B细胞表位预测方法研究进展

B细胞抗原表位预测方法的研究对基础免疫学的研究及实际应用有着重要的意义。本文归纳了理论预测B细胞表位的常用方法,并对目前预测B细胞表位方法存在的问题进行了分析。     

乳房链球菌GapC蛋白的表达及其B细胞抗原表位的预测与鉴定

乳房链球菌Streptococcus uberis的GapC蛋白是一种位于该菌表面的具有甘油醛-3-磷酸脱氢酶活性的蛋白,其参与细胞活动,表现出多种生物学活性,此外还具有良好的抗原性。文中旨在对乳房链球菌GapC蛋白可能的B细胞抗原表位进行预测,分析和验证候选表位肽的免疫原性。利用S. uberis分离株RF5-1克隆gapC基因,构建重组表达质粒pET-28a-GapC,诱导表达GapC重组蛋白,并以纯化蛋白免疫家兔,获得抗GapC多抗。利用生物信息学软件预测并分析GapCB细胞抗原表位的三维结构和空间位置及对GapC蛋白及表位的同源性比较。结果表明,表达纯化了44kDa的GapC蛋白具有良好的反应性。利用表位预测软件筛选并合成针对S.uberisGapC蛋白的6个线性和3个构象优势B细胞表位多肽,三维结构的分析显示,筛选的多肽具有良好的抗原表位形成条件。以纯化的S.uberis GapC蛋白免疫家兔制备多抗,通过间接ELISA对抗原表位进行鉴定。ELISA检测结果显示,9条抗原表位肽均可不同程度地与抗GapC多抗反应,其中表位266AANDSYGYTEDPIVSSD282与多抗反应...     

蛋白质抗原表位研究进展

本文综述了蛋白质抗原表位的种类及特性,回顾了近几年来实验确定和理论预测B细胞蛋白质抗原表位的常用方法,介绍了表位作图和表位疫苗的研究现状。     

EB病毒潜伏膜蛋白1的B细胞表位预测

目的 预测EB病毒潜伏膜蛋白1(Latent Membrane Protein 1,LMPl)的B细胞表位.方法 基于EB病毒基因组序列,采用DNAStar Lasergene软件包中的Protean软件,对LMP1的亲水性,表面可能性,抗原指数及其二级结构中的柔性区域进行分析,并结合吴玉章的抗原指数预测法预测其B细胞表位.结果 B细胞表位最有可能位于潜伏膜蛋白N端第356-358,2-19,249-314区段或其附近,而潜伏膜蛋白N端第185-223区段内或附近也可能存在B细胞表位.结论 用多参数预测EB病毒LMP1的B细胞表位,为鼻咽癌的筛查及抗肿瘤转移靶向治疗的分子免疫学研究奠定基础.     

构象性B细胞表位预测的免疫信息学方法及其网络资源

抗原表位预测是免疫信息学研究的重要方向之一,可以给实验提供重要的线索。B细胞表位或抗原决定簇是抗原中可被B细胞受体或抗体特异性识别并结合的部位。实际上,近90%的B细胞表位是构象性的。即使抗原蛋白质三级结构已知,B细胞表位预测仍然是一大挑战。该文结合实例阐述当今主要的构象性B细胞表位预测方法和算法:机器学习预测、非机器学习的计算预测、基于噬菌体展示数据的识别方法,以及一些也可用于构象性B细胞表位预测的通用蛋白质-蛋白质界面预测方法;介绍最新相关预测软件和Web服务资源,说明未来的研究趋势。     

HIV-1辅助蛋白Vpr多肽抗体的制备与鉴定

预测Vpr蛋白的B细胞抗原表位,并利用合成的B细胞表位肽制备Vpr特异性抗体。应用生物信息学技术获得Vpr蛋白共享氨基酸序列并预测其潜在B细胞抗原表位,与载体蛋白血蓝蛋白(KLH)偶联合成多肽并免疫家兔,鉴定及纯化获得的多肽特异性抗体。软件预测显示,Vpr蛋白N端的第3～19位(N)和C端的第82～95位(C)氨基酸序列为潜在B细胞抗原表位;ELISA检测抗血清中多肽特异性抗体的效价都达到1:105以上;Western-Blotting结果显示,无论对HIV-1B亚型还是CRF07_BC重组型的Vpr蛋白,其多肽N抗体和C抗体均能特异性识别;免疫沉淀结果显示,Vpr多肽N和C抗体也能特异性结合未变性的野生型Vpr或GFP-Vpr融合蛋白。利用生物信息学技术能成功预测Vpr蛋白B细胞抗原表位,免疫所获得的抗体具有较好的特异性和应用性。     

抗原表位的研究方法及口蹄疫病毒抗原表位的研究进展

本文综述了近几年来用于B细胞表位及T细胞表位研究的常用方法及其在口蹄疫病毒抗原表位研究中的应用,并介绍了口蹄疫病毒抗原表位的研究进展.     

A型肉毒毒素Hc片段B细胞表位预测

应用4种参数和方法对A型肉毒毒素Hc片段进行综合分析,包括亲水性参数、可及性参数、抗原性参数和二级结构预测等方法,预测A型肉毒毒素Hc片段(BoNT/A-Hc)氨基酸序列的B细胞表位,结果显示B细胞表位可能位于其Hc片段残基第37-42、294-300、355-359、400-411等区域内或附近.分析预测结果将有助于确定BoNT/A-Hc的B细胞表位.     

副溶血性弧菌外膜蛋白K的B细胞线性表位预测

目的预测副溶血性弧菌外膜蛋白K(OmpK)的B细胞线性表位。方法 NCBI下载已登录的OmpK的基因序列,对其进行生物信息学分析,应用DNAStar protean软件综合分析OmpK蛋白的二级结构、柔性、表面可能性、亲水性和抗原指数等多种参数,预测其B细胞线性表位。结果 OmpK蛋白的优势B细胞线性表位位于肽链的第7-13、25-36、63-69、140-147、182-188、234-239区段。结论预测得到OmpK蛋白的6个优势B细胞线性表位,为进而克隆表达串联表位蛋白,研制副溶血性弧菌多表位疫苗奠定基础。     

基于深度最大输出网络的抗体种型特异性B细胞表位预测

B细胞表位研究有助于肽段疫苗研制,抗体研制以及疾病诊断和治疗研究.不同的B细胞表位诱导免疫系统产生不同的抗体种型,探索研究能够诱导特异性抗体产生的B细胞表位具有重要意义.基于二肽组成特征,利用深度最大输出网络算法训练构建三个二类分类器,分别对应诱导三种不同特异性抗体的B细胞表位,即IgA表位,IgE表位以及IgG表位.通过五折交叉验证训练和测试这三个分类器,获得AUC的值分别为0.78,0.93以及0.78.IgA表位和IgE表位分类器的预测能力优于其它IgA表位和IgE表位分类器,IgG表位分类器和其它IgG表位分类器的预测能力相当.     

A meta-learning approach for B-cell conformational epitope prediction

Background

One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains.

Results

We developed the hierarchical meta-learning architectures based on stacked and cascade generalizations. The bottom level of the hierarchy consisted of four conformational and four linear epitope prediction tools that served as the base learners. To perform consistent and unbiased comparisons, we tested the meta-learning method on an independent set of antigen proteins that were not used previously to train the base epitope prediction tools. In addition, we conducted correlation and ablation studies of the base learners in the meta-learning model. Low correlation among the predictions of the base learners suggested that the eight base learners had complementary predictive capabilities. The ablation analysis indicated that the eight base learners differentially interacted and contributed to the final meta model. The results of the independent test demonstrated that the meta-learning approach markedly outperformed the single best-performing epitope predictor.

Conclusions

Computational B-cell epitope prediction tools exhibit several differences that affect their performances when predicting epitopic regions in protein antigens. The proposed meta-learning approach for epitope prediction combines multiple prediction tools by integrating their complementary predictive strengths. Our experimental results demonstrate the superior performance of the combined approach in comparison with single epitope predictors.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0378-y) contains supplementary material, which is available to authorized users.     

Characterization of a partially protective B-cell epitope within the 62 kDa antigen of Schistosoma japonicum

Approximately 200 million people worldwide currently suffer from schistosomiasis, one of the most important human parasitic diseases. Although an established infection can be treated with anthelminthics and praziquantel, vaccination would be the ideal method for integral control of schistosomiasis. Schistosoma mansoni IrV-5, recommended as a vaccine candidate by the World Health Organization/Special Programme for Research and Training in Tropical Diseases, produced high protection in animal models. We therefore focused on its homolog, the Schistosoma japonicum 62 kDa antigen, and analyzed it using B cell/antibody- related databases and analysis tools for the prediction of B-cell epitopes. Epitope B3 was selected for further investigation. Experiments using a murine model indicated that mice immunized with B3 resulted in lymphocytes proliferation and produced high levels of specific immunoglobulin G and GI, but did not produce impressive cytokines. The vaccination showed partial protective immunity, measured by worm burden and anti-fecundity immunity against S. japonicum. These results indicated that the epitope B3 from S. japonicum 62-kDa antigen might act as a candidate immunogen for future epitope vaccine investigation.     

In silico epitope-based vaccine design against influenza a neuraminidase protein: Computational analysis established on B- and T-cell epitope predictions

ObjectiveInfluenza A virus belongs to the most studied virus and its mutant initiates epidemic and pandemics outbreaks. Inoculation is the significant foundation to diminish the risk of infection. To prevent an incidence of influenza from the transmission, various practical approaches require more advancement and progress. More efforts and research must take in front to enhance vaccine efficacy.MethodsThe present research emphasizes the development and expansion of a universal vaccine for the influenza virus. Research focuses on vaccine design with high efficacy. In this study, numerous computational approaches were used, covering a wide range of elements and ideas in bioinformatics methodology. Various B and T-cell epitopic peptides derived from the Neuraminidase protein N1 are recognized by these approaches. With the implementation of numerous obtained databases and bioinformatics tools, the different immune framework methods of the conserved sequences of N1 neuraminidase were analyzed. NCBI databases were employed to retrieve amino acid sequences. The antigenic nature of the neuraminidase sequence was achieved by the VaxiJen server and Kolaskar and Tongaonkar method. After screening of various B and T cell epitopes, one efficient peptide each from B cell epitope and T cell epitopes was assessed for their antigenic determinant vaccine efficacy. Identical two B cell epitopes were recognized from the N1 protein when analyzed using B-cell epitope prediction servers. The detailed examination of amino acid sequences for interpretation of B and T cell epitopes was achieved with the help of the ABCPred and Immune Epitope Database.ResultsComputational immunology via immunoinformatic study exhibited RPNDKTG as having its high conservancy efficiency and demonstrated as a good antigenic, accessible surface hydrophilic B-cell epitope. Among T cell epitope analysis, YVNISNTNF was selected for being a conserved epitope. T cell epitope was also analyzed for its allergenicity and cytotoxicity evaluation. YVNISNTNF epitope was found to be a non-allergen and not toxic for cells as well. This T-cell epitope with maximum world populace coverages was scrutinized for its association with the HLA-DRB1*0401 molecule. Results from docking simulation analyses showed YVNISNTNF having lower binding energy, the radius of gyration (Rg), RMSD values, and RMSE values which make the protein structure more stable and increase its ability to become an epitopic peptide for influenza virus vaccination.ConclusionsWe propose that this epitope analysis may be successfully used as a measurement tool for the robustness of an antigen–antibody reaction between mutant strains in the annual design of the influenza vaccine.     

Immunoinformatics comes of age

With the burgeoning immunological data in the scientific literature, scientists must increasingly rely on Internet resources to inform and enhance their work. Here we provide a brief overview of the adaptive immune response and summaries of immunoinformatics resources, emphasizing those with Web interfaces. These resources include searchable databases of epitopes and immune-related molecules, and analysis tools for T cell and B cell epitope prediction, vaccine design, and protein structure comparisons. There is an agreeable synergy between the growing collections in immune-related databases and the growing sophistication of analysis software; the databases provide the foundation for developing predictive computational tools, which in turn enable more rapid identification of immune responses to populate the databases. Collectively, these resources contribute to improved understanding of immune responses and escape, and evolution of pathogens under immune pressure. The public health implications are vast, including designing vaccines, understanding autoimmune diseases, and defining the correlates of immune protection.     

Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools

A B-cell epitope is the three-dimensional structure within an antigen that can be bound to the variable region of an antibody. The prediction of B-cell epitopes is highly desirable for various immunological applications, but has presented a set of unique challenges to the bioinformatics and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington, DC to discuss the current state of the B-cell epitope prediction field. Many of the currently available tools were surveyed and a set of recommendations was devised to facilitate improvements in the currently existing tools and to expedite future tool development. An underlying theme of the recommendations put forth by the panel is increased collaboration among research groups. By developing common datasets, standardized data formats, and the means with which to consolidate information, we hope to greatly enhance the development of B-cell epitope prediction tools.     

Computational and Experimental Validation of B and T-Cell Epitopes of the In Vivo Immune Response to a Novel Malarial Antigen

Vaccine development efforts will be guided by algorithms that predict immunogenic epitopes. Such prediction methods rely on classification-based algorithms that are trained against curated data sets of known B and T cell epitopes. It is unclear whether this empirical approach can be applied prospectively to predict epitopes associated with protective immunity for novel antigens. We present a comprehensive comparison of in silico B and T cell epitope predictions with in vivo validation using an previously uncharacterized malaria antigen, CelTOS. CelTOS has no known conserved structural elements with any known proteins, and thus is not represented in any epitope databases used to train prediction algorithms. This analysis represents a blind assessment of this approach in the context of a novel, immunologically relevant antigen. The limited accuracy of the tested algorithms to predict the in vivo immune responses emphasizes the need to improve their predictive capabilities for use as tools in vaccine design.     

B细胞表位定位的研究进展

B细胞抗原表位的研究对免疫原性多肽和新型疫苗分子的设计都起着指导作用,同时也有利于诊断试剂的开发以及临床疾病的诊断。本文综述了近年来实验确定和理论预测B细胞蛋白质抗原表位的常用方法,以及B细胞抗原表位分析的研究方法。     

B细胞表位作图研究进展

抗原-抗体的特异性结合是由抗体表面的抗原决定簇与抗原表面的表位基序间的特异性互补识别决定的。B细胞表位作图既包括B细胞抗原表位基序的鉴定(即确定抗原分子上被B细胞表面受体或抗体特异性识别并结合的氨基酸基序),也包括绘制抗原蛋白的全部或接近全部的B细胞表位基序在其一级或高级结构上的分布图谱的过程。B细胞表位作图是研发表位疫苗、治疗性表位抗体药物和建立疾病免疫诊断方法的重要前提。目前,已经建立了多种B细胞表位鉴定或绘制抗原蛋白B细胞表位图谱的实验方法。基于抗原-单抗复合物晶体结构的X-射线晶体学分析的B细胞表位作图和基于抗原蛋白或抗原片段的突变体库筛选技术的B细胞表位作图可以在氨基酸水平,甚至原子水平上揭示抗原分子上与单抗特异性结合的关键基序;其它B细胞表位作图方法(如基于ELISA的肽库筛选技术)常常只能获得包含B细胞表位的抗原性肽段,因而,很少用于最小表位基序的鉴定;而改良的生物合成肽法多用于B细胞表位的最小基序鉴定和精细作图。鉴于每种B细胞作图方法都存在各自的优势与不足,B细胞表位作图往往需要多种作图方法的有机结合。本文对目前常用的B细胞表位作图的实验方法及其在动物疫病防控中的应用进行综述,以期为研究者设计最佳的表位作图方案提供参考。     

Arg77和Glu80定点突变同时去除葡激酶中的T和B细胞抗原表位

【目的】对葡激酶的T和B细胞抗原表位重叠的关键氨基酸Arg77和Glu80进行定点突变以降低葡激酶的免疫原性。【方法】基于Arg77和Glu80的溶剂可及表面积设计葡激酶的突变体;突变体在大肠杆菌DH5α中进行表达。经过三步层析法纯化后,分析突变体的纤溶活性和免疫原性。【结果】免疫学实验提示,葡激酶导致Th2免疫反应;Glu80突变为丙氨酸和丝氨酸减少了溶剂可及表面积,同时去除了部分T和B细胞抗原表位;Arg77突变为天冬酰胺、谷氨酰胺和赖氨酸仅去除了部分T细胞抗原表位;6个组合突变体中,Sak(R77Q/E80A)和Sak(R77Q/E80S)有效去除了部分B和T细胞抗原表位,降低了葡激酶的免疫原性;Sak(R77Q/E80A)and Sak(R77Q/E80S)的纤溶活性和催化效率与r-Sak相当。