免疫亲和质谱法研究／β2-微球蛋白抗原表位

采用免疫亲和分离与质谱分析相结合的方法,对β2-微球蛋白抗原表位进行了系统研究.完整的抗原分子和已固定在载体(CNBr-activated Sepharose beads)上的单克隆抗体发生免疫亲和反应后,用Endoproteinase Glu-C,Trypsin,AminopeptidaseM和carboxypeptidase Y四种不同的蛋白酶依次酶解抗原分了,并采用基质辅助激光解析电离飞行时间质谱(MALDI-TOF-MS)技术对与抗体连接受保护而未发生水解的肽段进行了研究.结果表明:β2-微球蛋白抗原表位位于整个蛋白分了氨基酸序列的61～67位,即为SFYLLYY.通过合成肽段的分析,证明了SFYLLYY即为抗原表位,与亲和质谱方法分析结果一致.     

免疫亲和质谱法研究β2-微球蛋白抗原表位

采用免疫亲和分离与质谱分析相结合的方法, 对β2-微球蛋白抗原表位进行了系统研究. 完整的抗原分子和已固定在载体(CNBr-activated Sepharose beads)上的单克隆抗体发生免疫亲和反应后, 用Endoproteinase Glu-C, Trypsin, Aminopeptidase M和carboxypeptidase Y四种不同的蛋白酶依次酶解抗原分子, 并采用基质辅助激光解析电离飞行时间质谱(MALDI-TOF-MS)技术对与抗体连接受保护而未发生水解的肽段进行了研究. 结果表明: β2-微球蛋白抗原表位位于整个蛋白分子氨基酸序列的61～67位, 即为SFYLLYY. 通过合成肽段的分析, 证明了SFYLLYY即为抗原表位, 与亲和质谱方法分析结果一致.     

基于SVM方法分析蛋白酶体裂解位点的特异性

真核细胞中的泛素-蛋白酶体系统在抗原肽加工呈递途径中发挥着重要作用. 为了进一步研究蛋白酶体的酶切位点特异性, 采用支持向量机(support vector machine, SVM)方法建立了蛋白酶体的酶切位点预测模型. 在相同检验集下, 将本模型性能指标与其他预测模型方法比较, 结果是本模型性能指标优先, 预测准确度为83.1%. 由样本数据相应氨基酸对酶切位点形成的权重系数, 得出蛋白酶体酶切位点及其两侧区域氨基酸的裂解特异性, 反映了蛋白酶体裂解抗原蛋白的相互作用信息, 这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.     

基于高维特征非线性筛选的HLA-A*0201限制性CTL表位预测

高活性细胞毒T细胞(CTL)表位鉴定是设计肿瘤疫苗的关键内容.采用天然氨基酸的531个物理化学性质参数表征HLA-A^*0201限制性表位9肽, 从531×9个初始描述子出发, 经二元矩阵重排过滤器粗筛和多轮末尾淘汰精细筛选, 获得18个物理化学意义明确的保留描述子. 18个保留描述子主要涉及除1位、5位外各位置残基的疏水性和空间结构特征, 3位残基疏水性对活性影响最大, 且2位、4位、9位残基共占10个保留描述子,支持2位和9位残基为锚点、3位为关键位点以及4位残基为标志链的现有认知. 对18个保留描述子以支持向量回归构建定量序效模型,其拟合、留一法交叉验证决定系数R²、Q_cv²分别为0.957、0.708; 独立预测决定系数及均方根误差Q_ext² 、RMSE_ext分别为0.818、0.366, 明显优于文献报道. 通过对全组合虚拟9肽的预测, 得到了多条预测活性高于已知表位肽的9肽, 可供实验验证. 较全面阐明了特定位置残基对多肽亲和性的影响规律, 为高活性多肽疫苗分子设计提供了切实指导.     

后叶催产素的定量构效关系研究

基于氨基酸物化性质的描述子矢量VHSE, 对21个后叶催产素类似物进行结构表征. 经逐步回归与偏最小二乘相结合的变量筛选技术, 根据模型的外部预测结果, 筛选得到一个最优的9变量组合. 应用该变量组合对21个后叶催产素类似物的促宫缩活性进行偏最小二乘建模, 模型复相关系数R2为92.6%, 留一法和留组法交互验证Q2分别为78.3%和79.4%. 结果表明, 后叶催产素的促宫缩活性主要与第3号氨基酸残基的疏水性、立体结构和电性性质以及第8号氨基酸残基的电性特征密切相关.     

基于VHSE结构表征的蛋白酶体酶切位点预测及酶切特异性研究

泛素-蛋白酶体在真核生物的抗原呈递、细胞周期调控和转录因子激活等生理过程中发挥着极为重要的作用,其核心就是蛋白酶体对底物的选择性酶切作用,因此对选择性酶切位点的预测一直是计算生物学的一个重点研究内容.针对现有酶切位点预测方法的非线性和物理意义不明确等问题,借鉴定量构效关系研究方法,采用基于氨基酸物理化学性质的描述子——VHSE(Principal component score vector of hydrophobic,steric,and electronic properties)对收集的2650个MHC-I配体的源蛋白序列进行了结构表征,在此基础上利用支持向量机建立了蛋白酶体酶切位点的预测模型,其最优线性模型的灵敏度(Sensitivity)、特异性(Specificity)、接受者操作特征曲线下面积(area under receiver operatingcharacteristics curve,AUC)和马休斯相关系数(Matthews coefficient of correlation,MCC)分别为90.18%,69.63%,0.8797和0.6131.模型分析结果表明:影响酶切位点选择性的氨基酸性质由大到小依次为:疏水性、电性和立体特征;P9,P8,P4,P1,P3’,P4’和P5’位氨基酸对酶切位点的选择有重要影响,研究亦显示酶切位点上游P1位和下游P1’～P5’的"疏水势差"有利于蛋白酶体的切割作用.     

抗原肽与MHC分子相互作用QM/MM分子动力学模拟研究

在MHC I类(major histocompatibility complex class I)分子抗原加工提呈过程中抗原蛋白在抗原提呈细胞(antigen-presenting cells, APC)的胞浆中被蛋白酶体(proteasome)裂解成短肽peptide, 由转运相关蛋白(transporter associated with antigen processing, TAP)将蛋白酶体裂解产生的短肽片段从胞浆转运至内质网腔. 短肽peptide在内质网中与新生成的MHC I类分子结合, 形成peptide-MHC复合体被提呈到APC细胞表面, 与T细胞表面抗原受体(T cell receptor, TCR)特异性识别结合, 使得CTL细胞开始活化、增殖、分化, 进而对肿瘤细胞进行特异性杀伤. 目前对CTL细胞如何识别抗原肽-MHC复合物分子及抗原短肽peptide如何与主要组织相容性复合体MHC分子的相互作用识别结合的机理还不是很清楚. 传统的预测CTL细胞表位的方法没有考虑受体与配体结合过程中电子结构的变化, 电子结构的变化需要用量子力学方法来处理. 本文采用QM/MM多尺度生物大分子的分子动力学模拟方法, 以天然抗原肽TAX (LLFGYPVYVYU)与HLA-A*0201分子结合的晶体结构为模板, 替换抗原肽“锚点”氨基酸, 将口袋氨基酸残基的原子极化电荷在空间形成的静电势用电多极矩分量表示. 用箱线图分析每个口袋氨基酸分子静电势变化和功能, 确定Pocket B的Glu63和Lys66的功能是精细识别氨基酸和一级结合氨基酸, Pocket F的Asp77, Tyr84的功能是精细识别氨基酸, 而Asp77, Lys146是一级结合氨基酸, 表明QM/MM方法在提取抗原肽与MHC I类分子识别结合特异性信息是可行的, 这对了解免疫识别机理和指导肿瘤疫苗的开发都具有指导意义.     

基于岭回归和SVM的高维特征选择与肽QSAR建模

岭回归估计权重绝对值在一定程度上体现了对应特征作用大小, 据此发展了基于岭回归(RR)和支持向量机(SVM)的高维特征选择算法. 对苦味二肽(BTT)和细胞毒性T淋巴细胞(CTL)表位9 肽两个肽体系, 以氨基酸的531 个物理化学性质参数直接表征肽结构, 各获得1062、4779 个初始特征; 对训练集, 初始特征以岭回归排序后序贯引入, 当SVM留一法交叉测试(LOOCV)的均方误差(MSE)显著上扬时终止, 最后以多轮末尾淘汰进一步精筛, 分别获得7、18个物理化学意义明确的保留特征. 基于保留特征与支持向量回归(SVR), 对训练集建立定量构效关系(QSAR)模型, 预测独立测试集, 其拟合精度、留一法交叉测试精度、独立预测精度均优于现有文献报道结果. 新方法运行速度快, 选取的特征物理化学意义明确, 解释性强, 在肽、蛋白质定量构效关系建模等高维数据回归预测领域有较广泛应用前景.     

中国海南省恶性疟原虫P190抗原变异的研究

本文应用双脱氧末端终止法测定了我国海南省恶性疟原虫FCC1/NH株P190抗原基因的部分DNA序列,共计2103个碱基对。与该基因现有资料比较,我们发现了该虫株P190基因的以下特征:(1)在核苷酸第2323至2340位间有18bp长的片段缺失,此缺失正位于该抗原一个重要T细胞表位和T-E-X三肽重复序列处。(2)该基因属MAD20变异型,但从3′端5013位起转变为K1型序列,表明在该处发生了基因内重组。(3)在N端三肽重复区为高度变异区,但该基因却与MAD20型序列呈现高度同源性。(4)除了缺失和5个碱基点突变外,其余序列符合双态性变异规则。本工作为P190疟疾疫苗的发展提供了可靠依据。     

一种新多肽表征方法及支持向量机用于肽HPLC定量结构-保留建模预测

从20种天然氨基酸的1369种性质参数经主成分分析得出一种新多肽序列表征方法——SZOTT. 将其用于71个不同长度肽序列表征, 以偏最小二乘(PLS)和支持向量机(SVM)建立定量结构-保留模型(QSRM). 研究表明, SZOTT能够较好表征71个肽序列特征, 其含信息量大且易操作, 与PLS相比, SVM对lgk建模预测表现出较强的拟合能力和良好外部预测能力, SZOTT表征方法和SVM建模可进一步用于肽HPLC保留行为研究.     

Computational analysis on the binding of epitope peptide to human leukocyte antigen class I molecule A*2402 subtype

Immunological response induced by small amino peptide has attracted much recent attention in the field of immunotherapy. Wilms' tumor (WT1) protein is one of the potent tumor antigens inducing immunological response in mouse and human, because WT1 is over expressed in many types of leukemia and various kinds of solid tumors. A 9-mer peptide encoded in WT1 protein (CMTWNQMNL; amino acid 235-243) is known to serve as antigenic peptide for human leukocyte antigen (HLA)-A*2402 molecule. It was reported that the replacement of the second amino residue, which is deeply responsible for the peptide binding to HLA, induced strong immunological response compared to the natural peptide. In this study, 19 kinds of single amino substitutions were introduced at position 2 of this 9-mer WT1 peptide. We performed molecular dynamics simulation on the complex of each of WT1 epitope peptides and HLA-β2 micro globulin (β2m) molecule, and subsequently estimated the binding affinity using molecular mechanics/generalized-Born surface area method combined with normal mode analysis. Our computation indicated that the peptide containing M2Y or M2W mutation showed high binding affinity to the HLA-β2m molecule as well as the natural peptide. We have also examined the role of the residue at position 2 in peptide binding to HLA-β2m. The calculation showed that van der Waals interaction between the side chain of the residue at position 2 and hydrophobic residues inside B-pocket of HLA are important. These findings will be helpful to search other potent peptides that will enhance strong immunological response specific to HLA-A*2402 molecule.     

SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences

At present, there are a number of methods for the prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides. Despite numerous methods for predicting T-cell epitopes, there still exist limitations that affect the reliability of prevailing methods. For this reason, the development of models with high accuracy are crucial. An accurate prediction of the peptides that bind to specific major histocompatibility complex class I and II (MHC-I and MHC-II) molecules is important for an understanding of the functioning of the immune system and the development of peptide-based vaccines. Peptide binding is the most selective step in identifying T-cell epitopes. In this paper, we present a new approach to predicting MHC-binding ligands that takes into account new weighting schemes for position-based amino acid frequencies, BLOSUM and VOGG substitution of amino acids, and the physicochemical and molecular properties of amino acids. We have made models for quantitatively and qualitatively predicting MHC-binding ligands. Our models are based on two machine learning methods support vector machine (SVM) and support vector regression (SVR), where our models have used for feature selection, several different encoding and weighting schemes for peptides. The resulting models showed comparable, and in some cases better, performance than the best existing predictors. The obtained results indicate that the physicochemical and molecular properties of amino acids (AA) contribute significantly to the peptide-binding affinity.     

Design, optimization and evaluation of specific affinity adsorbent for oligopeptides

A major challenge in the development of affinity adsorbents is the design of specific adsorbents for target molecules. In this paper, a two-step strategy was used to design a specific adsorbent for oligopeptides. Based on the structural characteristics of target peptide DFLAE (DE5), the affinity ligand CDenHis bearing hydrophobic inclusion and electrostatic interaction sites was prepared by grafting histidine onto β-cyclodextrin (CD) using ethylenediamine; ligands with single hydrophobic inclusion or electrostatic interaction sites (CDen and HisOMe) were used as reference ligands. Results indicated that the binding affinity (K(a)) of CDenHis with DE5 was 6.23×10(4)M(-1), 23- and 61-fold higher than that of CDen and HisOMe, respectively. Computer simulations were used to further optimize the steric configuration of CDenHis. It was found that the optimized ligand CDdnHis exhibited a much improved binding affinity for DE5 (K(a)=1.02×10(5)M(-1)). Moreover, the corresponding adsorbent A-CDdnHis not only showed much better adsorption ability compared with A-CDenHis, but also excellent adsorption specificity for DE5-containing peptides. Kinetic analysis and adsorption mechanism studies suggested that the configuration matching of CDdnHis with DE5 and the cooperation of multiple interactions led to the fast and selective adsorption of DE5-containing peptides to A-CDdnHis.     

氨基酸描述子VHSEH用于多肽定量序效建模研究

从20 种天然氨基酸的171个物化性质出发, 按照疏水、立体和电性特征及氢键贡献将其分类后, 分别进行主成分分析, 得到一个新描述子VHSEH(Principal component score vector of hydrophobic, steric, electronic properties and, hydrogen bonds contributions). 对后叶催产素的结构进行了表征, 并以偏最小二乘法及D-优化划分样本建立了PLS定量序效关系模型, 得到复相关系数R2分别为 0.958 和 0.957, Q2分别为0.903和0.845, 约高于VHSE描述子模型值; 对抗菌肽进行了结构表征, 建立了PLS和OSC-PLS模型, 其R2分别为0.84和 0.995, Q2分别为0.546和0.926, 较SZOTT描述子结果好; 对58 个血管紧张素转化酶抑制剂进行QSAM研究, 得到R2, Q2及RMS分别为0.877, 0.838和0.361. 研究结果表明, VHSEH 描述子信息量大, 物化意义明确, 结果更易解释.     

Tailoring GaN semiconductor surfaces with biomolecules

Functionalization of semiconductors constitutes a crucial step in using these materials for various electronic, photonic, biomedical, and sensing applications. Within the various possible approaches, selection of material-binding biomolecules from a random biological library, based on the natural recognition of proteins or peptides toward specific material, offers many advantages, most notably biocompatibility. Here we report on the selective functionalization of GaN, an important semiconductor that has found broad uses in the past decade due to its efficient electroluminescence and pronounced chemical stability. A 12-mer peptide ("GaN_probe") with specific recognition for GaN has evolved. The subtle interplay of mostly nonpolar hydrophobic and some polar amino acidic residues defines the high affinity adhesion properties of the peptide. The interaction forces between the peptide and GaN are quantified, and the hydrophobic domain of the GaN_probe is identified as primordial for the binding specificity. These nanosized binding blocks are further used for controlled placement of biotin-streptavidin complexes on the GaN surface. Thus, the controlled grow of a new, patterned inorganic-organic hybrid material is achieved. Tailoring of GaN by biological molecules can lead to a new class of nanostructured semiconductor-based devices.     

Endo- and aminopeptidase activities of rat cathepsin H.

Employing soluble denatured protein substrates and their derivatives, the proteolytic activity of rat cathepsin H was investigated. The enzyme showed aminopeptidase activity which sequentially released amino acid from the N-terminal of the substrate. The aminopeptidase activity did not act on N alpha-acetylated peptides and showed moderate ionic-strength dependence when methionyl-methylcoumarylamide was employed as a substrate. These results indicate that the activity essentially requires an N-terminal free amino group of the substrate and recognizes it electrostatically to some extent. On the other hand, the enzyme was also indicated to exhibit endopeptidase activity by employing appropriate N alpha-acetylated peptide substrates. In contrast to the aminopeptidase activity, the endopeptidase activity showed rather strict specificity, preferring hydrophobic residues at P2 and P3 sites. Because of the broad specificity and high efficiency of the aminopeptidase activity, it was difficult to directly observe endopeptidase activity in the digestion of large peptide substrates with a free alpha-amino terminal. Thus, this is the first experimental evidence that indicates endopeptidase activity by assigning internal peptide bonds cleaved by this activity. From this data, we proposed a model of the binding site of this enzyme.