Discovery of promiscuous HLA-II-restricted T cell epitopes with TEPITOPE

TEPITOPE is a prediction model that has been successfully applied to the in silico identification of T cell epitopes in the context of oncology, allergy, infectious diseases, and autoimmune diseases. Like most epitope prediction models, TEPITOPE's underlying algorithm is based on the prediction of HLA-II peptide binding, which constitutes a major bottleneck in the natural selection of epitopes. An important step in the design of subunit vaccines is the identification of promiscuous HLA-II ligands in sets of disease-specific gene products. TEPITOPE's user interface enables the systematic prediction of promiscuous peptide ligands for a broad range of HLA-binding specificity. We show how to apply the TEPITOPE prediction model to identify T cell epitopes, and provide both a road map and examples of its successful application.     

乙型肝炎病毒S蛋白的序列特征分析

乙肝病毒S蛋白是病毒的包膜蛋白,与病毒进入细胞有关,它存在逆转录过程并且具有极强的潜伏性。本论文应用生物信息学分析乙肝病毒S蛋白的序列特征,利用在线分析软件预测乙肝病毒S蛋白的理化性质和亲疏水性、跨膜区域、信号肽特征、磷酸化位点、二级结构以及乙肝病毒S蛋白的最佳抗原表位形成位置等。结果显示了乙肝病毒S蛋白由226个氨基酸组成,理论等电点是8.21,为不稳定蛋白,总平均亲水性为0.649,是疏水蛋白质,并且该蛋白存在信号肽,有4个跨膜区,有30个潜在的磷酸化位点,主要二级结构为α螺旋和无规则卷曲,同时,结合乙型肝炎病毒S蛋白的序列可及性、线性表位、β转角、柔性、抗原性的预测结果,可以找到潜在的抗原表位区域,为乙型肝炎的表位疫苗研制提供重要的参考依据,有利于进一步对乙型肝炎S蛋白的抗原性进行研究。     

柯萨奇病毒A组6型VP1的主要特性[]及表位预测分析

运用生物信息学方法预测分析襄阳地区分离的柯萨奇病毒A组6型(Coxsackievirus A6, CV-A6)病毒蛋白(Viral protein, VP)中VP1的主要特性及表位。应用ProtParam,SOPMA,Phyre 2,DNAstar v8.1.3,Mega11,ABCpred,ElliPro,NetMHCpan-4.1,NetMHCIIpan-4.0等软件和在线网站预测分析CV-A6 VP1包括理化性质、结构特征、序列特点,亲缘关系在内的主要特性及B,T细胞抗原表位。结果发现该分离株VP1为碱性、不稳定的亲水性蛋白,其二级结构以无规则卷曲为主,核苷酸(氨基酸)同源性不一,有12个氨基酸突变位点,属于D3a亚型。该蛋白还存在多个潜在及优势B,T细胞抗原表位。CV-A6襄阳分离株与国内广西、广东地区CV-A6共进化共循环,具有一定的免疫潜能,可为分子流行病学监测以及多价疫苗的研制提供科学依据与参考。     

OrFin: A web tool for detection of putative orthologs

Identification of ortholog is one of the important tasks to understand a novel genome. It helps to assign functional annotations, from one organism to another organism. To identify the putative ortholog, Reciprocal Best BLAST hit (RBBH) method is known to be an efficient approach. OrFin makes use of the same approach to identify pair of orthologous proteins for a given set of sequences of two species. It is a user-friendly web tool which works with user defined parameters to search RBBHs. Results are produced in both html and text format.

Availability

This web tool is freely available at http://bifl.uohyd.ac.in/orfin     

宫颈癌相关blcap基因的生物信息学分析

BLCAP is a potential gene for suppression of cervical carcinoma, which was found by analysing the cervical carcinoma specimen with the oncogene and anti-oncogene cDNA microarray. Basing on the bioinformatical analyses, we try to predict the function of blcap gene. The results show that there are several genes that highly resemble with blcap. The comparability between the sequences of blcap and Homo sapiens mRNA (DKFZp564M053) or BC10 is 99% and 87%, respectively. The protein encoded by BLCAP is composed of Leu(19.5%), pro(9.19%), ser(8.04%)、 cys(8.04%) and other amino acids. The secondary structure of the N-terminal of BLCAP encoded protein is an alpha helix. In the C-terminal, it is beta sheet and in the middle, it is coil. The of the terminals is more hydrophobile than the middle region. Between 45-55aa, there is a transmembrane region. Therefore, we forecast the BLCAP is a member of transmembrane protein I. By analyzing the signal peptide and the procedure of blcap gene with the program of SignalP (V1.1), we found a cleavage site in 59-66aa. By using the program of Netpho, we predicted there might be three phospholate sites at 68aa, 73aa and 78aa. At 78-81aa, we found a typical [ST]-X [2] -[DE] structure—the phospholate site of tyrosine protein kinase, which might be related to its function. Bioinformatic studies of blcap provided the foundation for the function researches of BLCAP in laboratory.     

宫颈癌相关blcap基因的生物信息学分析

宫颈癌是发展中国家最常见的妇科恶性肿瘤之一,其死亡率居女性肿瘤的第二位,已成为危害女性身体健康的最主要杀手[1].过去常认为高危型人乳头瘤病毒(Human papillomavirus, HPV)如HPV16,18等型的感染是导致宫颈癌的主要原因之一,但近年众多的研究表明,细胞内遗传因素的改变也起着非常重要的作用[2].因此,确定宫颈癌相关基因,从而对从分子水平上了解细胞癌变机制,为宫颈癌的临床诊断、预防、易感性预测及治疗提供分子标记具有十分重要的意义.     

Refining microRNA target predictions: Sorting the wheat from the chaff

microRNAs are short RNAs that reduce gene expression by binding to their targets. The accurate prediction of microRNA targets is essential to understanding the function of microRNAs. Computational predictions indicate that all human genes may be regulated by microRNAs, with each microRNA possibly targeting thousands of genes. Here we discuss computational methods for identifying mammalian microRNA targets and refining them for further experimental validation. We describe microRNA target prediction resources and procedures and how they integrate with various types of experimental techniques that aim to validate them or further explore their function. We also provide a list of target prediction databases and explain how these are curated.     

植物长链非编码RNA的生物信息学预测与分析研究进展

长链非编码RNA(Long non-coding RNAs,lncRNAs)是一类广泛存在于真核生物中,长度大于200个核苷酸、无蛋白编码功能,具有调控基因转录后表达的RNA转录本。新近研究表明,lncRNA在多种生物途径中起着重要调节作用。生物信息学由生物、数学、计算机科学,统计学等多学科交叉产生,能从全局和系统水平对大数据信息进行深入挖掘与分析。采用生物信息学方法预测与分析lncRNA是当前发现和鉴定植物lncRNA的重要策略之一。本文梳理和总结了近年来采用生物信息学预测植物lncRNA及其靶基因的方法策略,以期为今后深入认知植物lncRNA在植物的生长发育过程、抗逆境胁迫及系统进化等过程中的作用研究提供一定参考。     

表位疫苗及相关技术研究进展

表位疫苗是用抗原表位制备的疫苗,是近年来新兴的一种疫苗研制技术,也是今后最具开发前景的疫苗技术之一,在肿瘤、病毒等疾病的防治中有着自身独特的优势。本文详细阐述了T表位和B表位的筛选和鉴定方法、表位疫苗的载体研究及表位疫苗在肿瘤、病毒和微生物感染中的应用等,对表位疫苗的最新研究进展进行了综述。     

A probabilistic meta-predictor for the MHC class II binding peptides

Several computational methods for the prediction of major histocompatibility complex (MHC) class II binding peptides embodying different strengths and weaknesses have been developed. To provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. The construction of a meta-predictor of this type based on a probabilistic approach is introduced in this paper. The design permits the easy incorporation of results obtained from any number of individual predictors. It is demonstrated that this integrated method outperforms six state-of-the-art individual predictors based on computational studies using MHC class II peptides from 13 HLA alleles and three mouse MHC alleles obtained from the Immune Epitope Database and Analysis Resource. It is concluded that this integrative approach provides a clearly enhanced reliability of prediction. Moreover, this computational framework can be directly extended to MHC class I binding predictions.     

Identification of disease-associated proteins by proteomic approach in ankylosing spondylitis

Ankylosing spondylitis (AS) is a chronic systemic inflammatory disorder of the axial skeleton and shows significant inherited susceptibility. Auto-immune responses have been traditionally considered as a putative pathogenetic event in AS. However, no consistent self-antigen has been identified to responsible for the disorders in AS to this day. In this study, serum protein profiles of AS patients and healthy controls from a large Chinese AS family were investigated by two dimensional electrophoresis analysis. A group of four highly expressed protein spots was observed in all AS patients' profiles and subsequently identified as isoforms of haptoglobin precursor (pre-Hp) by ESI-Q-TOF MS/MS. Increased expression of haptoglobin precursor were also observed in sera of sporadic AS patients. Moreover, bioinformatics analysis revealed epitopes derived from haptoglobin precursor with high affinity binding to HLA-B( *)2705, a primary subtype associated with AS. These results indicate that pre-Hp may be involved in the pathogenesis of AS.     

Experimental and bioinformatic approach to identifying antigenic epitopes in human α- and β-enolases

Human α- and β-enolases are highly homologous enzymes, difficult to differentiate immunologically. In this work, we describe production, purification and properties of anti-α- and anti-β-enolase polyclonal antibodies. To raise antibodies, rabbits were injected with enolase isoenzymes that were purified from human kidney (α-enolase) and skeletal muscle (β-enolase). Selective anti-α- and anti-β-enolase antibodies were obtained by affinity chromatography on either α- or β-enolase-Sepharose columns. On Western blots, antibodies directed against human β-enolase, did not react with human α-isoenzyme, but recognized pig and rat β-enolase. To determine what makes these antibodies selective bioinformatic tools were used to predict conformational epitopes for both enolase isoenzymes. Three predicted epitopes were mapped to the same regions in both α- and β-enolase. Peptides corresponding to predicted epitopes were synthesized and tested against purified antibodies. One of the pin-attached peptides representing α-enolase epitope (the C-terminal portion of the epitope 3 - S²⁶²PDDPSRYISPDQ²⁷³) reacted with anti-α-enolase, while the other also derived from the α-enolase sequence (epitope 2 - N¹⁹³VIKEKYGKDATN²⁰⁵) was recognized by anti-β-enolase antibodies. Interestingly, neither anti-α- nor anti-β-antibody reacted with a peptide corresponding to the epitope 2 in β-enolase (G¹⁹⁴VIKAKYGKDATN²⁰⁶). Further analysis showed that substitution of E¹⁹⁷ with A in α-enolase epitope 2 peptide lead to 70% loss of immunological activity, while replacement of A¹⁹⁸ with E in peptide representing β-enolase epitope 2, caused 67% increase in immunological activity. Our results suggest that E¹⁹⁷ is essential for preserving immunologically active conformation in epitope 2 peptidic homolog, while it is not crucial for this epitope's antigenic activity in native β-enolase.     

SNEP: SNP-derived Epitope Prediction program for minor H antigens

The single nucleotide polymorphism (SNP)-derived Epitope Prediction program (SNEP) is now available to the public. It predicts minor histocompatibility antigens (miHAgs), which are T-cell epitopes containing polymorphic spots, from proteins listed in the SWISS-PROT database. SNEP recognizes polymorphisms (termed VARIANT or CONFLICT in SWISS-PROT) and predicts potential T-cell epitopes within a chosen distance around the polymorphic residue. The prediction algorithm is based on the SYFPEITHI T-cell epitope prediction program. SNEP is able to search for proteins according to their accession numbers, sequence stretches or gene names, for example. The predictions are available for several human leucocyte antigen class I and class II allelic products, which allow for a rapid and precise evaluation of potential miHAgs within polymorphic antigens.     

氨基甲酸乙酯水解酶的家族生物信息学分析

氨基甲酸乙酯(Ethyl carbamate,EC)是酒精饮料生产过程中自然产生的副产物,具有潜在的致癌性和遗传毒性,成为影响人们健康的隐患.利用氨基甲酸乙酯水解酶(Urethane hydrolase,UH)消除酒精饮料中已存在的EC具有直接、高效的作用.为了进一步探索EC水解酶的功能与作用,本实验利用生物信息学的方...     

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。     

Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions

Background

It is important to accurately determine the performance of peptide:MHC binding predictions, as this enables users to compare and choose between different prediction methods and provides estimates of the expected error rate. Two common approaches to determine prediction performance are cross-validation, in which all available data are iteratively split into training and testing data, and the use of blind sets generated separately from the data used to construct the predictive method. In the present study, we have compared cross-validated prediction performances generated on our last benchmark dataset from 2009 with prediction performances generated on data subsequently added to the Immune Epitope Database (IEDB) which served as a blind set.

Results

We found that cross-validated performances systematically overestimated performance on the blind set. This was found not to be due to the presence of similar peptides in the cross-validation dataset. Rather, we found that small size and low sequence/affinity diversity of either training or blind datasets were associated with large differences in cross-validated vs. blind prediction performances. We use these findings to derive quantitative rules of how large and diverse datasets need to be to provide generalizable performance estimates.

Conclusion

It has long been known that cross-validated prediction performance estimates often overestimate performance on independently generated blind set data. We here identify and quantify the specific factors contributing to this effect for MHC-I binding predictions. An increasing number of peptides for which MHC binding affinities are measured experimentally have been selected based on binding predictions and thus are less diverse than historic datasets sampling the entire sequence and affinity space, making them more difficult benchmark data sets. This has to be taken into account when comparing performance metrics between different benchmarks, and when deriving error estimates for predictions based on benchmark performance.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-241) contains supplementary material, which is available to authorized users.