PLS方法在T细胞表位预测中的应用
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摘要
T细胞表位能够激活T细胞的免疫应答,在特异性免疫应答中发挥重要的作用。而T细胞表位的产生依赖于抗原加工提呈过程。细胞毒性T细胞(cytotoxicy T lymphocyte,CTL)表位的产生需要经过内源性抗原蛋白被蛋白酶体裂解产生内源性抗原肽、抗原肽被与抗原提呈相关的转运蛋白(transporter associated with antigen processing,TAP)转运至内质网(endoplasmic reticulum,ER)中、抗原肽与主要组织相容性复合体(majorhistocompatibility complex,MHC)Ⅰ类分子结合形成抗原肽-MHCⅠ类分子复合物、复合物被提呈至抗原提呈细胞(antigen presenting cells,APC)表面,与T细胞表面抗原受体(T cell receptor,TCR)结合激活CTL细胞这些主要过程。而辅助性T细胞(helperT cell,Th)表位的产生需要经过外源性抗原蛋白在内体/溶酶体(endosome/lysosome)中被裂解产生外源性抗原肽、抗原肽与MHCⅡ类分子结合形成抗原肽-MHCⅡ类分子复合物、复合物被提呈至APC表面,与TCR结合激活Th细胞这些主要过程。因此,抗原的加工提呈过程对T细胞表位的产生起到决定性作用。为了深入了解抗原的加工提呈机制,本文应用定量构效关系(quantitative structure activity relationships,QSAR)方法对抗原加工提呈途径中三个重要的选择性阶段进行了理论预测研究。
1.在内源性抗原的加工提呈途径中,抗原肽-MHCⅠ类分子复合物起到激活CTL细胞免疫应答的关键作用。而MHCⅠ类分子只能与特定的抗原肽结合,那么深入研究哪些抗原肽能够与MHCⅠ类分子结合,准确预测抗原肽与MHCⅠ类分子的结合亲和力将有助于深入了解抗原肽与MHCⅠ类分子结合的生物机理,有助于揭示CTL表位的产生机制。为了研究抗原肽与MHCⅠ类分子的结合特异性,本文建立了三种MHCⅠ类配体的QSAR模型,并采用偏最小二乘法(partial least squares,PLS)方法求解。通过比较各个模型的预测性能可知,在建立MHCⅠ类配体的QSAR模型时需要考虑氨基酸残基之间的相互作用。另外,本文通过分析抗原肽中不同位置氨基酸对结合MHCⅠ类分子形成的权重系数,获得了抗原肽与MHCⅠ类分子的结合特异性。
2.在内源性抗原的加工提呈途径中,真核细胞中的泛素—蛋白酶体系统对内源性抗原蛋白发挥着重要的降解功能。本文建立了蛋白酶体裂解内源性抗原蛋白的QSAR模型来研究蛋白酶体对抗原蛋白裂解的特异性,并采用PLS方法求解模型。得到模型的预测准确度为82.8%。在相同的检验集下与同领域其他模型比较可知,本文模型的预测性能最优。本文通过分析预测模型中不同位置上氨基酸对裂解位点形成的权重系数,发现裂解位点“｜”及其近邻位置氨基酸具有明显的特异性。研究结果表明蛋白酶体对抗原蛋白的酶切处理不是随机的,而是有一定模式和选择性的。
3.在外源性抗原的加工提呈途径中,抗原肽与MHCⅡ类分子复合物起到激活Th细胞免疫应答的关键作用。MHCⅡ类分子只能与特定的抗原肽结合,为了研究抗原肽与MHCⅡ类分子的结合特异性,本文分别建立了四种基于9-mer核心结合序列的MHCⅡ类配体QSAR模型和四种基于13-mer扩展核心结合序列的MHCⅡ类配体QSAR模型,采用CV-ISC-PLS方法进行求解。通过比较各个模型预测性能可知建立模型时需要考虑核心结合序列两侧的扩展位置并应采用合适的氨基酸描述符对氨基酸进行描述。另外,本文以HLA DRB1*0101为例,通过分析抗原肽中不同位置氨基酸对结合MHCⅡ类分子形成的权重系数,获取了抗原肽与MHCⅡ类分子的结合特异性。所得结果表明抗原肽的氨基酸特异性与实验结果基本一致。
T cell epitopes could activate immune response of T cell, and play an important role in specific immune response. The production of T cell epitopes depends on the antigen processing and presentation pathway. There are some primary steps in process of producting the cytotoxic T lymphocytes (CTL) epitopes, i.e. endogenous antigen proteins need to be degradated by proteasome into endogenous peptides, peptides need to be transported by transporter associated with antigen processing (TAP) into endoplasmic reticulum (ER), the peptides need to bind with major histocompatibility complex (MHC) class I molecule, and peptide-MHC class I complexes need to be transported to the surface of antigen presenting cells (APC), recognized by T cell receptor (TCR) and activate CTL. There are some primary processes in production of the helper T cell (Th) epitopes, i.e. exogenous antigen proteins need to be degradated by lysosomal enzyme into exogenous peptides, peptides need to bind with MHC class II molecule, and peptide-MHC class II complexex need to be transported to the surface of APC and recognized by TCR and activate Th. Therefore, in the process of producting T cell epitope, the antigen processing and presentation pathway plays a key role. In order to further study the biological mechanism of the antigen processing and presentation, quantitative structure activity relationships (QSAR) method was used to theoretically study three important steps in the antigen processing and presentation pathway.
1. In the endogenous antigen processing and presentation pathway, peptide-MHC class I complex plays a critical role in T cell activation. Only certain peptides could bind to MHCclass I molecule. Therefore, determining which peptides could bind to MHC class I molecule and predicting the affinity of peptide binding to MHC class I molecule accurately should be helpful to understand the mechanism of peptide binding MHC class I molecule and to show the mechanism of producting T cell epitope. In order to further study the specificity of MHC class I molecule binding antigen peptide, Three QSAR models of MHC class I ligand are built by PLS method. Comparison the prediction performance of the three model, it is known that the interactions between amino acid residues need to be considered when the QSAR model of MHC class I ligand is built. In addition, the specificities of MHC class I molecule binding antigen peptide were obtained by analysis the weight coefficients of QSAR model.
2. The ubiquitin-proteasome system of the eukaryote plays an importance role in the process of endogenous antigen degradation. The QSAR model of proteasomal cleaving antigen protein is built in order to study the specificity of the proteasome cleaving antigen protein, and PLS method is used to solve the model. And the predictive accuracy of the model is 82.8%. Comparison with other models in the same test set, this model has the superior predictive performance. It is known that there are obvious specificities in the cleavage site and its adjacent positions. The result indicates that the proteasome cleaves the antigen protein selectively, but not randomly.
3. In the exogenous antigen processing and presentation pathway, peptide-MHC classII complex plays an important role in activating immune response of helper T cell. Only certain peptides could bind to MHC class II molecule. In order to study the binding specificities between peptide and MHC class II molecule, four QSAR models of MHC class II ligand based on 9-mer core binding sequence and four QSAR models of MHC class II ligand based on 13-mer extended core binding sequence are built by CV-ISC-PLS. Through comparion among the predictive performance of models, it is known that the adjacent positons of core binding sequence need to be considered and suitable amino aicd descriptor is needed to be applied when the model is built. In addition, HLA DRBl*0101 is taken for example, the specificities of MHC class II molecule binding antigen peptide were obtained by analysis the weight coefficients of model. And the specificities agree with the experiment results.
1.在内源性抗原的加工提呈途径中,抗原肽-MHCⅠ类分子复合物起到激活CTL细胞免疫应答的关键作用。而MHCⅠ类分子只能与特定的抗原肽结合,那么深入研究哪些抗原肽能够与MHCⅠ类分子结合,准确预测抗原肽与MHCⅠ类分子的结合亲和力将有助于深入了解抗原肽与MHCⅠ类分子结合的生物机理,有助于揭示CTL表位的产生机制。为了研究抗原肽与MHCⅠ类分子的结合特异性,本文建立了三种MHCⅠ类配体的QSAR模型,并采用偏最小二乘法(partial least squares,PLS)方法求解。通过比较各个模型的预测性能可知,在建立MHCⅠ类配体的QSAR模型时需要考虑氨基酸残基之间的相互作用。另外,本文通过分析抗原肽中不同位置氨基酸对结合MHCⅠ类分子形成的权重系数,获得了抗原肽与MHCⅠ类分子的结合特异性。
2.在内源性抗原的加工提呈途径中,真核细胞中的泛素—蛋白酶体系统对内源性抗原蛋白发挥着重要的降解功能。本文建立了蛋白酶体裂解内源性抗原蛋白的QSAR模型来研究蛋白酶体对抗原蛋白裂解的特异性,并采用PLS方法求解模型。得到模型的预测准确度为82.8%。在相同的检验集下与同领域其他模型比较可知,本文模型的预测性能最优。本文通过分析预测模型中不同位置上氨基酸对裂解位点形成的权重系数,发现裂解位点“｜”及其近邻位置氨基酸具有明显的特异性。研究结果表明蛋白酶体对抗原蛋白的酶切处理不是随机的,而是有一定模式和选择性的。
3.在外源性抗原的加工提呈途径中,抗原肽与MHCⅡ类分子复合物起到激活Th细胞免疫应答的关键作用。MHCⅡ类分子只能与特定的抗原肽结合,为了研究抗原肽与MHCⅡ类分子的结合特异性,本文分别建立了四种基于9-mer核心结合序列的MHCⅡ类配体QSAR模型和四种基于13-mer扩展核心结合序列的MHCⅡ类配体QSAR模型,采用CV-ISC-PLS方法进行求解。通过比较各个模型预测性能可知建立模型时需要考虑核心结合序列两侧的扩展位置并应采用合适的氨基酸描述符对氨基酸进行描述。另外,本文以HLA DRB1*0101为例,通过分析抗原肽中不同位置氨基酸对结合MHCⅡ类分子形成的权重系数,获取了抗原肽与MHCⅡ类分子的结合特异性。所得结果表明抗原肽的氨基酸特异性与实验结果基本一致。
T cell epitopes could activate immune response of T cell, and play an important role in specific immune response. The production of T cell epitopes depends on the antigen processing and presentation pathway. There are some primary steps in process of producting the cytotoxic T lymphocytes (CTL) epitopes, i.e. endogenous antigen proteins need to be degradated by proteasome into endogenous peptides, peptides need to be transported by transporter associated with antigen processing (TAP) into endoplasmic reticulum (ER), the peptides need to bind with major histocompatibility complex (MHC) class I molecule, and peptide-MHC class I complexes need to be transported to the surface of antigen presenting cells (APC), recognized by T cell receptor (TCR) and activate CTL. There are some primary processes in production of the helper T cell (Th) epitopes, i.e. exogenous antigen proteins need to be degradated by lysosomal enzyme into exogenous peptides, peptides need to bind with MHC class II molecule, and peptide-MHC class II complexex need to be transported to the surface of APC and recognized by TCR and activate Th. Therefore, in the process of producting T cell epitope, the antigen processing and presentation pathway plays a key role. In order to further study the biological mechanism of the antigen processing and presentation, quantitative structure activity relationships (QSAR) method was used to theoretically study three important steps in the antigen processing and presentation pathway.
1. In the endogenous antigen processing and presentation pathway, peptide-MHC class I complex plays a critical role in T cell activation. Only certain peptides could bind to MHCclass I molecule. Therefore, determining which peptides could bind to MHC class I molecule and predicting the affinity of peptide binding to MHC class I molecule accurately should be helpful to understand the mechanism of peptide binding MHC class I molecule and to show the mechanism of producting T cell epitope. In order to further study the specificity of MHC class I molecule binding antigen peptide, Three QSAR models of MHC class I ligand are built by PLS method. Comparison the prediction performance of the three model, it is known that the interactions between amino acid residues need to be considered when the QSAR model of MHC class I ligand is built. In addition, the specificities of MHC class I molecule binding antigen peptide were obtained by analysis the weight coefficients of QSAR model.
2. The ubiquitin-proteasome system of the eukaryote plays an importance role in the process of endogenous antigen degradation. The QSAR model of proteasomal cleaving antigen protein is built in order to study the specificity of the proteasome cleaving antigen protein, and PLS method is used to solve the model. And the predictive accuracy of the model is 82.8%. Comparison with other models in the same test set, this model has the superior predictive performance. It is known that there are obvious specificities in the cleavage site and its adjacent positions. The result indicates that the proteasome cleaves the antigen protein selectively, but not randomly.
3. In the exogenous antigen processing and presentation pathway, peptide-MHC classII complex plays an important role in activating immune response of helper T cell. Only certain peptides could bind to MHC class II molecule. In order to study the binding specificities between peptide and MHC class II molecule, four QSAR models of MHC class II ligand based on 9-mer core binding sequence and four QSAR models of MHC class II ligand based on 13-mer extended core binding sequence are built by CV-ISC-PLS. Through comparion among the predictive performance of models, it is known that the adjacent positons of core binding sequence need to be considered and suitable amino aicd descriptor is needed to be applied when the model is built. In addition, HLA DRBl*0101 is taken for example, the specificities of MHC class II molecule binding antigen peptide were obtained by analysis the weight coefficients of model. And the specificities agree with the experiment results.
引文
[1]The Hygiene Hypothesis.Available from:http://www.squidoo.com/hygienehypothesis.
[2]周光炎.免疫学原理[M].2003,上海:上海科学技术文献出版社.
[3]Janeway C A著,钱旻,马瑞主译.免疫生物学[M].5 ed.2008:科学出版社.
[4]溶酶体途径—外源性抗原的降解.Available from:http://www.uscnlife.cn/web/page/news2246.htm.
[5]Sette A,Vitiello A,Reherman B,et al.THE RELATIONSHIP BETWEEN CLASS-I FINDING AFFINITY AND IMMUNOGENICITY OF POTENTIAL CYTOTOXIC T-CELL EPITOPES[J].Journal of Immunology,1994,153(12):5586-5592.
[6]林治华.QSAR分析结合实验方法用于T细胞表位快速筛选的研究[D].第三军医大学,2003.
[7]Blythe M J,Doytchinova I A,Flower D R.JenPep:a database of quantitative functional peptide data for immunology[J].Bioinformatics,2002,18(3):434-439.
[8]McSparron H,Blythe M J,Zygouri C,et al.JenPep:A novel computational information resource for immunobiology and vaccinology[J].Journal of Chemical Information and Computer Sciences,2003,43(4):1276-1287.
[9]Toseland C P,Clayton D J,McSparron H,et al.AntiJen:a quantitative immunology database integrating functional,thermodynamic,kinetic,biophysical,and cellular data[J].Immunome Res,2005,1(1):4.
[10]Rammensee H G,Bachmann J,Emmerich N P N,et al.SYFPEITHI:database for MHC ligands and peptide motifs[J].Immunogenetics,1999,50(3-4):213-219.
[11]Brusic V,Rudy G,Harrison L C.MHCPEP,a database of MHC-binding peptides:update 1997[J].Nucleic Acids Research,1998,26(1):368-371.
[12]Robinson J,Waller M J,Parham P,et al.IMGT/HLA and IMGT/MHC:sequence databases for the study of the major histocompatibility complex[J].Nucleic Acids Research,2003,31(1):311-314.
[13]Bhasin M,Singh H,Raghava G P S.MHCBN:a comprehensive database of MHC binding and non-binding peptides[J].Bioinformatics,2003,19(5):665-666.
[14]Reche P A,Glutting J P,Reinherz E L.Prediction of MHC class I binding peptides using profile motifs[J].Human Immunology,2002,63(9):701-709.
[15]Reche P A,Glutting J P,Zhang H,et al.Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles[J].Immunogenetics,2004,56(6):405-419.
[16] 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]. Journal of Immunology, 1994, 152(1): 163-175.
[17] Bian H J, Hammer J. Discovery of promiscuous HLA-II-restricted T cell epitopes with TEPITOPE[J]. Methods, 2004, 34(4): 468-475.
[18] Bian H J, Reidhaar-Olson J F, Hammer J. The use of bioinformatics for identifying class II-restricted T-cell epitopes[J]. Methods, 2003, 29(3): 299-309.
[19] Guan P, Doytchinova I A, Zygouri C, et al. MHCPred: bringing a quantitative dimension to the online prediction of MHC binding[J]. Appl Bioinformatics, 2003, 2(1): 63-6.
[20] Guan P P, Doytchinova I A, Zygouri C, et al. MHCPred: a server for quantitative prediction of peptide-MHC binding[J]. Nucleic Acids Research, 2003, 31 (13): 3621-3624.
[21] Singh H, Raghava G P S. ProPred: prediction of HLA-DR binding sites[J].Bioinformatics, 2001, 17(12): 1236-1237.
[22] Zhang G L, Khan A M, Srinivasan K N, et al. MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides[J]. Nucleic Acids Research, 2005, 33:W172-W179.
[23] Wang L, Pan D L, Hu X H, et al. BiodMHC: an online server for the prediction of MHC class II-peptide binding affinity[J]. Journal of Genetics and Genomics, 2009, 36(5):289-296.
[24] Bui H H, Sidney J, Peters B, et al. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications[J]. Immunogenetics, 2005, 57(5):304-314.
[25] Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method[J]. Bmc Bioinformatics, 2007, 8.
[26] Kuttler C, Nussbaum A K, Dick T P, et al. An algorithm for the prediction of proteasomal cleavages[J]. Journal of Molecular Biology, 2000, 298(3): 417-429.
[27] Nussbaum A K, Kuttler C, Hadeler K P, et al. PAProC: a prediction algorithm for proteasomal cleavages available on the WWW[J]. Immunogenetics, 2001, 53(2): 87-94.
[28] Kesmir C, Nussbaum A K, Schild H, et al. Prediction of proteasome cleavage motifs by neural networks[J]. Protein Engineering, 2002, 15(4): 287-296.
[29] Brusic V, Rudy G, Honeyman M, et al. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network[J]. Bioinformatics, 1998,14(2): 121-130.
[30] Nielsen M, Lundegaard C, Worning P, et al. Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach[J]. Bioinformatics, 2004, 20(9):1388-1397.
[31] Lata S, Bhasin M, Raghava G P S. Application of machine learning techniques in predicting MHC binders[J]. Methods in Molecular Biology, 2007: 201-215.
[32] Donnes P, Elofsson A. Prediction of MHC class I binding peptides, using SVMHC[J].Bmc Bioinformatics, 2002, 3.
[33] Wan J, Liu W, Xu Q Q, et al. SVRMHC prediction server for MHObinding peptides[J].Bmc Bioinformatics, 2006, 7.
[34] Doytchinova I A, Flower D R. Quantitative approaches to computational vaccinology[J]. Immunology and Cell Biology, 2002, 80(3): 270-279.
[35] Flower D R, McSparron H, Blythe M J, et al. Computational vaccinology: quantitative approaches[J]. Novartis Found Symp, 2003, 254: 102-20; discussion 120-5, 216-22, 250-2.
[36] Holzhutter H G, Frommel C, Kloetzel P M. A theoretical approach towards the identification of cleavage-determining amino acid motifs of the 20 S proteasome[J]. Journal of Molecular Biology, 1999, 286(4): 1251-1265.
[37] Holzhutter H G, Kloetzel P M. A kinetic model of vertebrate 20S proteasome accounting for the generation of major proteolytic fragments from oligomeric peptide substrates[J]. Biophysical Journal, 2000, 79(3): 1196-1205.
[38] McCammon J A, Gelin B R, Karplus M. Dynamics of folded proteins[J]. Nature, 1977,267(5612): 585-590.
[39] Hansch C, Fujita T. p-σ -π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure[J]. Journal of the American Chemical Society, 1964, 86(8):1616-1626.
[40] Free S M, Wilson J W. A Mathematical Contribution to Structure-Activity Studies[J].Journal of Medicinal Chemistry, 1964, 7(4): 395-399.
[41] Hopf inger A J. A QSAR investigation of dihydrofolate reductase inhibition by Baker triazines based upon molecular shape analysis[J]. Journal of the American Chemical Society,1980, 102(24) : 7196-7206.
[42] Crippen G M. QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS BY DISTANCE GEOMETRY- SYSTEMATIC ANALYSIS OF DIHYDROFOLATE-REDUCTASE INHIBITORS[J]. Journal of Medicinal Chemistry, 1980, 23(6): 599-606.
[43] Cramer R D, Patterson D E, Bunce J D. Comparative molecular field analysis (CoMFA).1. Effect of shape on binding of steroids to carrier proteins[J]. Journal of the American Chemical Society, 1988, 110(18): 5959-5967.
[44] Contreras M L, Gonzalez F D, Munoz V C, et al. COMPUTER-ASSISTED MOLECULAR DESIGN AND BIOPHORE DETERMINATION - APPLICATION TO AIDS DRUGS[J]. Boletin De La Sociedad Chilena De Quimica, 1995, 40(3): 279-291.
[45]Ortiz A R,Pisabarro M T,Gago F,et al.PREDICTION OF DRUG-BINDING AFFINITIES BY COMPARATIVE BINDING-ENERGY ANALYSIS[J].Journal of Medicinal Chemistry,1995,38(14):2681-2691.
[46]So S S,Karplus M.Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks.1.Method and validations[J].Journal of Medicinal Chemistry,1997,40(26):4347-4359.
[47]Robinson D D,Winn P J,Lyne P D,et al.Self-organizing molecular field analysis:A tool for structure-activity studies[J].Journal of medicinal Chemistry,1999,42(4):573-583.
[48]Polanski J,Walczak B.The comparative molecular surface analysis(COMSA):a novel tool for molecular design[J].Computers & Chemistry,2000,24(5):615-625.
[49]高惠璇.应用多元统计分析[M].2005,北京:北京大学出版社.
[50]郭纯孝.计算化学[M].2004,北京:化学工业出版社.
[51]王惠文.偏最小乘回归方法及其应用[M].1999.北京:国防工业出版社.
[52]Bodmer W F.THE HLA SYSTEM - STRUCTURE AND FUNCTION[J].Journal of Clinical Pathology,1987,40(9):948-958.
[53]Mehra N K,Kaur G.MilE-based vaccination approaches:progress and perspectives[J].Expert Reviews in Molecular Medicine,2003,5(07):1-17.
[54]Bjorkman P J,Saper M A,Samraoui B,et al.STRUCTURE OF THE HUMAN CLASS-I HISTOCOMPATIBILITY ANTIGEN,HLA-A2[J].Nature,1987,329(6139):506-512.
[55]Saper M A,Bjorkman P J,Wiley D C.Refined structure of the human histocompatibility antigen HLA-A2 at 2.6A resolution[J].Journal of Molecular Biology,1991,219(2):277-319.
[56]Hattotuwagama C K,Guan P P,Doytchinova I A,et al.Quantitative online prediction of peptide binding to the major histocompatibility complex[J].Journal of Molecular Graphics & Modelling,2004,22(3):195-207.
[57]Hellberg S,Sjostrom M,Skagerberg B,et al.peptide quantitative structure-activity relationships,a multivariate approach[J].Journal of Medicinal Chemistry,1987,30(7):1126-1135.
[58]Doytchinova I A,Flower D R.Toward the quantitative prediction of T-cell epitopes:CoMFA and CoMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201[J].Journal of Medicinal Chemistry,2001,44(22):3572-3581.
[59]Zhao B,Mathura V S,Rajaseger G,et al.A novel MHCp binding prediction model[J].Human Immunology,2003,64(12):1123-1143.
[60]Bhasin M,Raghava G P S.Prediction of CTL epitopes using QM,SVM and ANN techniques[J].Vaccine,2004,22(23-24):3195-3204.
[61]王松,夏绍玮.一种鲁棒主成分分析(PCA)算法[J].系统工程理论与实践,1998(01).
[62] Altuvia Y, Margalit H. A structure-based approach for prediction of MHC-binding peptides[J]. Methods, 2004, 34(4): 454-459.
[63] Kirksey T J, Pogue-Caley R R, Frelinger J A, et al. The structural basis for the increased immunogenicity of two HIV-reverse transcriptase peptide variant/class I major histocompatibility complexes[J]. Journal of Biological Chemistry, 1999, 274(52):37259-37264.
[64] Falk K, Rotzschke O, Stevanovic S, et al. allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules[J]. Nature, 1991, 351 (6324) : 290-296.
[65] Passmore L A, Barford D. Getting into position: the catalytic mechanisms of protein ubiquitylation[J]. Biochemical Journal, 2004, 379: 513-525.
[66] Coux O, Tanaka K, Goldberg A L. Structure and functions of the 20S and 26S proteasomes[J]. Annual Review of Biochemistry, 1996, 65: 801-847.
[67] Kisselev A F, Akopian T N, Woo K M, et al. The sizes of peptides generated from protein by mammalian 26 and 20 S proteasomes - Implications for understanding the degradative mechanism and antigen presentation[J]. Journal of Biological Chemistry, 1999,274(6): 3363-3371.
[68] Corn P. Role of the ubiquitin proteasome system in renal cell carcinoma[J]. BMC Biochemistry, 2007, 8(Suppl 1): S4.
[69] Goldberg A, Smith D M, Benaroudj N. Proteasomes and their associated ATPases: A.destructive combination[J]. Journal of Stuctural Biology, 2006, 156(1): 72-83.
[70] Seemuller E, Lupas A, Stock D, et al. PROTEASOME FROM THERMOPLASMA-ACIDOPHILUM- A THREONINE PROTEASE[J]. Science, 1995, 268(5210): 579-582.
[71] Mykles D L, Haire M F. BRANCHED-CHAIN-AMINO-ACID-PREFERRING PEPTIDASE ACTIVITY OF THE LOBSTER MULTICATALYTIC PROTEINASE (PROTEASOME) AND THE DEGRADATION OF MYOFIBRILLAR PROTEINS[J]. Biochemical Journal, 1995, 306: 285-291.
[72] Tanaka K. The proteasome: Overview of structure and functions[J]. Proceedings of the Japan Academy Series B-Physical and Biological Sciences, 2009, 85(1): 12-36.
[73] Altuvia Y, Margalit H. Sequence signals for generation of antigenic peptides by the proteasome: Implications for proteasomal cleavage mechanism[J]. Journal of Molecular Biology, 2000, 295(4): 879-890.
[74] Heinemeyer W, Ramos P C, Dohmen R J. Ubiquitin-proteasome system - The ultimate nanoscale mincer: assembly, structure and active sites of the 20S proteasome core[J].Cellular and Molecular Life Sciences, 2004, 61(13): 1562-1578.
[75] Goldberg A L, Cascio P, Saric T, et al. The importance of the proteasome and subsequent proteolytic steps in the generation of antigenic peptides[J]. Molecular Immunology, 2002, 39(3-4): 147-164.
[76] Saxova P, Buus S, Brunak S, et al. Predicting proteasomal cleavage sites: a comparison of available methods[J]. International Immunology, 2003, 15(7): 781-787.
[77] Wenzel T, Eckerskorn C, Lottspeich F, et al. existence of a molecular ruler in proteasomes suggested by analysis of degradation products[J]. Febs Letters, 1994, 349(2):205-209.
[78] Dick L R, Moomaw C R, Demartino G N, et al. degradation of oxidized insulin b-chain by the multiproteinase complex macropain (proteasome)[J]. Biochemistry, 1991, 30(10):2725-2734.
[79] Nussbaum A K, Dick T P, Keilholz W, et al. Cleavage motifs of the yeast 20S proteasome beta subunits deduced from digests of enolase 1[J]. Proceedings of the National Academy of Sciences of the United States of America, 1998, 95(21): 12504-12509.
[80] Unno M, Mizushima T, Morimoto Y, et al. The structure of the mammalian 20S proteasome at 2.75 angstrom resolution[J]. Structure, 2002, 10(5): 609-618.
[81]宋哲,刘涛,王雪莹等.PLS方法的应用于T细胞表位定量构效关系的研究[J].免疫学杂志, 2007, 23(02): 166-171.
[82] Song Z, Liu T, Liu W, et al. QSAR model study of interaction between peptides and MHC molecules[J]. Acta Physico-Chimica Sinica, 2007, 23(2): 198-205.
[83] Liu T, Song Z, Liu W, et al. Prediction of CTL epitopes based on modified artificial neural network[J]. Dalian Ligong Daxue Xuebao/Journal of Dalian University of Technology,2007, 47(4): 473-478.
[84] Brown J H, Jardetzky T S, Gorga J C, et al. 3-DIMENSIONAL STRUCTURE OF THE HUMAN CLASS-II HISTOCOMPATIBILITY ANTIGEN HLA-DR1[J]. Nature, 1993, 364(6432): 33-39.
[85] Stern L J, Brown J H, Jardetzky T S, et al. Crystal-structure of the human class-II MHC protein HLA-DR1 complexed with an influenza-virus peptide[J]. Nature, 1994, 368(6468):215-221.
[86] Jardetzky T S, Brown J H, Gorga J C, et al. Crystallographic analysis of endogenous peptides associated with HLA-DR1 suggests a common, polyproline II—1 ike conformation for bound peptides[J]. Proceedings of the National Academy of Sciences of the United States of America, 1996, 93(2): 734-738.
[87] Guan P, Hattotuwagama C K, Doytchihova I A, et al. MHCPred 2.0: an updated quantitative T-cell epitope prediction server[J]. Appl Bioinformatics, 2006, 5(1): 55-61.
[88] Zhu Y R, Rudensky A Y, Corper A L, et al. Crystal structure of MHC class II I-A(b) in complex with a human CLIP peptide: Prediction of an I-A(b) peptide-binding motif[J].Journal of Molecular Biology, 2003, 326(4): 1157-1174.
[89] Wang R F, Wang X, Rosenberg S A. Identification of a novel major histocompatibility complex class II-restricted tumor antigen resulting from a chromosomal rearrangement recognized by CD4(+) T cells[J]. Journal of Experimental Medicine, 1999, 189(10):1659-1667.
[90] Bhasin M, Raghava G P S. Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences[J].Nucleic Acids Research, 2005, 33: 202-207.
[91] Consogno G, Manici S, Facchinetti V, et al. Identification of immunodominant regions among promiscuous HLA-DR-restricted CD4(+) T-cell epitopes on the tumor antigen MAGE-3[J]. Blood, 2003, 101(3): 1038-1044.
[92] Collantes E R, Dunn W J. Amino Acid Side Chain Descriptors for Quantitative Structure-Activity Relationship Studies of Peptide Analogs[J]. Journal of Medicinal Chemistry, 1995, 38(14): 2705-2713.
[93] Zaliani A, Gancia E. MS-WHIM Scores for Amino Acids: A New 3D-Description for Peptide QSAR and QSPR Studies[J]. Journal of Chemical Information and Computer Sciences,1999, 39(3): 525-533.
[94] Marina C, Erik J. Amino Acids Characterization by GRID and Multivariate Data Analysis[J]. Quantitative Structure-Activity Relationships, 1993, 12(1): 1-8.
[95] Liu S S, Yin C S, Cai S X, et al. A novel MHDV descriptor for dipeptide QSAR studies[J]. Journal of the Chinese Chemical Society, 2001, 48(2): 253-260.
[96] Kawashima S, Kanehisa M. AAindex: Amino acid index database[J]. Nucleic Acids Research, 2000, 28(1): 374-374.
[97] Nakai K, Kidera A, Kanehisa M. CLUSTER-ANALYSIS OF AMINO-ACID INDEXES FOR PREDICTION OF PROTEIN-STRUCTURE AND FUNCTION[J]. Protein Engineering, 1988, 2(2): 93-100.
[98] Tomii K, Kanehisa M. Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins[J]. Protein Engineering, 1996,9(1): 27-36.
[99] Kawashima S, Ogata H, Kanehisa M. AAindex: Amino Acid Index Database[J]. Nucleic Acids Research, 1999, 27(1): 368-369.
[100] Kawashima S, Pokarowski P, Pokarowska M, et al. AAindex: amino acid index database,progress report 2008[J]. Nucleic Acids Research, 2008, 36: 202-205.
[101] Sandberg M, Eriksson L, Jonsson J, et al. New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids[J]. Journal of Medicinal Chemistry, 1998, 41(14): 2481-2491.
[102] Liu W, Meng X S, Xu Q Q, et al. Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models[J]. Bmc Bioinformatics, 2006, 7.
[103] Peters B, Sidney J, Bourne P, et al. The immune epitope database and analysis resource: From vision to blueprint[J]. Plos Biology, 2005, 3(3): 379-381.
[104] Mygind H E, M(?)lgaard M. Prediction of MHC class II epitopes using genetic algorithms and other metaheuristics[D]. Lyngby: Technical University of Denmark Department of Informatics and Mathematical Modeling, Algorithms and Logic, 2009.
[105] Doytchinova I A, Flower D R. Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction[J]. Bioinformatics, 2003, 19(17): 2263-2270.
[106] Jonassen I, Eidhammer I, Conklin D, et al. Structure motif discovery and mining the PDB[J]. Bioinformatics, 2002, 18(2): 362-367.
[107] Swets J A. Measuring the accuracy of diagnostic systems[J]. Science, 1988,240(4857): 1285-1293.
[108] Hammer J, Takacs B, Sinigaglia F. Identification of a motif for HLA-DR1 binding peptides using M13 display libraries[J]. Journal of Experimental Medicine, 1992, 176(4):1007-1013.
[109] Madden D R. The 3-dimensional structure of peptde-MHC complexes[J]. Annual Review of Immunology, 1995, 13: 587-622.
[110] Cardenas C, Ortiz M, Balbin A, et al. Allele effects in MHC-peptide interactions:A theoretical analysis of HLA-DR beta l*0101-HA and HLA-DR beta l*0401-HA complexes[J].Biochemical and Biophysical Research Communications, 2005, 330(4): 1162-1167.
[111] Sundberg E J, Sawicki M W, Southwood S, et al. Minor structural changes in a mutated human melanoma antigen correspond to dramatically enhanced stimulation of a CD4(+) tumor-infiltrating lymphocyte line[J]. Journal of Molecular Biology, 2002, 319(2):449-461.
[112] Rosloniec E F, Ivey R A, Whittington K B, et al. Crystallographic structure of a rheumatoid arthritis MHC susceptibility allele, HLA-DR1 (DRBI*0101), complexed with the immunodominant determinant of human type II collagen[J]. Journal of Immunology, 2006,177(6): 3884-3892.
[113] Busch R, Hill CM, Hayball J D, et al. Effect of natural polymorphism at residue-86 of the HLA-DR beta-chain on peptide binding[J]. Journal of Immunology, 1991, 147(4):1292-1298.
[114] Newtonnash D K, Eckels D D. Differential effect of polymorphism at HLA-DR1 beta-chain position-85 and position-86 on binding and recognition of DR1-restricted antigenic peptides[J]. Journal of Immunology, 1993, 150(5): 1813-1821.
[115] Hill C M, Liu A, Marshall K W, et al. Exploration of requirements for peptide binding to HLA DRBl*0101 and DRB1*0401[J]. Journal of Immunology, 1994, 152(6) : 2890-2898.
[116] Jardetzky T S, Gorga J C, Busch R, et al. Peptide binding to HLA-DR1 - a peptide with most residues substituted to alanine retains MHC binding[J]. Embo Journal, 1990, 9(6):1797-1803.
[117] Murthy V L, Stern L J. The class II MHC protein HLA-DR1 in complex with an endogenous peptide: implications for the structural basis of the specificity of peptide binding[J]. Structure, 1997, 5(10): 1385-1396.
[118] Vargas L E, Parra C A, Salazar L M, et al. MHC allele-specific binding of a malaria peptide makes it become promiscuous on fitting a glycine residue into pocket 6[J].Biochemical and Biophysical Research Communications, 2003, 307(1): 148-156.
[119] Fremont D H, Hendrickson W A, Marrack P, et al. Structures of an MHC class II molecule with covalently bound single peptides[J]. Science, 1996, 272(5264): 1001-1004.
[120] Wilson N, Fremont D, Marrack P, et al. Mutations changing the kinetics of class II MHC peptide exchange[J]. Immunity, 2001, 14(5): 513-522.
[121] Zavala-Ruiz Z, Sundberg E J, Stone J D, et al. Exploration of the P6/P7 region of the peptide-binding site of the human class II major histocompatability complex protein HLA-DR1[J]. Journal of Biological Chemistry, 2003, 278(45): 44904-44912.
[122] Cardenas C, Villaveces J L, Bohorquez H, et al. Quantum chemical analysis explains hemagglutinin peptide-MHC class II molecule HLA-DR beta I*0101 interactions[J].Biochemical and Biophysical Research Communications, 2004, 323(4): 1265-1277.
[123] Zavala-Ruiz Z, Strug I, Anderson M W, et al. A polymorphic pocket at the P10 position contributes to peptide binding specificity in class II MHC proteins[J]. Chemistry& Biology, 2004, 11(10): 1395-1402.
[2]周光炎.免疫学原理[M].2003,上海:上海科学技术文献出版社.
[3]Janeway C A著,钱旻,马瑞主译.免疫生物学[M].5 ed.2008:科学出版社.
[4]溶酶体途径—外源性抗原的降解.Available from:http://www.uscnlife.cn/web/page/news2246.htm.
[5]Sette A,Vitiello A,Reherman B,et al.THE RELATIONSHIP BETWEEN CLASS-I FINDING AFFINITY AND IMMUNOGENICITY OF POTENTIAL CYTOTOXIC T-CELL EPITOPES[J].Journal of Immunology,1994,153(12):5586-5592.
[6]林治华.QSAR分析结合实验方法用于T细胞表位快速筛选的研究[D].第三军医大学,2003.
[7]Blythe M J,Doytchinova I A,Flower D R.JenPep:a database of quantitative functional peptide data for immunology[J].Bioinformatics,2002,18(3):434-439.
[8]McSparron H,Blythe M J,Zygouri C,et al.JenPep:A novel computational information resource for immunobiology and vaccinology[J].Journal of Chemical Information and Computer Sciences,2003,43(4):1276-1287.
[9]Toseland C P,Clayton D J,McSparron H,et al.AntiJen:a quantitative immunology database integrating functional,thermodynamic,kinetic,biophysical,and cellular data[J].Immunome Res,2005,1(1):4.
[10]Rammensee H G,Bachmann J,Emmerich N P N,et al.SYFPEITHI:database for MHC ligands and peptide motifs[J].Immunogenetics,1999,50(3-4):213-219.
[11]Brusic V,Rudy G,Harrison L C.MHCPEP,a database of MHC-binding peptides:update 1997[J].Nucleic Acids Research,1998,26(1):368-371.
[12]Robinson J,Waller M J,Parham P,et al.IMGT/HLA and IMGT/MHC:sequence databases for the study of the major histocompatibility complex[J].Nucleic Acids Research,2003,31(1):311-314.
[13]Bhasin M,Singh H,Raghava G P S.MHCBN:a comprehensive database of MHC binding and non-binding peptides[J].Bioinformatics,2003,19(5):665-666.
[14]Reche P A,Glutting J P,Reinherz E L.Prediction of MHC class I binding peptides using profile motifs[J].Human Immunology,2002,63(9):701-709.
[15]Reche P A,Glutting J P,Zhang H,et al.Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles[J].Immunogenetics,2004,56(6):405-419.
[16] 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]. Journal of Immunology, 1994, 152(1): 163-175.
[17] Bian H J, Hammer J. Discovery of promiscuous HLA-II-restricted T cell epitopes with TEPITOPE[J]. Methods, 2004, 34(4): 468-475.
[18] Bian H J, Reidhaar-Olson J F, Hammer J. The use of bioinformatics for identifying class II-restricted T-cell epitopes[J]. Methods, 2003, 29(3): 299-309.
[19] Guan P, Doytchinova I A, Zygouri C, et al. MHCPred: bringing a quantitative dimension to the online prediction of MHC binding[J]. Appl Bioinformatics, 2003, 2(1): 63-6.
[20] Guan P P, Doytchinova I A, Zygouri C, et al. MHCPred: a server for quantitative prediction of peptide-MHC binding[J]. Nucleic Acids Research, 2003, 31 (13): 3621-3624.
[21] Singh H, Raghava G P S. ProPred: prediction of HLA-DR binding sites[J].Bioinformatics, 2001, 17(12): 1236-1237.
[22] Zhang G L, Khan A M, Srinivasan K N, et al. MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides[J]. Nucleic Acids Research, 2005, 33:W172-W179.
[23] Wang L, Pan D L, Hu X H, et al. BiodMHC: an online server for the prediction of MHC class II-peptide binding affinity[J]. Journal of Genetics and Genomics, 2009, 36(5):289-296.
[24] Bui H H, Sidney J, Peters B, et al. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications[J]. Immunogenetics, 2005, 57(5):304-314.
[25] Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method[J]. Bmc Bioinformatics, 2007, 8.
[26] Kuttler C, Nussbaum A K, Dick T P, et al. An algorithm for the prediction of proteasomal cleavages[J]. Journal of Molecular Biology, 2000, 298(3): 417-429.
[27] Nussbaum A K, Kuttler C, Hadeler K P, et al. PAProC: a prediction algorithm for proteasomal cleavages available on the WWW[J]. Immunogenetics, 2001, 53(2): 87-94.
[28] Kesmir C, Nussbaum A K, Schild H, et al. Prediction of proteasome cleavage motifs by neural networks[J]. Protein Engineering, 2002, 15(4): 287-296.
[29] Brusic V, Rudy G, Honeyman M, et al. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network[J]. Bioinformatics, 1998,14(2): 121-130.
[30] Nielsen M, Lundegaard C, Worning P, et al. Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach[J]. Bioinformatics, 2004, 20(9):1388-1397.
[31] Lata S, Bhasin M, Raghava G P S. Application of machine learning techniques in predicting MHC binders[J]. Methods in Molecular Biology, 2007: 201-215.
[32] Donnes P, Elofsson A. Prediction of MHC class I binding peptides, using SVMHC[J].Bmc Bioinformatics, 2002, 3.
[33] Wan J, Liu W, Xu Q Q, et al. SVRMHC prediction server for MHObinding peptides[J].Bmc Bioinformatics, 2006, 7.
[34] Doytchinova I A, Flower D R. Quantitative approaches to computational vaccinology[J]. Immunology and Cell Biology, 2002, 80(3): 270-279.
[35] Flower D R, McSparron H, Blythe M J, et al. Computational vaccinology: quantitative approaches[J]. Novartis Found Symp, 2003, 254: 102-20; discussion 120-5, 216-22, 250-2.
[36] Holzhutter H G, Frommel C, Kloetzel P M. A theoretical approach towards the identification of cleavage-determining amino acid motifs of the 20 S proteasome[J]. Journal of Molecular Biology, 1999, 286(4): 1251-1265.
[37] Holzhutter H G, Kloetzel P M. A kinetic model of vertebrate 20S proteasome accounting for the generation of major proteolytic fragments from oligomeric peptide substrates[J]. Biophysical Journal, 2000, 79(3): 1196-1205.
[38] McCammon J A, Gelin B R, Karplus M. Dynamics of folded proteins[J]. Nature, 1977,267(5612): 585-590.
[39] Hansch C, Fujita T. p-σ -π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure[J]. Journal of the American Chemical Society, 1964, 86(8):1616-1626.
[40] Free S M, Wilson J W. A Mathematical Contribution to Structure-Activity Studies[J].Journal of Medicinal Chemistry, 1964, 7(4): 395-399.
[41] Hopf inger A J. A QSAR investigation of dihydrofolate reductase inhibition by Baker triazines based upon molecular shape analysis[J]. Journal of the American Chemical Society,1980, 102(24) : 7196-7206.
[42] Crippen G M. QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS BY DISTANCE GEOMETRY- SYSTEMATIC ANALYSIS OF DIHYDROFOLATE-REDUCTASE INHIBITORS[J]. Journal of Medicinal Chemistry, 1980, 23(6): 599-606.
[43] Cramer R D, Patterson D E, Bunce J D. Comparative molecular field analysis (CoMFA).1. Effect of shape on binding of steroids to carrier proteins[J]. Journal of the American Chemical Society, 1988, 110(18): 5959-5967.
[44] Contreras M L, Gonzalez F D, Munoz V C, et al. COMPUTER-ASSISTED MOLECULAR DESIGN AND BIOPHORE DETERMINATION - APPLICATION TO AIDS DRUGS[J]. Boletin De La Sociedad Chilena De Quimica, 1995, 40(3): 279-291.
[45]Ortiz A R,Pisabarro M T,Gago F,et al.PREDICTION OF DRUG-BINDING AFFINITIES BY COMPARATIVE BINDING-ENERGY ANALYSIS[J].Journal of Medicinal Chemistry,1995,38(14):2681-2691.
[46]So S S,Karplus M.Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks.1.Method and validations[J].Journal of Medicinal Chemistry,1997,40(26):4347-4359.
[47]Robinson D D,Winn P J,Lyne P D,et al.Self-organizing molecular field analysis:A tool for structure-activity studies[J].Journal of medicinal Chemistry,1999,42(4):573-583.
[48]Polanski J,Walczak B.The comparative molecular surface analysis(COMSA):a novel tool for molecular design[J].Computers & Chemistry,2000,24(5):615-625.
[49]高惠璇.应用多元统计分析[M].2005,北京:北京大学出版社.
[50]郭纯孝.计算化学[M].2004,北京:化学工业出版社.
[51]王惠文.偏最小乘回归方法及其应用[M].1999.北京:国防工业出版社.
[52]Bodmer W F.THE HLA SYSTEM - STRUCTURE AND FUNCTION[J].Journal of Clinical Pathology,1987,40(9):948-958.
[53]Mehra N K,Kaur G.MilE-based vaccination approaches:progress and perspectives[J].Expert Reviews in Molecular Medicine,2003,5(07):1-17.
[54]Bjorkman P J,Saper M A,Samraoui B,et al.STRUCTURE OF THE HUMAN CLASS-I HISTOCOMPATIBILITY ANTIGEN,HLA-A2[J].Nature,1987,329(6139):506-512.
[55]Saper M A,Bjorkman P J,Wiley D C.Refined structure of the human histocompatibility antigen HLA-A2 at 2.6A resolution[J].Journal of Molecular Biology,1991,219(2):277-319.
[56]Hattotuwagama C K,Guan P P,Doytchinova I A,et al.Quantitative online prediction of peptide binding to the major histocompatibility complex[J].Journal of Molecular Graphics & Modelling,2004,22(3):195-207.
[57]Hellberg S,Sjostrom M,Skagerberg B,et al.peptide quantitative structure-activity relationships,a multivariate approach[J].Journal of Medicinal Chemistry,1987,30(7):1126-1135.
[58]Doytchinova I A,Flower D R.Toward the quantitative prediction of T-cell epitopes:CoMFA and CoMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201[J].Journal of Medicinal Chemistry,2001,44(22):3572-3581.
[59]Zhao B,Mathura V S,Rajaseger G,et al.A novel MHCp binding prediction model[J].Human Immunology,2003,64(12):1123-1143.
[60]Bhasin M,Raghava G P S.Prediction of CTL epitopes using QM,SVM and ANN techniques[J].Vaccine,2004,22(23-24):3195-3204.
[61]王松,夏绍玮.一种鲁棒主成分分析(PCA)算法[J].系统工程理论与实践,1998(01).
[62] Altuvia Y, Margalit H. A structure-based approach for prediction of MHC-binding peptides[J]. Methods, 2004, 34(4): 454-459.
[63] Kirksey T J, Pogue-Caley R R, Frelinger J A, et al. The structural basis for the increased immunogenicity of two HIV-reverse transcriptase peptide variant/class I major histocompatibility complexes[J]. Journal of Biological Chemistry, 1999, 274(52):37259-37264.
[64] Falk K, Rotzschke O, Stevanovic S, et al. allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules[J]. Nature, 1991, 351 (6324) : 290-296.
[65] Passmore L A, Barford D. Getting into position: the catalytic mechanisms of protein ubiquitylation[J]. Biochemical Journal, 2004, 379: 513-525.
[66] Coux O, Tanaka K, Goldberg A L. Structure and functions of the 20S and 26S proteasomes[J]. Annual Review of Biochemistry, 1996, 65: 801-847.
[67] Kisselev A F, Akopian T N, Woo K M, et al. The sizes of peptides generated from protein by mammalian 26 and 20 S proteasomes - Implications for understanding the degradative mechanism and antigen presentation[J]. Journal of Biological Chemistry, 1999,274(6): 3363-3371.
[68] Corn P. Role of the ubiquitin proteasome system in renal cell carcinoma[J]. BMC Biochemistry, 2007, 8(Suppl 1): S4.
[69] Goldberg A, Smith D M, Benaroudj N. Proteasomes and their associated ATPases: A.destructive combination[J]. Journal of Stuctural Biology, 2006, 156(1): 72-83.
[70] Seemuller E, Lupas A, Stock D, et al. PROTEASOME FROM THERMOPLASMA-ACIDOPHILUM- A THREONINE PROTEASE[J]. Science, 1995, 268(5210): 579-582.
[71] Mykles D L, Haire M F. BRANCHED-CHAIN-AMINO-ACID-PREFERRING PEPTIDASE ACTIVITY OF THE LOBSTER MULTICATALYTIC PROTEINASE (PROTEASOME) AND THE DEGRADATION OF MYOFIBRILLAR PROTEINS[J]. Biochemical Journal, 1995, 306: 285-291.
[72] Tanaka K. The proteasome: Overview of structure and functions[J]. Proceedings of the Japan Academy Series B-Physical and Biological Sciences, 2009, 85(1): 12-36.
[73] Altuvia Y, Margalit H. Sequence signals for generation of antigenic peptides by the proteasome: Implications for proteasomal cleavage mechanism[J]. Journal of Molecular Biology, 2000, 295(4): 879-890.
[74] Heinemeyer W, Ramos P C, Dohmen R J. Ubiquitin-proteasome system - The ultimate nanoscale mincer: assembly, structure and active sites of the 20S proteasome core[J].Cellular and Molecular Life Sciences, 2004, 61(13): 1562-1578.
[75] Goldberg A L, Cascio P, Saric T, et al. The importance of the proteasome and subsequent proteolytic steps in the generation of antigenic peptides[J]. Molecular Immunology, 2002, 39(3-4): 147-164.
[76] Saxova P, Buus S, Brunak S, et al. Predicting proteasomal cleavage sites: a comparison of available methods[J]. International Immunology, 2003, 15(7): 781-787.
[77] Wenzel T, Eckerskorn C, Lottspeich F, et al. existence of a molecular ruler in proteasomes suggested by analysis of degradation products[J]. Febs Letters, 1994, 349(2):205-209.
[78] Dick L R, Moomaw C R, Demartino G N, et al. degradation of oxidized insulin b-chain by the multiproteinase complex macropain (proteasome)[J]. Biochemistry, 1991, 30(10):2725-2734.
[79] Nussbaum A K, Dick T P, Keilholz W, et al. Cleavage motifs of the yeast 20S proteasome beta subunits deduced from digests of enolase 1[J]. Proceedings of the National Academy of Sciences of the United States of America, 1998, 95(21): 12504-12509.
[80] Unno M, Mizushima T, Morimoto Y, et al. The structure of the mammalian 20S proteasome at 2.75 angstrom resolution[J]. Structure, 2002, 10(5): 609-618.
[81]宋哲,刘涛,王雪莹等.PLS方法的应用于T细胞表位定量构效关系的研究[J].免疫学杂志, 2007, 23(02): 166-171.
[82] Song Z, Liu T, Liu W, et al. QSAR model study of interaction between peptides and MHC molecules[J]. Acta Physico-Chimica Sinica, 2007, 23(2): 198-205.
[83] Liu T, Song Z, Liu W, et al. Prediction of CTL epitopes based on modified artificial neural network[J]. Dalian Ligong Daxue Xuebao/Journal of Dalian University of Technology,2007, 47(4): 473-478.
[84] Brown J H, Jardetzky T S, Gorga J C, et al. 3-DIMENSIONAL STRUCTURE OF THE HUMAN CLASS-II HISTOCOMPATIBILITY ANTIGEN HLA-DR1[J]. Nature, 1993, 364(6432): 33-39.
[85] Stern L J, Brown J H, Jardetzky T S, et al. Crystal-structure of the human class-II MHC protein HLA-DR1 complexed with an influenza-virus peptide[J]. Nature, 1994, 368(6468):215-221.
[86] Jardetzky T S, Brown J H, Gorga J C, et al. Crystallographic analysis of endogenous peptides associated with HLA-DR1 suggests a common, polyproline II—1 ike conformation for bound peptides[J]. Proceedings of the National Academy of Sciences of the United States of America, 1996, 93(2): 734-738.
[87] Guan P, Hattotuwagama C K, Doytchihova I A, et al. MHCPred 2.0: an updated quantitative T-cell epitope prediction server[J]. Appl Bioinformatics, 2006, 5(1): 55-61.
[88] Zhu Y R, Rudensky A Y, Corper A L, et al. Crystal structure of MHC class II I-A(b) in complex with a human CLIP peptide: Prediction of an I-A(b) peptide-binding motif[J].Journal of Molecular Biology, 2003, 326(4): 1157-1174.
[89] Wang R F, Wang X, Rosenberg S A. Identification of a novel major histocompatibility complex class II-restricted tumor antigen resulting from a chromosomal rearrangement recognized by CD4(+) T cells[J]. Journal of Experimental Medicine, 1999, 189(10):1659-1667.
[90] Bhasin M, Raghava G P S. Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences[J].Nucleic Acids Research, 2005, 33: 202-207.
[91] Consogno G, Manici S, Facchinetti V, et al. Identification of immunodominant regions among promiscuous HLA-DR-restricted CD4(+) T-cell epitopes on the tumor antigen MAGE-3[J]. Blood, 2003, 101(3): 1038-1044.
[92] Collantes E R, Dunn W J. Amino Acid Side Chain Descriptors for Quantitative Structure-Activity Relationship Studies of Peptide Analogs[J]. Journal of Medicinal Chemistry, 1995, 38(14): 2705-2713.
[93] Zaliani A, Gancia E. MS-WHIM Scores for Amino Acids: A New 3D-Description for Peptide QSAR and QSPR Studies[J]. Journal of Chemical Information and Computer Sciences,1999, 39(3): 525-533.
[94] Marina C, Erik J. Amino Acids Characterization by GRID and Multivariate Data Analysis[J]. Quantitative Structure-Activity Relationships, 1993, 12(1): 1-8.
[95] Liu S S, Yin C S, Cai S X, et al. A novel MHDV descriptor for dipeptide QSAR studies[J]. Journal of the Chinese Chemical Society, 2001, 48(2): 253-260.
[96] Kawashima S, Kanehisa M. AAindex: Amino acid index database[J]. Nucleic Acids Research, 2000, 28(1): 374-374.
[97] Nakai K, Kidera A, Kanehisa M. CLUSTER-ANALYSIS OF AMINO-ACID INDEXES FOR PREDICTION OF PROTEIN-STRUCTURE AND FUNCTION[J]. Protein Engineering, 1988, 2(2): 93-100.
[98] Tomii K, Kanehisa M. Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins[J]. Protein Engineering, 1996,9(1): 27-36.
[99] Kawashima S, Ogata H, Kanehisa M. AAindex: Amino Acid Index Database[J]. Nucleic Acids Research, 1999, 27(1): 368-369.
[100] Kawashima S, Pokarowski P, Pokarowska M, et al. AAindex: amino acid index database,progress report 2008[J]. Nucleic Acids Research, 2008, 36: 202-205.
[101] Sandberg M, Eriksson L, Jonsson J, et al. New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids[J]. Journal of Medicinal Chemistry, 1998, 41(14): 2481-2491.
[102] Liu W, Meng X S, Xu Q Q, et al. Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models[J]. Bmc Bioinformatics, 2006, 7.
[103] Peters B, Sidney J, Bourne P, et al. The immune epitope database and analysis resource: From vision to blueprint[J]. Plos Biology, 2005, 3(3): 379-381.
[104] Mygind H E, M(?)lgaard M. Prediction of MHC class II epitopes using genetic algorithms and other metaheuristics[D]. Lyngby: Technical University of Denmark Department of Informatics and Mathematical Modeling, Algorithms and Logic, 2009.
[105] Doytchinova I A, Flower D R. Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction[J]. Bioinformatics, 2003, 19(17): 2263-2270.
[106] Jonassen I, Eidhammer I, Conklin D, et al. Structure motif discovery and mining the PDB[J]. Bioinformatics, 2002, 18(2): 362-367.
[107] Swets J A. Measuring the accuracy of diagnostic systems[J]. Science, 1988,240(4857): 1285-1293.
[108] Hammer J, Takacs B, Sinigaglia F. Identification of a motif for HLA-DR1 binding peptides using M13 display libraries[J]. Journal of Experimental Medicine, 1992, 176(4):1007-1013.
[109] Madden D R. The 3-dimensional structure of peptde-MHC complexes[J]. Annual Review of Immunology, 1995, 13: 587-622.
[110] Cardenas C, Ortiz M, Balbin A, et al. Allele effects in MHC-peptide interactions:A theoretical analysis of HLA-DR beta l*0101-HA and HLA-DR beta l*0401-HA complexes[J].Biochemical and Biophysical Research Communications, 2005, 330(4): 1162-1167.
[111] Sundberg E J, Sawicki M W, Southwood S, et al. Minor structural changes in a mutated human melanoma antigen correspond to dramatically enhanced stimulation of a CD4(+) tumor-infiltrating lymphocyte line[J]. Journal of Molecular Biology, 2002, 319(2):449-461.
[112] Rosloniec E F, Ivey R A, Whittington K B, et al. Crystallographic structure of a rheumatoid arthritis MHC susceptibility allele, HLA-DR1 (DRBI*0101), complexed with the immunodominant determinant of human type II collagen[J]. Journal of Immunology, 2006,177(6): 3884-3892.
[113] Busch R, Hill CM, Hayball J D, et al. Effect of natural polymorphism at residue-86 of the HLA-DR beta-chain on peptide binding[J]. Journal of Immunology, 1991, 147(4):1292-1298.
[114] Newtonnash D K, Eckels D D. Differential effect of polymorphism at HLA-DR1 beta-chain position-85 and position-86 on binding and recognition of DR1-restricted antigenic peptides[J]. Journal of Immunology, 1993, 150(5): 1813-1821.
[115] Hill C M, Liu A, Marshall K W, et al. Exploration of requirements for peptide binding to HLA DRBl*0101 and DRB1*0401[J]. Journal of Immunology, 1994, 152(6) : 2890-2898.
[116] Jardetzky T S, Gorga J C, Busch R, et al. Peptide binding to HLA-DR1 - a peptide with most residues substituted to alanine retains MHC binding[J]. Embo Journal, 1990, 9(6):1797-1803.
[117] Murthy V L, Stern L J. The class II MHC protein HLA-DR1 in complex with an endogenous peptide: implications for the structural basis of the specificity of peptide binding[J]. Structure, 1997, 5(10): 1385-1396.
[118] Vargas L E, Parra C A, Salazar L M, et al. MHC allele-specific binding of a malaria peptide makes it become promiscuous on fitting a glycine residue into pocket 6[J].Biochemical and Biophysical Research Communications, 2003, 307(1): 148-156.
[119] Fremont D H, Hendrickson W A, Marrack P, et al. Structures of an MHC class II molecule with covalently bound single peptides[J]. Science, 1996, 272(5264): 1001-1004.
[120] Wilson N, Fremont D, Marrack P, et al. Mutations changing the kinetics of class II MHC peptide exchange[J]. Immunity, 2001, 14(5): 513-522.
[121] Zavala-Ruiz Z, Sundberg E J, Stone J D, et al. Exploration of the P6/P7 region of the peptide-binding site of the human class II major histocompatability complex protein HLA-DR1[J]. Journal of Biological Chemistry, 2003, 278(45): 44904-44912.
[122] Cardenas C, Villaveces J L, Bohorquez H, et al. Quantum chemical analysis explains hemagglutinin peptide-MHC class II molecule HLA-DR beta I*0101 interactions[J].Biochemical and Biophysical Research Communications, 2004, 323(4): 1265-1277.
[123] Zavala-Ruiz Z, Strug I, Anderson M W, et al. A polymorphic pocket at the P10 position contributes to peptide binding specificity in class II MHC proteins[J]. Chemistry& Biology, 2004, 11(10): 1395-1402.