
[33] Krogh A, Larsson B, von Heijne G, et al. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. Journal of Molecular Biology, 2001, 305(3): 567-580.
doi: 10.1006/jmbi.2000.4315 pmid: 11152613
[34] Pettersen E F, Goddard T D, Huang C C, et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Science, 2021, 30(1): 70-82.
doi: 10.1002/pro.v30.1
[35] Kozakov D, Hall D R, Beglov D, et al. Achieving reliability and high accuracy in automated protein docking: ClusPro, PIPER, SDU, and stability analysis in CAPRI rounds 13-19. Proteins, 2010, 78(15): 3124-3130.
doi: 10.1002/prot.22835
[36] Ambrosetti F, Jandova Z, Bonvin A M J J. Information-driven antibody-antigen modelling with HADDOCK. Methods in Molecular Biology, 2023, 2552: 267-282.
doi: 10.1007/978-1-0716-2609-2_14 pmid: 36346597
[37] Sanches R C O, Tiwari S, Ferreira L C G, et al. Immunoinformatics design of multi-epitope peptide-based vaccine against Schistosoma mansoni using transmembrane proteins as a target. Frontiers in Immunology, 2021, 12: 621706.
doi: 10.3389/fimmu.2021.621706
[38] Grote A, Hiller K, Scheer M, et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Research, 2005, 33: W526-W531.
doi: 10.1093/nar/gki376 pmid: 15980527
[39] Vincze T, Posfai J, Roberts R J. NEBcutter: a program to cleave DNA with restriction enzymes. Nucleic Acids Research, 2003, 31(13): 3688-3691.
doi: 10.1093/nar/gkg526 pmid: 12824395
[40] Mushtaq M, Naz S, Parang K, et al. Exploiting dengue virus protease as a therapeutic target: current status, challenges and future avenues. Current Medicinal Chemistry, 2021, 28(37): 7767-7802.
doi: 10.2174/0929867328666210629152929 pmid: 34212826
[41] Kaushik V, Sunil Krishnan G, Gupta L R, et al. Immunoinformatics aided design and in-vivo validation of a cross-reactive peptide based multi-epitope vaccine targeting multiple serotypes of dengue virus. Frontiers in Immunology, 2022, 13: 865180.
doi: 10.3389/fimmu.2022.865180
[42] Mohammadzadeh R, Soleimanpour S, Pishdadian A, et al. Designing and development of epitope-based vaccines against Helicobacter pylori. Critical Reviews in Microbiology, 2022, 48(4): 489-512.
doi: 10.1080/1040841X.2021.1979934
[43] Li M, Zhu Y J, Niu C, et al. Design of a multi-epitope vaccine candidate against Brucella melitensis. Scie.pngic Reports, 2022, 12(1): 10146.
[44] Foroutan M, Ghaffarifar F, Sharifi Z, et al. Vaccination with a novel multi-epitope ROP 8 DNA vaccine against acute Toxoplasma gondii infection induces strong B and T cell responses in mice. Comparative Immunology, Microbiology and Infectious Diseases, 2020, 69: 101413.
doi: 10.1016/j.cimid.2020.101413
[45] Kaumaya P T P, Guo L L, Overholser J, et al. Immunogenicity and antitumor efficacy of a novel human PD-1 B-cell vaccine (PD1-Vaxx) and combination immunotherapy with dual trastuzumab/pertuzumab-like HER-2 B-cell epitope vaccines (B-Vaxx) in a syngeneic mouse model. Oncoimmunology, 2020, 9(1): 1818437.
doi: 10.1080/2162402X.2020.1818437
[46] Moten D, Kolchakova D, Todorov K, et al. Design of an epitope-based peptide vaccine against the major allergen amb a 11 using immunoinformatic approaches. The Protein Journal, 2022, 41(2): 315-326.
doi: 10.1007/s10930-022-10050-z
[47] Alonso-Padilla J, Lafuente E M, Reche P A. Computer-aided design of an epitope-based vaccine against Epstein-Barr virus. Journal of Immunology Research, 2017, 2017: 9363750.
[48] Ahmad T A, Eweida A E, Sheweita S A. B-cell epitope mapping for the design of vaccines and effective diagnostics. Trials in Vaccinology, 2016, 5: 71-83.
doi: 10.1016/j.trivac.2016.04.003
[49] Khan A M, Miotto O, Heiny A T, et al. A systematic bioinformatics approach for selection of epitope-based vaccine targets. Cellular Immunology, 2006, 244(2): 141-147.
doi: 10.1016/j.cellimm.2007.02.005 pmid: 17434154
[50] Hajissa K, Zakaria R, Suppian R, et al. Epitope-based vaccine as a universal vaccination strategy against Toxoplasma gondii infection: a mini-review. Journal of Advanced Veterinary and Animal Research, 2019, 6(2): 174-182.
doi: 10.5455/javar.2019.f329 pmid: 31453188
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