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。     

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细胞表位的计算机预测

B细胞表位预测对于多种免疫学研究是必不可少的重要工具.本文概括了计算机预测B细胞表位的现状,汇总了多种表位预测工具及其原理和应用,介绍了一些用于建立与评价预测工具的数据库和数据集,对各类预测工具和数据库的特点和网址进行整理列表.另外,还分析了B细胞表位预测领域存在的问题,并对其未来发展提出了建议.     

白念珠菌细胞壁蛋白Csp37抗原表位预测

目的预测白念珠菌细胞壁蛋白Csp37的抗原表位,分析其作为疫苗靶点的免疫原性。方法采用生物信息学方法对Csp37蛋白的抗原表位进行预测,利用ProtParam网络服务器分析蛋白基本理化性质,SignaIP 3.0预测信号肽,TMHMM软件预测跨膜区,GOR4在线分析蛋白二级结构,DNAStar预测分析亲水性、可塑性、表面可及性和氨基酸抗原指数,使用在线工具ABCPred预测B细胞抗原表位,Syfpeithi预测T细胞抗原表位。最后,综合分析B细胞和T细胞共有抗原表位。结果预测白念珠菌细胞壁蛋白Csp37的B细胞表位9个和T细胞表位8个,以及共有的优势抗原表位5个,共同优势区域为:45-48,76-78,153-158,222-225,303-305位氨基酸。结论白念珠菌细胞壁蛋白Csp37含有丰富的抗原表位,具有诱导细胞免疫应答和体液免疫应答的潜能,可以作为疫苗研究的新靶点。     

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.     

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。     

重组牛乳源金黄色葡萄球菌GapC蛋白优势B细胞抗原表位的预测和筛选

旨在表达牛乳源金黄色葡萄球菌(Staphylococcus aureus)GapC蛋白并对其B细胞抗原表位进行预测与鉴定，本研究利用实验室分离鉴定的S. aureus分离株15119扩增GapC基因并构建重组表达质粒pET-28a-GapC，诱导纯化得到分子量为44 kD重组蛋白GapC，以此免疫新西兰大白兔，获得特异多克隆抗体。利用生物信息学方法，对GapC蛋白的二级及三级结构进行分析，预测其B细胞抗原表位，并利用特异性抗体对筛选的表位进行鉴定。结果表明，GapC蛋白具有良好的免疫原性，筛选出7个线性B细胞抗原表位，利用兔抗重组GapC蛋白多克隆抗体鉴定得到了PL 5(²²¹ IPEIDGKLDGGAQRVP²³⁶)多肽和PL 7(²⁶⁴KNASNESFGYTEDEIVSSDVVGM²⁸⁶)2个优势B细胞表位。本研究成功制备了GapC蛋白，预测并鉴定了2个优势抗原表位，为其嵌合表位疫苗的开发提供了技术支持。     

EB病毒gp125蛋白的B细胞表位预测

目的预测EB病毒gp125蛋白的B细胞表位。方法基于EB病毒gp125蛋白的氨基酸序列,采用亲水性参数、可及性参数、极性参数和抗原性指数方案等,辅以对gp125蛋白的二级结构中的柔性区域的分析,预测gp125蛋白的B细胞表位。结果最有可能的B细胞表位位于gp125蛋白N端第403-416、565—574、578—584、618-630和832—843区段及其附近。结论用多参数预测EB病毒gp125蛋白的B细胞表位,为制备具有高灵敏度和高特异性的鼻咽癌诊断试剂及研究抗肿瘤转移靶向治疗的分子免疫学奠定基础。     

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（英文）

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Ig A表位,Ig E表位以及Ig G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。Ig A表位和Ig E表位分类器的预测能力优于其它Ig A表位和Ig E表位分类器,Ig G表位分类器和其它Ig G表位分类器的预测能力相当。     

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.     

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.     

Antibody-protein interactions: benchmark datasets and prediction tools evaluation

Background  

The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods.     

Immunoinformatics analysis of H5N1 proteome for designing an epitope-derived vaccine and predicting the prevalence of pre-existing cellular-mediated immunity toward bird flu virus in Indonesian population

As an essential step of adaptive immune response, the recognition between antigen and antibody triggers a series of self-protection mechanisms. Therefore, the prediction of antibody-binding sites (B-cell epitope) for protein antigens is an important field in immunology research. The performance of current prediction methods is far from satisfying, especially for conformational epitope prediction. Here a multi-perspective analysis was carried on with a comprehensive B-cell conformational epitope dataset, which contains 161 immunoglobulin complex structures collected from PDB, corresponding to 166 unique computationally defined epitopes. These conformational epitopes were described with parameters from different perspectives, including characteristics of epitope itself, comparison to non-epitope surface areas, and interaction pattern with antibody. According to the analysis results, B-cell conformational epitopes were relatively constant both in the number of composing residues and the accessible surface area. Though composed of spatially clustering residues, there were sequentially linear segments exist in these epitopes. Besides, statistical differences were found between epitope and non-epitope surface residues with parameters in residual and structural levels. Compared to non-epitope surface residues, epitope ones were more accessible. Amino acid enrichment and preference for specific types of residue-pair set on epitope areas have also been observed. Several amino acid properties from AAindex have been proven to distinguish epitope residues from non-epitope surface ones. Additionally, epitope residues tended to be less conservative under the environmental pressure. Measured by topological parameters, epitope residues were surrounded with fewer residues but in a more compact way. The occurrences of residue-pair sets between epitope and paratope also showed some patterns. Results indicate that, certain rules do exist in conformational epitopes in terms of size and sequential continuity. Statistical differences have been found between epitope and non-epitope surface residues in residual and structural levels. Such differences indicate the existence of distinctiveness for conformation epitopes. On the other hand, there was no accordant estimation for higher or lower values derived from any parameter for epitope residues compared with non-epitope surface residues. This observation further confirms the complicacy of characteristics for conformational epitope. Under such circumstance, it will be a more effective and accurate approach to combine several parameters to predict the conformation epitope. Finding conformational epitopes and analysing their properties is an important step to identify internal formation mechanism of conformational epitopes and this study will help future development of new prediction tools.     

B-cell epitope prediction for peptide-based vaccine design: towards a paradigm of biological outcomes for global health

Global health must address a rapidly evolving burden of disease, hence the urgent need for versatile generic technologies exemplified by peptide-based vaccines. B-cell epitope prediction is crucial for designing such vaccines; yet this approach has thus far been largely unsuccessful, prompting further inquiry into the underlying reasons for its apparent inadequacy. Two major obstacles to the development of B-cell epitope prediction for peptide-based vaccine design are (1) the prevailing binary classification paradigm, which mandates the problematic dichotomization of continuous outcome variables, and (2) failure to explicitly model biological consequences of immunization that are relevant to practical considerations of safety and efficacy. The first obstacle is eliminated by redefining the predictive task as quantitative estimation of empirically observable biological effects of antibody-antigen binding, such that prediction is benchmarked using measures of correlation between continuous rather than dichotomous variables; but this alternative approach by itself fails to address the second obstacle even if benchmark data are selected to exclusively reflect functionally relevant cross-reactivity of antipeptide antibodies with protein antigens (as evidenced by antibody-modulated protein biological activity), particularly where only antibody-antigen binding is actually predicted as a surrogate for its biological effects. To overcome the second obstacle, the prerequisite is deliberate effort to predict, a priori, biological outcomes that are of immediate practical significance from the perspective of vaccination. This demands a much broader and deeper systems view of immunobiology than has hitherto been invoked for B-cell epitope prediction. Such a view would facilitate comprehension of many crucial yet largely neglected aspects of the vaccine-design problem. Of these, immunodominance among B-cell epitopes is a central unifying theme that subsumes immune phenomena of tolerance, imprinting and refocusing; but it is meaningful for vaccine design only in the light of disease-specific pathophysiology, which for infectious processes is complicated by host-pathogen coevolution. To better support peptide-based vaccine design, B-cell epitope prediction would entail individualized quantitative estimation of biological outcomes relevant to safety and efficacy. Passive-immunization experiments could serve as an important initial proving ground for B-cell epitope prediction en route to vaccine-design applications, by restricting biological complexity to render epitope-prediction problems more computationally tractable.      

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.     

Conformational B-Cell Epitope Prediction on Antigen Protein Structures: A Review of Current Algorithms and Comparison with Common Binding Site Prediction Methods

Accurate prediction of B-cell antigenic epitopes is important for immunologic research and medical applications, but compared with other bioinformatic problems, antigenic epitope prediction is more challenging because of the extreme variability of antigenic epitopes, where the paratope on the antibody binds specifically to a given epitope with high precision. In spite of the continuing efforts in the past decade, the problem remains unsolved and therefore still attracts a lot of attention from bioinformaticists. Recently, several discontinuous epitope prediction servers became available, and it is intriguing to review all existing methods and evaluate their performances on the same benchmark. In addition, these methods are also compared against common binding site prediction algorithms, since they have been frequently used as substitutes in the absence of good epitope prediction methods.     

Identification of amino acid propensities that are strong determinants of linear B-cell epitope using neural networks

BACKGROUND: Identification of amino acid propensities that are strong determinants of linear B-cell epitope is very important to enrich our knowledge about epitopes. This can also help to obtain better epitope prediction. Typical linear B-cell epitope prediction methods combine various propensities in different ways to improve prediction accuracies. However, fewer but better features may yield better prediction. Moreover, for a propensity, when the sequence length is k, there will be k values, which should be treated as a single unit for feature selection and hence usual feature selection method will not work. Here we use a novel Group Feature Selecting Multilayered Perceptron, GFSMLP, which treats a group of related information as a single entity and selects useful propensities related to linear B-cell epitopes, and uses them to predict epitopes. METHODOLOGY/ PRINCIPAL FINDINGS: We use eight widely known propensities and four data sets. We use GFSMLP to rank propensities by the frequency with which they are selected. We find that Chou's beta-turn and Ponnuswamy's polarity are better features for prediction of linear B-cell epitope. We examine the individual and combined discriminating power of the selected propensities and analyze the correlation between paired propensities. Our results show that the selected propensities are indeed good features, which also cooperate with other propensities to enhance the discriminating power for predicting epitopes. We find that individually polarity is not the best predictor, but it collaborates with others to yield good prediction. Usual feature selection methods cannot provide such information. CONCLUSIONS/ SIGNIFICANCE: Our results confirm the effectiveness of active (group) feature selection by GFSMLP over the traditional passive approaches of evaluating various combinations of propensities. The GFSMLP-based feature selection can be extended to more than 500 remaining propensities to enhance our biological knowledge about epitopes and to obtain better prediction. A graphical-user-interface version of GFSMLP is available at: http://bio.classcloud.org/GFSMLP/.     

Prediction of residues in discontinuous B-cell epitopes using protein 3D structures

Discovery of discontinuous B-cell epitopes is a major challenge in vaccine design. Previous epitope prediction methods have mostly been based on protein sequences and are not very effective. Here, we present DiscoTope, a novel method for discontinuous epitope prediction that uses protein three-dimensional structural data. The method is based on amino acid statistics, spatial information, and surface accessibility in a compiled data set of discontinuous epitopes determined by X-ray crystallography of antibody/antigen protein complexes. DiscoTope is the first method to focus explicitly on discontinuous epitopes. We show that the new structure-based method has a better performance for predicting residues of discontinuous epitopes than methods based solely on sequence information, and that it can successfully predict epitope residues that have been identified by different techniques. DiscoTope detects 15.5% of residues located in discontinuous epitopes with a specificity of 95%. At this level of specificity, the conventional Parker hydrophilicity scale for predicting linear B-cell epitopes identifies only 11.0% of residues located in discontinuous epitopes. Predictions by the DiscoTope method can guide experimental epitope mapping in both rational vaccine design and development of diagnostic tools, and may lead to more efficient epitope identification.