新型多肽表位技术研究进展

doi:10.16431/j.cnki.1671-7236.2017.01.006

摘要/Abstract

摘要：

多肽表位作为抗原的重要组成成分，可以被宿主免疫系统B细胞产生的抗体（Ig）和T细胞受体（TCR）识别，从而诱导机体产生免疫保护作用。传统的研究方法如交叠肽合成等费时费力，极大地限制了多肽表位的研究。随着生物信息学、高通量测序、CRISPR/Cas9、质谱（MS）等技术出现以及TAP非依赖型蛋白加工机制的不断阐述，使表位的研究得到不断完善。作者介绍了最新的多肽表位研究方法，包括利用计算机对B细胞和T细胞表位的预测、基于MHC与抗原肽结构作用为基础的方法、结合高通量测序技术的表位筛选方法、结合新型基因编辑技术的表位筛选方法、TAP非依赖性T细胞表位的最新研究等，并对今后多肽表位研究的发展进行了展望。

关键词: 多肽; 表位; 筛选; CRISPR/Cas9技术; TAP非依赖性表位

Abstract:

As an important component of the antigen, peptide epitopes can be recognized by T cell receptor (TCR) and antibodies which induced protective immunity by the host immune system. Traditional research methods, such as overlapping peptide synthesis is long time-consuming which greatly limits the study of epitopes. With the development of bioinformatics, high-throughput sequencing, CRISPR/Cas9 technology, MS and other technologies and explaining the processing mechanism of TAP-dificient protein constantly, the research for epitopes is being perfect continually. In this article, some new methods to study epitopes were introduced, including computer prediction of B cell or T cell epitopes, identification of epitopes based on the structure and interaction of MHC and antigenic peptides, screening epitopes by means of high throughput sequencing, screening epitopes by means of newly genome editing, the up-to-date research in TAP-deficient T cell epitopes, etc. Finally,future prospect for research direction of peptides was further forecasted.

Key words: peptide; epitope; screening; CRISPR/Cas9 technology; TAP-dificient epitopes

中图分类号:

S852.4⁺3

韩勇, 高花, 翟晓鑫, 高凤山. 新型多肽表位技术研究进展[J]. 《中国畜牧兽医》, 2017, 44(1): 44-52.

HAN Yong, GAO Hua, ZHAI Xiao-xin, GAO Feng-shan. Research Progress on Novel Peptide Epitopes Technology[J]. , 2017, 44(1): 44-52.

参考文献

[1] Peters B, Sidney J, Bourne P, et al. The design and implementation of the immune epitope database and analysis resource[J]. Immunogenetics, 2005, 57(5):326-336.
[2] Fruci D, Lauvau G, Saveanu L, et al. Quantifying recruitment of cytosolic peptides for HLA class Ⅰ presentation:Impact of TAP transport[J]. J Immunol, 2003, 170(6):2977-2984.
[3] He Y, Xiang Z, Mobley H L. Vaxign:The first web-based vaccine design program for reverse vaccinology and applications for vaccine development[J]. J Biomed Biotechnol, 2010, 7(1):297505-297520.
[4] Trim P J, Snel M F. Small molecule MALDI MS imaging:Current technologies and future challenges[J]. Methods, 2016, 16(1):e11.
[5] Vandemoortele G, Gevaert K, Eyckerman S. Proteomics in the genome engineering era[J]. Proteomics, 2016, 16(2):177-187.
[6] Armengol E, Wiesmuller K H, Wienhold D, et al. Identification of T-cell epitopes in the structural and non-structural proteins of classical swine fever virus[J]. J Gen Virol, 2002, 83(3):551-560.
[7] Larsen J E, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes[J]. Immunome Res, 2006, 2(1):2-9.
[8] Saha S, Raghava G P. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network[J]. Proteins, 2006, 65(1):40-48.
[9] Chen J, Liu H, Yang J, et al. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale[J]. Amino Acids, 2007, 33(3):423-428.
[10] El-Manzalawy Y, Dobbs D, Honavar V. Predicting linear B-cell epitopes using string kernels[J]. J Mol Recognit, 2008, 21(4):243-255.
[11] Kringelum J V, Lundegaard C, Lund O, et al. Reliable B cell epitope predictions:Impacts of method development and improved benchmarking[J]. PLoS Comput Biol, 2012, 8(12):e1002829.
[12] Lian Y, Ge M, Pan X M. EPMLR:Sequence-based linear B-cell epitope prediction method using multiple linear regression[J]. BMC Bioinformatics, 2014, 15(1):414-420.
[13] Lian Y, Huang Z C, Ge M, et al. An Improved method for predicting linear B-cell epitope using deep maxout networks[J]. Biomed Environ Sci, 2015, 28(6):460-463.
[14] Sun J, Wu D, Xu T, et al. SEPPA:A computational server for spatial epitope prediction of protein antigens[J]. Nucleic Acids Res, 2009, 37(4):612-616.
[15] Garcia-Prieto F F, Fdez Galvan I, Aguilar M A, et al. Study on the conformational equilibrium of the alanine dipeptide in water solution by using the averaged solvent electrostatic potential from molecular dynamics methodology[J]. J Chem Phys, 2011, 135(19):194502-194511.
[16] Brenke R, Hall D R, Chuang G Y, et al. Application of asymmetric statistical potentials to antibody-protein docking[J]. Bioinformatics, 2012, 28(20):2608-2614.
[17] Hu Y J, Lin S C, Lin Y L,et al. A meta-learning approach for B-cell conformational epitope prediction[J]. BMC Bioinformatics, 2014, 15(1):378-393.
[18] Castrignano T, De Meo P D, Carrabino D, et al. The MEPS server for identifying protein conformational epitopes[J]. BMC Bioinformatics, 2007, 8(1):1-6.
[19] Chen W H, Sun P P, Lu Y, et al. MimoPro:A more efficient Web-based tool for epitope prediction using phage display libraries[J]. BMC Bioinformatics, 2011, 12(1):199-212.
[20] Zhao L, Li J Y. Mining for the antibody-antigen interacting associations that predict the B cell epitopes[J]. BMC Struct Biol, 2010, 10(suppl 1):1-6.
[21] Zhao L, Wong L, Li J Y. Antibody-specified B-cell epitope prediction in line with the principle of context-awareness[J]. IEEE/ACM Trans Comput Biol Bioinform, 2011, 8(6):1483-1494.
[22] Sun P, Qi J, Zhao Y, et al. A novel conformational B-cell epitope prediction method based on mimotope and patch analysis[J]. J Theor Biol, 2016, 394(1):102-108.
[23] Huang W L, Tsai M J, Hsu K T, et al. Prediction of linear B-cell epitopes of hepatitis C virus for vaccine development[J]. BMC Med Genomics, 2015, 8(14):1-3.
[24] Saravanan V, Gautham N. Harnessing computational biology for exact linear B-cell epitope prediction:A novel amino acid composition-based feature descriptor[J]. OMICS, 2015, 19(10):648-658.
[25] Guan P, Doytchinova I A, Zygouri C, et al. MHCPred:A server for quantitative prediction of peptide-MHC binding[J]. Nucleic Acids Res, 2003, 31(13):3621-3624.
[26] Parker K C, Bednarek M A, Coligan J E. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains[J]. J Immunol, 1994, 152(1):163-175.
[27] Rammensee H, Bachmann J, Emmerich N P, et al. SYFPEITHI:Database for MHC ligands and peptide motifs[J]. Immunogenetics, 1999, 50(3-4):213-219.
[28] Wang B, Chen H, Jiang X, et al. Identification of an HLA-A*0201-restricted CD8⁺ T-cell epitope SSp-1 of SARS-CoV spike protein[J]. Blood, 2004, 104(1):200-206.
[29] Abdel-Hady K M, Gutierrez H, Terry F, et al. Identification and retrospective validation of T-cell epitopes in the hepatitis C virus genotype 4 proteome:An accelerated approach toward epitope-driven vaccine development[J]. Hum Vaccin Immunother, 2014, 10(8):2366-2377.
[30] Bhasin M, Raghava G P. A hybrid approach for predicting promiscuous MHC class Ⅰ restricted T cell epitopes[J]. J Biosci, 2007, 32(1):32-42.
[31] Zhang G L, DeLuca D S, Keskin D B, et al. MULTIPRED:A computational system for prediction of promiscuous HLA binding peptides[J]. Nucleic Acids Res, 2005, 33(6):172-179.
[32] Vita R, DeLuca D S, Keskin D B, et al. The immune epitope database 2.0[J]. Nucleic Acids Res, 2010, 38(10):854-862.
[33] Larsen M V, Lundegaard C, Lamberth K, et al. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction[J]. BMC Bioinformatics, 2007, 8(1):424-435.
[34] Donnes P, Kohlbacher O. Integrated modeling of the major events in the MHC class Ⅰ antigen processing pathway[J]. Protein Sci, 2005, 14(8):2132-2140.
[35] Lundegaard C, Lamberth K, Harndahl M, et al. NetMHC-3.0:Accurate web accessible predictions of human, mouse and monkey MHC class Ⅰ affinities for peptides of length 8-11[J]. Nucleic Acids Res, 2008, 36(12):509-512.
[36] Farrell D, Gordon S V. Epitopemap:A web application for integrated whole proteome epitope prediction[J]. BMC Bioinformatics, 2015, 16(1):221-227.
[37] Lu Y F, Sheng H, Zhang Y, et al. Computational prediction of cleavage using proteasomal in vitro digestion and MHC Ⅰ ligand data[J]. J Zhejiang Univ Sci B, 2013, 14(9):816-828.
[38] Schubert B, Brachvogel H P, Jurges C, et al. EpiToolKit-a web-based workbench for vaccine design[J]. Bioinformatics, 2015, 31(13):2211-2231.
[39] Atanasova M, Patronov A, Dimitrov I, et al. EpiDOCK:A molecular docking-based tool for MHC class Ⅱ binding prediction[J]. Protein Eng Des Sel, 2013, 26(10):631-641.
[40] Grabowska A K, Kaufmann A M, Riemer A B. Identification of promiscuous HPV 16-derived T helper cell epitopes for therapeutic HPV vaccine design[J]. Int J Cancer, 2015,136(1):212-224.
[41] Lohia N, Baranwal M. Identification of conserved peptides comprising multiple T cell epitopes of matrix 1 protein in H1N1 influenza virus[J]. Viral Immunol, 2015, 28(10):570-579.
[42] Shahsavandi S, Ebrahimi M M, Sadeghi K, et al. Design of a heterosubtypic epitope-based peptide vaccine fused with hemokinin-1 against influenza viruses[J]. Virol Sin, 2015, 30(3):200-207.
[43] Duan Z L, Liu H F, Huang X, et al. Identification of conserved and HLA-A*2402-restricted epitopes in Dengue virus serotype 2[J]. Virus Res, 2015, 196(1):5-12.
[44] Liao Y C, Lin H H, Lin C H, et al. Identification of cytotoxic T lymphocyte epitopes on swine viruses:Multi-epitope design for universal T cell vaccine[J]. PLoS One, 2013, 8(12):e84443.
[45] Manijeh M, Mehrnaz K, Violaine M, et al. In silico design of discontinuous peptides representative of B and T-cell epitopes from HER 2-ECD as potential novel cancer peptide vaccines[J]. Asian Pac J Cancer Prev, 2013, 14(10):5973-5981.
[46] Park H, Chung Y S, Yoon C H, et al. Presentation of available CTL epitopes that induction of cell-mediatedimmune response against HIV-1 Koran clade B strain using computational technology[J]. HIV Med, 2015, 17(6):460-466.
[47] Sela-Culang I, Ashkenazi S, Peters B, et al. PEASE:Predicting B-cell epitopes utilizing antibody sequence[J]. Bioinformatics, 2015, 31(8):1313-1325.
[48] Mardis E R. The impact of next-generation sequencing technology on genetics[J]. Trends Genet, 2008, 24(3):133-141.
[49] Whitehead T A, Chevalie A, Song Y, et al. Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing[J]. Nat Biotechnol, 2012, 30(6):543-548.
[50] He L, Sok D, Azadnia P, et al. Toward a more accurate view of human B-cell repertoire by next-generation sequencing, unbiased repertoire capture and single-molecule barcoding[J]. Sci Rep, 2014, 4(1):6778-6790.
[51] Tschochner M, Leary S, Cooper D, et al. Identifying patient-specific epstein-barr nuclear antigen-1 genetic variation and potential autoreactive targets relevant to multiple sclerosis pathogenesis[J]. PLoS One, 2016, 11(2):e0147567.
[52] Yu Y, R Ceredig, C Seoighe. LymAnalyzer:A tool for comprehensive analysis of next generation sequencing data of T cell receptors and immunoglobulins[J]. Nucleic Acids Res, 2016, 44(4):e31.
[53] Van Blarcom T, Rossi A, Foletti D, et al. Precise and efficient antibody epitope determination through library design, yeast display and next-generation sequencing[J]. J Mol Biol, 2015, 427(6):1513-1534.
[54] Doolan K M, Colby D W. Conformation-dependent epitopes recognized by prion protein antibodies probed using mutational scanning and deep sequencing[J]. J Mol Biol, 2015, 427(2):328-340.
[55] Allegra C J, Rumble R B, Hamilton S R, et al. Extended RAS gene mutation testing in metastatic colorectal carcinoma to predict response to anti-epidermal growth factor receptor monoclonal antibody therapy:American society of clinical oncology provisional clinical opinion update 2015[J]. J Clin Oncol, 2016, 34(2):179-185.
[56] Hart J R, Zhang Y, Liao L, et al. The butterfly effect in cancer:A single base mutation can remodel the cell[J]. Proc Natl Acad Sci USA, 2015, 112(4):1131-1136.
[57] Alam M, Vance D E, Lehner R. Structure-function analysis of human triacylglycerol hydrolase by site-directed mutagenesis:Identification of the catalytic triad and a glycosylation site[J]. Biochemistry, 2002, 41(21):6679-6687.
[58] An M C, O' Brien R N, Zhang N, et al. Polyglutamine disease modeling:Epitope based screen for homologous recombination using CRISPR/Cas9 system[J]. PLoS Curr, 2014, 6(1):1-18.
[59] Keilhauer E C, Hein M Y, Mann M. Accurate protein complex retrieval by affinity enrichment mass spectrometry (AE-MS) rather than affinity purification mass spectrometry (AP-MS)[J]. Mol Cell Proteomics, 2015, 14(1):120-135.
[60] Hansen T H, Bouvier M. MHC class Ⅰ antigen presentation:Learning from viral evasion strategies[J]. Nat Rev Immunol, 2009, 9(7):503-513.
[61] Lopez D, Lorente E, Barriga A, et al. Vaccination and the TAP-independent antigen processing pathways[J]. Expert Rev Vaccine, 2013, 12(9):11077-11083.
[62] Gil-Torregrosa B C, Castano A R, Lopez D, et al. Generation of MHC class Ⅰ peptide antigens by protein processing in the secretory route by furin[J]. Traffic, 2000, 1(8):641-651.
[63] Doorduijn E M, Sluijter M, Querido B J, et al. TAP-independent self-peptides enhance T cell recognition of immune-escaped tumors[J]. J Clin Invest, 2016, 126(2):784-794.
[64] Weinzierl A O, Rudolf D, Hillen N, et al. Features of TAP-independent MHC class Ⅰ ligands revealed by quantitative mass spectrometry[J]. Eur J Immunol, 2008, 38(6):1503-1510.
[65] Rodriguez-Garcia A, Svensson E, Gil-Hoyo R, et al. Insertion of exogenous epitopes in the E3-19K of oncolytic adenoviruses to enhance TAP-independent presentation and immunogenicity[J]. Gene Ther, 2015, 22(7):596-601.