Ultra-high intra-spectrum mass accuracy enables unambiguous identification of fragment reporter ions in isobaric multiplexed quantitative proteomics

Isobaric tagging using reagents such as tandem mass tags (TMT) and isobaric tags for relative and absolute quantification (iTRAQ) have become popular tools for mass spectrometry based quantitative proteomics. Because the peptide quantification information is collected in tandem mass spectra, the accuracy and precision of this method largely depend on the resolution with which precursor ions can be selected for the fragmentation and the specificity of the generated reporter ion. The latter can constitute an issue if near isobaric ion signals are present in such spectra because they may distort quantification results. We propose a simple remedy for this problem by identifying reporter ions via the accurate mass differences within a single tandem mass spectrum instead of applying fixed mass error tolerances for all tandem mass spectra. Our results show that this leads to unambiguous reporter ion identification and complete removal of interfering signals. This mode of data processing is easily implemented in software and offers advantages for protein quantification based on few peptides.     

A simple solid phase mass tagging approach for quantitative proteomics

New mass-tagging reagents for quantitative proteomics measurements have been designed using solid phase peptide synthesis technology. The solid phase mass tags have been used to accurately measure the relative amounts of cysteine-containing peptides in model peptide mixtures as well as in mixtures of tryptic digests in the femtomol range. Measurements were made using both matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) and online reversed-phase capillary liquid chromatography coupled through a nanoelectrospray interface to an ion trap mass spectrometer (capillary LC/ESI-MS). Results of mass-tagging experiments obtained from these two mass spectrometry techniques and their relative advantages and disadvantages for identification and quantitation of mass tagged peptides are compared. These reagents provide a simple, rapid and cost-effective alternative to currently available mass tagging technologies.     

In vitro and in silico processes to identify differentially expressed proteins

We present an integrated proteomics platform designed for performing differential analyses. Since reproducible results are essential for comparative studies, we explain how we improved reproducibility at every step of our laboratory processes, e.g. by taking advantage of the powerful laboratory information management system we developed. The differential capacity of our platform is validated by detecting known markers in a real sample and by a spiking experiment. We introduce an innovative two-dimensional (2-D) plot for displaying identification results combined with chromatographic data. This 2-D plot is very convenient for detecting differential proteins. We also adapt standard multivariate statistical techniques to show that peptide identification scores can be used for reliable and sensitive differential studies. The interest of the protein separation approach we generally apply is justified by numerous statistics, complemented by a comparison with a simple shotgun analysis performed on a small volume sample. By introducing an automatic integration step after mass spectrometry data identification, we are able to search numerous databases systematically, including the human genome and expressed sequence tags. Finally, we explain how rigorous data processing can be combined with the work of human experts to set high quality standards, and hence obtain reliable (false positive < 0.35%) and nonredundant protein identifications.     

Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: application to protein acetylation and phosphorylation

Despite advances in metabolic and postmetabolic labeling methods for quantitative proteomics, there remains a need for improved label-free approaches. This need is particularly pressing for workflows that incorporate affinity enrichment at the peptide level, where isobaric chemical labels such as isobaric tags for relative and absolute quantitation and tandem mass tags may prove problematic or where stable isotope labeling with amino acids in cell culture labeling cannot be readily applied. Skyline is a freely available, open source software tool for quantitative data processing and proteomic analysis. We expanded the capabilities of Skyline to process ion intensity chromatograms of peptide analytes from full scan mass spectral data (MS1) acquired during HPLC MS/MS proteomic experiments. Moreover, unlike existing programs, Skyline MS1 filtering can be used with mass spectrometers from four major vendors, which allows results to be compared directly across laboratories. The new quantitative and graphical tools now available in Skyline specifically support interrogation of multiple acquisitions for MS1 filtering, including visual inspection of peak picking and both automated and manual integration, key features often lacking in existing software. In addition, Skyline MS1 filtering displays retention time indicators from underlying MS/MS data contained within the spectral library to ensure proper peak selection. The modular structure of Skyline also provides well defined, customizable data reports and thus allows users to directly connect to existing statistical programs for post hoc data analysis. To demonstrate the utility of the MS1 filtering approach, we have carried out experiments on several MS platforms and have specifically examined the performance of this method to quantify two important post-translational modifications: acetylation and phosphorylation, in peptide-centric affinity workflows of increasing complexity using mouse and human models.     

利用串联质谱鉴定氨基酸突变的生物信息学算法

氨基酸突变能够改变蛋白的结构和功能,影响生物体的生命过程.基于串联质谱的鸟枪法蛋白质组学是目前大规模研究蛋白质组学的主要方法,但是现有的质谱数据鉴定流程为了提高鉴定结果的灵敏度往往会有意压缩数据库中的氨基酸突变信息.因此,如何挖掘数据中的氨基酸突变信息成为当前质谱数据鉴定的一个重要部分.当前应用于氨基酸突变鉴定的串联质谱鉴定方法大致可以分为3大类:基于序列数据库搜索的方法、基于序列标签搜索的算法以及基于图谱库搜索的算法.本文首先详细介绍了这3种氨基酸突变鉴定算法,并分析了各种方法的特点和不足,然后介绍了氨基酸突变鉴定的研究现状和发展方向.随着基于串联质谱的蛋白质组学的不断发展,蛋白序列中的氨基酸突变信息将被更好地解析出来,从而得以深入探讨由氨基酸突变引起的蛋白结构和功能改变,为揭示氨基酸突变的生物学意义奠定基础.     

Peptide sequence tag-based blind identification of post-translational modifications with point process model

An important but difficult problem in proteomics is the identification of post-translational modifications (PTMs) in a protein. In general, the process of PTM identification by aligning experimental spectra with theoretical spectra from peptides in a peptide database is very time consuming and may lead to high false positive rate. In this paper, we introduce a new approach that is both efficient and effective for blind PTM identification. Our work consists of the following phases. First, we develop a novel tree decomposition based algorithm that can efficiently generate peptide sequence tags (PSTs) from an extended spectrum graph. Sequence tags are selected from all maximum weighted antisymmetric paths in the graph and their reliabilities are evaluated with a score function. An efficient deterministic finite automaton (DFA) based model is then developed to search a peptide database for candidate peptides by using the generated sequence tags. Finally, a point process model-an efficient blind search approach for PTM identification, is applied to report the correct peptide and PTMs if there are any. Our tests on 2657 experimental tandem mass spectra and 2620 experimental spectra with one artificially added PTM show that, in addition to high efficiency, our ab-initio sequence tag selection algorithm achieves better or comparable accuracy to other approaches. Database search results show that the sequence tags of lengths 3 and 4 filter out more than 98.3% and 99.8% peptides respectively when applied to a yeast peptide database. With the dramatically reduced search space, the point process model achieves significant improvement in accuracy as well. AVAILABILITY: The software is available upon request.     

Combination of two-dimensional electrophoresis and shotgun peptide sequencing in comparative proteomics

Two-dimensional electrophoresis (2-DE) and shotgun peptide sequencing are the two major technologies to compare the expression profile of proteins, which is also referred to as comparative proteomics or quantitative proteomics. Although the methodologies, such as difference gel electrophoresis for 2-DE and isotope-coded affinity tags for shotgun peptide sequencing, have made rapid progress, these two approaches have their own strengths and weaknesses. Therefore, the combination of the two methodologies is beneficial for the purpose of better comparative proteomics, especially in comprehensive coverage of the proteome and protein information such as post-translational modifications.     

Analysing proteomic data

The rapid growth of proteomics has been made possible by the development of reproducible 2D gels and biological mass spectrometry. However, despite technical improvements 2D gels are still less than perfectly reproducible and gels have to be aligned so spots for identical proteins appear in the same place. Gels can be warped by a variety of techniques to make them concordant. When gels are manipulated to improve registration, information is lost, so direct methods for gel registration which make use of all available data for spot matching are preferable to indirect ones. In order to identify proteins from gel spots a property or combination of properties that are unique to that protein are required. These can then be used to search databases for possible matches. Molecular mass, pI, amino acid composition and short sequence tags can all be used in database searches. Currently the method of choice for protein identification is mass spectrometry. Proteins are eluted from the gels and cleaved with specific endoproteases to produce a series of peptides of different molecular mass. In peptide mass fingerprinting, the peptide profile of the unknown protein is compared with theoretical peptide libraries generated from sequences in the different databases. Tandem mass spectroscopy (MS/MS) generates short amino acid sequence tags for the individual peptides. These partial sequences combined with the original peptide masses are then used for database searching, greatly improving specificity. Increasingly protein identification from MS/MS data is being fully or partially automated. When working with organisms, which do not have sequenced genomes (the case with most helminths), protein identification by database searching becomes problematical. A number of approaches to cross species protein identification have been suggested, but if the organism being studied is only distantly related to any organism with a sequenced genome then the likelihood of protein identification remains small. The dynamic nature of the proteome means that there really is no such thing as a single representative proteome and a complete set of metadata (data about the data) is going to be required if the full potential of database mining is to be realised in the future.     

生物质谱在细胞信号转导研究中的应用

近几年快速发展起来的生物质谱技术 ,依靠 (酶解后肽段 )精确质量数测定和随机肽序列标签分析 ,实现了对蛋白质高通量的鉴定 ,并被成功地用于蛋白质相互作用和蛋白质磷酸化等翻译后修饰研究。与传统的研究手段相比 ,上述技术能够在一次实验中对多信号通路中所有磷酸化的蛋白质分子及其磷酸化位点进行鉴定 ,已成为蛋白质组学最新发展中令人关注的一个热点。简要综述质谱技术应用于上述工作中的 3种策略     

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.     

Mass spectrometry-based proteomics and peptidomics for systems biology and biomarker discovery

The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology.Proteomics technologies have evolved to produce larg...     

Mass Spectrometric Identification of In Vivo Phosphorylation Sites of Differentially Expressed Proteins in Elongating Cotton Fiber Cells

Two-dimensional gel electrophoresis (2-DE)-based proteomics approach was applied to extensively explore the molecular basis of plant development and environmental adaptation. These proteomics analyses revealed thousands of differentially expressed proteins (DEPs) closely related to different biological processes. However, little attention has been paid to how peptide mass fingerprinting (PMF) data generated by the approach can be directly utilized for the determination of protein phosphorylation. Here, we used the software tool FindMod to predict the peptides that might carry the phosphorylation modification by examining their PMF data for mass differences between the empirical and theoretical peptides and then identified phosphorylation sites using MALDI TOF/TOF according to predicted peptide data from these DEP spots in the 2-D gels. As a result, a total of 48 phosphorylation sites of 40 DEPs were successfully identified among 235 known DEPs previously revealed in the 2-D gels of elongating cotton fiber cells. The 40 phosphorylated DEPs, including important enzymes such as enolase, transketolase and UDP-L-rhamnose synthase, are presumed to participate in the functional regulation of numerous metabolic pathways, suggesting the reverse phosphorylation of these proteins might play important roles in elongating cotton fibers. The results also indicated that some different isoforms of the identical DEP revealed in our 2-DE-based proteomics analysis could be annotated by phosphorylation events. Taken together, as the first report of large-scale identification of phosphorylation sites in elongating cotton fiber cells, our study provides not only an excellent example of directly identifying phosphorylation sites from known DEPs on 2-D gels but also provides a valuable resource for future functional studies of phosphorylated proteins in this field.     

Publishing large proteome datasets: scientific policy meets emerging technologies

Currently, there are various approaches to proteomic analyses based on either 2D gel or HPLC separation platforms, generating data of different formats, structures and types. Identification of these separated proteins or peptide fragments is typically achieved by mass spectrometry (MS) measurements that use either accurate mass measurements or fragmentation (MS–MS) information. Integrating the information generated from these different platforms is essential if proteomics is to succeed. A further challenge lies in generating standards that can accept the hundreds-of-thousands of mass spectra produced per analysis based on threshold or probability measurements. Finally, peer review and electronic publication processes will be crucial to the dissemination and use of proteomic information. Merging the policy requirements of data-intensive research with information technology will enable scientists to gain real value from global proteomics information.     

Postexperiment monoisotopic mass filtering and refinement (PE-MMR) of tandem mass spectrometric data increases accuracy of peptide identification in LC/MS/MS

Methods for treating MS/MS data to achieve accurate peptide identification are currently the subject of much research activity. In this study we describe a new method for filtering MS/MS data and refining precursor masses that provides highly accurate analyses of massive sets of proteomics data. This method, coined "postexperiment monoisotopic mass filtering and refinement" (PE-MMR), consists of several data processing steps: 1) generation of lists of all monoisotopic masses observed in a whole LC/MS experiment, 2) clusterization of monoisotopic masses of a peptide into unique mass classes (UMCs) based on their masses and LC elution times, 3) matching the precursor masses of the MS/MS data to a representative mass of a UMC, and 4) filtration of the MS/MS data based on the presence of corresponding monoisotopic masses and refinement of the precursor ion masses by the UMC mass. PE-MMR increases the throughput of proteomics data analysis, by efficiently removing "garbage" MS/MS data prior to database searching, and improves the mass measurement accuracies (i.e. 0.05 +/- 1.49 ppm for yeast data (from 4.46 +/- 2.81 ppm) and 0.03 +/- 3.41 ppm for glycopeptide data (from 4.8 +/- 7.4 ppm)) for an increased number of identified peptides. In proteomics analyses of glycopeptide-enriched samples, PE-MMR processing greatly reduces the degree of false glycopeptide identification by correctly assigning the monoisotopic masses for the precursor ions prior to database searching. By applying this technique to analyses of proteome samples of varying complexities, we demonstrate herein that PE-MMR is an effective and accurate method for treating massive sets of proteomics data.     

A statistical framework to discover true associations from multiprotein complex pull-down proteomics data sets

Experimental processes to collect and process proteomics data are increasingly complex, and the computational methods to assess the quality and significance of these data remain unsophisticated. These challenges have led to many biological oversights and computational misconceptions. We developed an empirical Bayes model to analyze multiprotein complex (MPC) proteomics data derived from peptide mass spectrometry detections of purified protein complex pull-down experiments. Using our model and two yeast proteomics data sets, we estimated that there should be an average of about 20 true associations per MPC, almost 10 times as high as was previously estimated. For data sets generated to mimic a real proteome, our model achieved on average 80% sensitivity in detecting true associations, as compared with the 3% sensitivity in previous work, while maintaining a comparable false discovery rate of 0.3%. Cross-examination of our results with protein complexes confirmed by various experimental techniques demonstrates that many true associations that cannot be identified by previous approach are identified by our method.     

iTRAQ技术及其在蛋白质组学中的应用

近年来随着蛋白质组学的迅速发展,其相应的方法学研究也取得了巨大的进步, 一系列新技术融入了蛋白质组学研究中,极大地促进了这门学科的发展.相对和绝对定量同位素标记(iTRAQ)技术与高度敏感性和准确性的串联质谱及多维液相色谱联用技术已成为蛋白质定性和定量研究的主要工具之一. 该技术可对复杂样本、细胞器、细 胞裂解液等样本进行相对和绝对定量研究,具有较好的定量效果、较高的重复性.由于其能够同时对多达8种样品进行标记分析,故在生命科学的各个领域得到了广泛的应用.本文对iTRAQ的原理、实验流程、优缺点及近几年的应用进展进行综述.     

Strategies for metagenomic-guided whole-community proteomics of complex microbial environments

Accurate protein identification in large-scale proteomics experiments relies upon a detailed, accurate protein catalogue, which is derived from predictions of open reading frames based on genome sequence data. Integration of mass spectrometry-based proteomics data with computational proteome predictions from environmental metagenomic sequences has been challenging because of the variable overlap between proteomic datasets and corresponding short-read nucleotide sequence data. In this study, we have benchmarked several strategies for increasing microbial peptide spectral matching in metaproteomic datasets using protein predictions generated from matched metagenomic sequences from the same human fecal samples. Additionally, we investigated the impact of mass spectrometry-based filters (high mass accuracy, delta correlation), and de novo peptide sequencing on the number and robustness of peptide-spectrum assignments in these complex datasets. In summary, we find that high mass accuracy peptide measurements searched against non-assembled reads from DNA sequencing of the same samples significantly increased identifiable proteins without sacrificing accuracy.     

Thermodynamic analysis of protein-ligand interactions in complex biological mixtures using a shotgun proteomics approach

Shotgun proteomics protocols are widely used for the identification and/or quantitation of proteins in complex biological samples. Described here is a shotgun proteomics protocol that can be used to identify the protein targets of biologically relevant ligands in complex protein mixtures. The protocol combines a quantitative proteomics platform with a covalent modification strategy, termed Stability of Proteins from Rates of Oxidation (SPROX), which utilizes the denaturant dependence of hydrogen peroxide-mediated oxidation of methionine side chains in proteins to assess the thermodynamic properties of proteins and protein-ligand complexes. The quantitative proteomics platform involves the use of isobaric mass tags and a methionine-containing peptide enhancement strategy. The protocol is evaluated in a ligand binding experiment designed to identify the proteins in a yeast cell lysate that bind the well-known enzyme cofactor, β-nicotinamide adenine dinucleotide (NAD+). The protocol is also used to investigate the protein targets of resveratrol, a biologically active ligand with less well-understood protein targets. A known protein target of resveratrol, cytosolic aldehyde dehydrogenase, was identified in addition to six other potential new proteins targets including four that are associated with the protein translation machinery, which has previously been implicated as a target of resveratrol.