A guide to the Proteomics Identifications Database proteomics data repository

The Proteomics Identifications Database (PRIDE, www.ebi.ac.uk/pride ) is one of the main repositories of MS derived proteomics data. Here, we point out the main functionalities of PRIDE both as a submission repository and as a source for proteomics data. We describe the main features for data retrieval and visualization available through the PRIDE web and BioMart interfaces. We also highlight the mechanism by which tailored queries in the BioMart can join PRIDE to other resources such as Reactome, Ensembl or UniProt to execute extremely powerful across‐domain queries. We then present the latest improvements in the PRIDE submission process, using the new easy‐to‐use, platform‐independent graphical user interface submission tool PRIDE Converter. Finally, we speak about future plans and the role of PRIDE in the ProteomExchange consortium.     

YPED:An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database(YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a singlelaboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry(LC–MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring(MRM)/selective reaction monitoring(SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.     

Learning from Decoys to Improve the Sensitivity and Specificity of Proteomics Database Search Results

The statistical validation of database search results is a complex issue in bottom-up proteomics. The correct and incorrect peptide spectrum match (PSM) scores overlap significantly, making an accurate assessment of true peptide matches challenging. Since the complete separation between the true and false hits is practically never achieved, there is need for better methods and rescoring algorithms to improve upon the primary database search results. Here we describe the calibration and False Discovery Rate (FDR) estimation of database search scores through a dynamic FDR calculation method, FlexiFDR, which increases both the sensitivity and specificity of search results. Modelling a simple linear regression on the decoy hits for different charge states, the method maximized the number of true positives and reduced the number of false negatives in several standard datasets of varying complexity (18-mix, 49-mix, 200-mix) and few complex datasets (E. coli and Yeast) obtained from a wide variety of MS platforms. The net positive gain for correct spectral and peptide identifications was up to 14.81% and 6.2% respectively. The approach is applicable to different search methodologies- separate as well as concatenated database search, high mass accuracy, and semi-tryptic and modification searches. FlexiFDR was also applied to Mascot results and showed better performance than before. We have shown that appropriate threshold learnt from decoys, can be very effective in improving the database search results. FlexiFDR adapts itself to different instruments, data types and MS platforms. It learns from the decoy hits and sets a flexible threshold that automatically aligns itself to the underlying variables of data quality and size.     

临床蛋白质组学———蛋白质组学在临床研究中的应用

临床蛋白质组学是将蛋白质组学技术应用于临床医学研究,它主要围绕疾病的预防、早期诊断和治疗等方面开展研究,其中,恶性肿瘤是临床蛋白质组学研究的一个重点研究对象.由于肿瘤生物标志物对早期诊断具有重要价值,所以临床蛋白质组学的主要目标之一是寻找合适的肿瘤生物标志物,多分子生物标志物已成为寻找肿瘤生物标志物的一个研究趋势.简要介绍了临床蛋白质组学的基本概念,实验设计,临床样本收集与预处理以及蛋白质组学技术在临床研究中的应用与进展.     

线粒体蛋白质组学研究进展

总结近年来不同物种线粒体蛋白表达谱的构建及线粒体蛋白功能的研究。线粒体蛋白质组正处于迅速发展阶段,但是由于分离、鉴定等技术的局限,线粒体蛋白数据库仍然贫乏。     

水稻蛋白质组学研究

水稻是全球最重要的粮食作物之一,目前水稻基因组精细图谱已得到全面解析,运用以双向电泳和质谱分析为主的蛋白质组技术平台对水稻各种组织器官进行蛋白质组学研究也取得了诸多进展,对水稻蛋白质组研究背景,技术路线及取得的进展进行了介绍,并对水稻蛋白质组的发展作了展望。     

基于GEO数据库、TMT蛋白组学筛选绞股蓝对膀胱癌的蛋白靶点

绞股蓝(Gynostemma pentaphyllum)是传统的中药材,对于多种癌症具有显著的疗效.为筛选绞股蓝对膀胱癌的作用靶点,本研究将30只SPF级SD大鼠随机分为模型组、给药组和空白组,通过膀胱灌注N-甲基-N-亚硝基脲(MNU)建立膀胱癌模型并通过相同的方式进行膀胱灌注绞股蓝干预治疗,在第10周进行取材,通过H&E染色病理切片观察各组膀胱内肿瘤情况以明确药效.通过蛋白质组学串联质谱标签(TMT) 10重标记法检测各组膀胱组织的蛋白差异表达情况,结合GEO数据库中3组膀胱癌数据集对作用靶点进行筛选.结果 显示,绞股蓝反向调节膀胱癌335种蛋白质,其中包括55种上调和280种下调.GEO的3个膀胱癌表达谱中汇集的差异蛋白(DEGs)包含20种上调和50种下调.将GEO中DEGs与TMT蛋白质组学中膀胱癌趋势相同的蛋白进行汇集,结合绞股蓝反向调节数据,总共筛选获得了3个反向调节靶点,其中包括1种下调的靶点CNN1和2种上调的靶点KRT19、PCP4,并通过蛋白免疫印迹法进行验证.本研究结果表明,CNN1、KRT19和PCP4可能是绞股蓝治疗膀胱癌的潜在靶点,绞股蓝可能通过调节CNN1、KRT19和PCP4来抵抗膀胱癌,这为绞股蓝治疗膀胱癌提供分子机制依据.     

病毒蛋白质组学及其应用

病毒蛋白质组学是蛋白质组学研究技术在病毒学领域的应用,其研究方法主要是基于质谱鉴定的电泳分离或色谱分离技术。病毒蛋白质组学的研究可以补充基因组注释、纯化单一的病毒成分、研究病毒与其宿主细胞蛋白的相互作用、识别病毒作用的靶位点、鉴定病毒感染的致病因子及病毒的进化关系、识别病毒的免疫源性蛋白。病毒蛋白质组的研究有助于对病毒致病性的了解,加速新的诊断方法及治疗药物的研制,增强对病毒的生物防御。由于一些技术及主观因素的影响,病毒蛋白质组的研究是很有限的,这是一个亟待重视并增强的领域。     

蛋白质组学研究技术及其进展

蛋白质组学是在后基因组时代出现的一个新的研究领域,它是对机体、组织或细胞的全部蛋白质的表达和功能模式进行研究。对蛋白质组的研究可以使我们更容易接近对生命过程的认识。本文对蛋白质组学研究所使用的主要技术例如二维凝胶电泳、质谱、酵母双杂交、蛋白质芯片、表面等离子共振和生物信息学等作一简要综述。     

The HUPO Proteomics Standards Initiative Meeting: Towards Common Standards for Exchanging Proteomics Data

The Proteomics Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparison, exchange and verification. Initially the fields of protein-protein interactions (PPI) and mass spectroscopy have been targeted and the inaugural meeting of the PSI addressed the questions of data storage and exchange in both of these areas. The PPI group rapidly reached consensus as to the minimum requirements for a data exchange model; an XML draft is now being produced. The mass spectroscopy group have achieved major advances in the definition of a required data model and working groups are currently taking these discussions further. A further meeting is planned in January 2003 to advance both these projects.     

目标?鄄诱饵库搜索策略在蛋白质组质谱鉴定和质控中的应用及研究进展

基于串联质谱的蛋白质组研究会产生海量的质谱数据,这些数据通常使用数据库搜索引擎进行鉴定分析,并根据肽段谱图匹配(PSM)反推真实的样品蛋白质．对于高通量蛋白质组数据的处理,其鉴定结果的可信是后续分析应用的前提,因此对鉴定结果的质量控制尤为重要,而基于目标-诱饵库(target-decoy)搜索策略的质量控制是目前应用最为广泛的方法．本文首先介绍了基于目标-诱饵库搜索策略搜库和质量控制的实施流程,然后综述了基于目标-诱饵库搜索策略的质量控制工具,并提出了目标-诱饵库搜索策略的不足及改善方法,最后对目标-诱饵库搜索策略进行了总结与展望．     

蛋白质组学及其技术发展

蛋白质组学产生于20世纪90年代,发展至今已日趋成熟。蛋白质组学是以生物体的全部或部分蛋白为研究对象,研究它们在生命活动过程中的作用、功能。蛋白质组学较之前的基因组学对于生命现象的解释更直接、更准确,近年得到了快速发展,并受到世界各国学者的高度关注。我们简要综述了蛋白质组学及其技术,并简单概述了这项技术在生命科学领域的应用。     

The MetaCyc Database

MetaCyc is a metabolic-pathway database that describes 445 pathways and 1115 enzymes occurring in 158 organisms. MetaCyc is a review-level database in that a given entry in MetaCyc often integrates information from multiple literature sources. The pathways in MetaCyc were determined experimentally, and are labeled with the species in which they are known to occur based on literature references examined to date. MetaCyc contains extensive commentary and literature citations. Applications of MetaCyc include pathway analysis of genomes, metabolic engineering and biochemistry education. MetaCyc is queried using the Pathway Tools graphical user interface, which provides a wide variety of query operations and visualization tools. MetaCyc is available via the World Wide Web at http://ecocyc.org/ecocyc/metacyc.html, and is available for local installation as a binary program for the PC and the Sun workstation, and as a set of flatfiles. Contact metacyc-info@ai.sri.com for information on obtaining a local copy of MetaCyc.