ICPLQuant – A software for non‐isobaric isotopic labeling proteomics

The main goal of many proteomics experiments is an accurate and rapid quantification and identification of regulated proteins in complex biological samples. The bottleneck in quantitative proteomics remains the availability of efficient software to evaluate and quantify the tremendous amount of mass spectral data acquired during a proteomics project. A new software suite, ICPLQuant, has been developed to accurately quantify isotope‐coded protein label (ICPL)‐labeled peptides on the MS level during LC‐MALDI and peptide mass fingerprint experiments. The tool is able to generate a list of differentially regulated peptide precursors for subsequent MS/MS experiments, minimizing time‐consuming acquisition and interpretation of MS/MS data. ICPLQuant is based on two independent units. Unit 1 performs ICPL multiplex detection and quantification and proposes peptides to be identified by MS/MS. Unit 2 combines MASCOT MS/MS protein identification with the quantitative data and produces a protein/peptide list with all the relevant information accessible for further data mining. The accuracy of quantification, selection of peptides for MS/MS‐identification and the automated output of a protein list of regulated proteins are demonstrated by the comparative analysis of four different mixtures of three proteins (Ovalbumin, Horseradish Peroxidase and Rabbit Albumin) spiked into the complex protein background of the DGPF Proteome Marker.     

Quantitative proteomics analysis of adsorbed plasma proteins classifies nanoparticles with different surface properties and size

Nanoparticle biological activity, biocompatibility and fate can be directly affected by layers of readily adsorbed host proteins in biofluids. Here, we report a study on the interactions between human blood plasma proteins and nanoparticles with a controlled systematic variation of properties using (18)O-labeling and LC-MS-based quantitative proteomics. We developed a novel protocol to both simplify isolation of nanoparticle bound proteins and improve reproducibility. LC-MS analysis identified and quantified 88 human plasma proteins associated with polystyrene nanoparticles consisting of three different surface chemistries and two sizes, as well as, for four different exposure times (for a total of 24 different samples). Quantitative comparison of relative protein abundances was achieved by spiking an (18)O-labeled "universal" reference into each individually processed unlabeled sample as an internal standard, enabling simultaneous application of both label-free and isotopic labeling quantification across the entire sample set. Clustering analysis of the quantitative proteomics data resulted in distinctive patterns that classified the nanoparticles based on their surface properties and size. In addition, temporal data indicated that the formation of the stable protein corona was at equilibrium within 5 min. The comprehensive quantitative proteomics results obtained in this study provide rich data for computational modeling and have potential implications towards predicting nanoparticle biocompatibility.     

Accurate mass comparison coupled with two endopeptidases enables identification of protein termini

Protein termini play important roles in biological processes, but there have been few methods for comprehensive terminal proteomics. We have developed a new method that can identify both the amino and the carboxyl termini of proteins. The method independently uses two proteases, (lysyl endopeptidase) Lys-C and peptidyl-Lys metalloendopeptidase (Lys-N), to digest proteins, followed by LC-MS/MS analysis of the two digests. Terminal peptides can be identified by comparing the peptide masses in the two digests as follows: (i) the amino terminal peptide of a protein in Lys-C digest is one lysine residue mass heavier than that in Lys-N digest; (ii) the carboxyl terminal peptide in Lys-N digest is one lysine residue mass heavier than that in Lys-C digest; and (iii) all internal peptides give exactly the same molecular masses in both the Lys-C and the Lys-N digest, although amino acid sequences of Lys-C and Lys-N peptides are different (Lys-C peptides end with lysine, whereas Lys-N peptides begin with lysine). The identification of terminal peptides was further verified by examining their MS/MS spectra to avoid misidentifying pairs as termini. In this study, we investigated the usefulness of this method using several protein and peptide mixtures. Known protein termini were successfully identified. Acetylation on N-terminus and protein isoforms, which have different termini, was also determined. These results demonstrate that our new method can confidently identify terminal peptides in protein mixtures.     

2D electrophoresis-based expression proteomics: a microbiologist’s perspective

Quantitative proteomics based on 2D electrophoresis (2-DE) coupled with peptide mass fingerprinting is still one of the most widely used quantitative proteomics approaches in microbiology research. Our view on the exploitation of this global expression analysis technique and its contribution and potential to push forward the field of molecular microbial physiology towards a molecular systems microbiology perspective is discussed in this article. The advances registered in 2-DE-based quantitative proteomic analysis leading to increased protein resolution, sensitivity and accuracy, and the promising use of 2-DE to gain insights into post-translational modifications at a proteome-wide level (considering all the proteins/protein forms expressed by the genome) are focused on. Given the progress made in this field, it is foreseen that the 2-DE-based approach to quantitative proteomics will continue to be a fundamental tool for microbiologists working at a genome-wide scale. Guidelines are also provided for the exploitation of expression proteomics data, based on useful computational tools, and for the integration of these data with other genome-wide expression information. The advantages and limitations of a complete 2-DE-based expression proteomics analysis, envisaging the quantification of the global changes occurring in the proteome of a given cell depending on environmental or genetic manipulations, are discussed from the microbiologist’s perspective. Particular focus is given to the emerging field of toxicoproteomics, a new systems toxicity approach that offers a powerful tool to directly monitor the earliest stages of the toxicological response by identifying critical proteins and pathways that are affected by, and respond to, a chemical stress. The experimental design and the bioinformatics analysis of data used in our laboratory to gain mechanistic insights through expression proteomics into the responses of the eukaryotic model Saccharomyces cerevisiae or of Pseudomonas strains to environmental toxicants are presented as case studies.     

Protein identification and quantification from riverbank grape,Vitis riparia: Comparing SDS‐PAGE and FASP‐GPF techniques for shotgun proteomic analysis

Protein sample preparation optimisation is critical for establishing reproducible high throughput proteomic analysis. In this study, two different fractionation sample preparation techniques (in‐gel digestion and in‐solution digestion) for shotgun proteomics were used to quantitatively compare proteins identified in Vitis riparia leaf samples. The total number of proteins and peptides identified were compared between filter aided sample preparation (FASP) coupled with gas phase fractionation (GPF) and SDS‐PAGE methods. There was a 24% increase in the total number of reproducibly identified proteins when FASP‐GPF was used. FASP‐GPF is more reproducible, less expensive and a better method than SDS‐PAGE for shotgun proteomics of grapevine samples as it significantly increases protein identification across biological replicates. Total peptide and protein information from the two fractionation techniques is available in PRIDE with the identifier PXD001399 ( http://proteomecentral.proteomexchange.org/dataset/PXD001399 ).     

Automated reporting from gel-based proteomics experiments using the open source Proteios database application

The assembly of data from different parts of proteomics workflow is often a major bottleneck in proteomics. Furthermore, there is an increasing demand for the publication of details about protein identifications due to the problems with false-positive and false-negative identifications. In this report, we describe how the open-source Proteios software has been expanded to automate the assembly of the different parts of a gel-based proteomics workflow. In Proteios it is possible to generate protein identification reports that contain all the information currently required by proteomics journals. It is also possible for the user to specify maximum allowed false positive ratios, and reports are automatically generated with the corresponding score cut-offs calculated. When protein identification is conducted using multiple search engines, the score thresholds that correlate to the predetermined error rate are also explicitly calculated for proteins that appear on the result lists of more than one search engine.     

Prioritizing targets for structural biology through the lens of proteomics: The archaeal protein TGAM_1934 from Thermococcus gammatolerans

ORFans are hypothetical proteins lacking any significant sequence similarity with other proteins. Here, we highlighted by quantitative proteomics the TGAM_1934 ORFan from the hyperradioresistant Thermococcus gammatolerans archaeon as one of the most abundant hypothetical proteins. This protein has been selected as a priority target for structure determination on the basis of its abundance in three cellular conditions. Its solution structure has been determined using multidimensional heteronuclear NMR spectroscopy. TGAM_1934 displays an original fold, although sharing some similarities with the 3D structure of the bacterial ortholog of frataxin, CyaY, a protein conserved in bacteria and eukaryotes and involved in iron–sulfur cluster biogenesis. These results highlight the potential of structural proteomics in prioritizing ORFan targets for structure determination based on quantitative proteomics data. The proteomic data and structure coordinates have been deposited to the ProteomeXchange with identifier PXD000402 ( http://proteomecentral.proteomexchange.org/dataset/PXD000402 ) and Protein Data Bank under the accession number 2mcf, respectively.     

Structural proteomics by NMR spectroscopy

Structural proteomics is one of the powerful research areas in the postgenomic era, elucidating structure-function relationships of uncharacterized gene products based on the 3D protein structure. It proposes biochemical and cellular functions of unannotated proteins and thereby identifies potential drug design and protein engineering targets. Recently, a number of pioneering groups in structural proteomics research have achieved proof of structural proteomic theory by predicting the 3D structures of hypothetical proteins that successfully identified the biological functions of those proteins. The pioneering groups made use of a number of techniques, including NMR spectroscopy, which has been applied successfully to structural proteomics studies over the past 10 years. In addition, advances in hardware design, data acquisition methods, sample preparation and automation of data analysis have been developed and successfully applied to high-throughput structure determination techniques. These efforts ensure that NMR spectroscopy will become an important methodology for performing structural proteomics research on a genomic scale. NMR-based structural proteomics together with x-ray crystallography will provide a comprehensive structural database to predict the basic biological functions of hypothetical proteins identified by the genome projects.     

生物质谱技术在蛋白质组学研究中的应用

随着技术的进步,蛋白质组学的研究重心由最初旨在鉴定细胞或组织内基因组所表达的全部蛋白质转移到从整个蛋白质组水平上阐述包括蛋白翻译后修饰、生物大分子相互作用等反映蛋白质功能的层次。多种质谱离子化技术的突破使质谱技术成为蛋白质组学研究必不可少的手段。质谱技术联合蛋白质组学多角度、深层次探索生命系统分子本质成为现阶段生命科学研究领域的主旋律之一。本文简要综述了肽和蛋白质等生物大分子质谱分析的原理、方式和应用,并对其发展前景做出展望。     

Re-fraction: a machine learning approach for deterministic identification of protein homologues and splice variants in large-scale MS-based proteomics

A key step in the analysis of mass spectrometry (MS)-based proteomics data is the inference of proteins from identified peptide sequences. Here we describe Re-Fraction, a novel machine learning algorithm that enhances deterministic protein identification. Re-Fraction utilizes several protein physical properties to assign proteins to expected protein fractions that comprise large-scale MS-based proteomics data. This information is then used to appropriately assign peptides to specific proteins. This approach is sensitive, highly specific, and computationally efficient. We provide algorithms and source code for the current version of Re-Fraction, which accepts output tables from the MaxQuant environment. Nevertheless, the principles behind Re-Fraction can be applied to other protein identification pipelines where data are generated from samples fractionated at the protein level. We demonstrate the utility of this approach through reanalysis of data from a previously published study and generate lists of proteins deterministically identified by Re-Fraction that were previously only identified as members of a protein group. We find that this approach is particularly useful in resolving protein groups composed of splice variants and homologues, which are frequently expressed in a cell- or tissue-specific manner and may have important biological consequences.     

定量蛋白质组同位素标记技术及应用

定量蛋白质组学是对蛋白质组进行精确的定量和鉴定的学科,突破了传统蛋白质组研究集中于对蛋白质的分离和鉴定,着重于定性定量解析细胞蛋白质的动态变化信息,更真实地反映了细胞功能、过程机制等综合信息。以同位素为内标的质谱分析新技术的提出,显示出可同时自动鉴定和精确定量的能力,代表了目前定量蛋白质组研究的主要发展方向。对近年来定量蛋白质组学同位素标记技术和应用研究所取得的重要进展以及最新的发展动态进行了综述。     

PPI spider: A tool for the interpretation of proteomics data in the context of protein–protein interaction networks

Recent advances in experimental technologies allow for the detection of a complete cell proteome. Proteins that are expressed at a particular cell state or in a particular compartment as well as proteins with differential expression between various cells states are commonly delivered by many proteomics studies. Once a list of proteins is derived, a major challenge is to interpret the identified set of proteins in the biological context. Protein–protein interaction (PPI) data represents abundant information that can be employed for this purpose. However, these data have not yet been fully exploited due to the absence of a methodological framework that can integrate this type of information. Here, we propose to infer a network model from an experimentally identified protein list based on the available information about the topology of the global PPI network. We propose to use a Monte Carlo simulation procedure to compute the statistical significance of the inferred models. The method has been implemented as a freely available web‐based tool, PPI spider ( http://mips.helmholtz‐muenchen.de/proj/ppispider ). To support the practical significance of PPI spider, we collected several hundreds of recently published experimental proteomics studies that reported lists of proteins in various biological contexts. We reanalyzed them using PPI spider and demonstrated that in most cases PPI spider could provide statistically significant hypotheses that are helpful for understanding of the protein list.     

Validation of a robust proteomic analysis carried out on formalin‐fixed paraffin‐embedded tissues of the pancreas obtained from mouse and human

A number of reports have recently emerged with focus on extraction of proteins from formalin‐fixed paraffin‐embedded (FFPE) tissues for MS analysis; however, reproducibility and robustness as compared to flash frozen controls is generally overlooked. The goal of this study was to identify and validate a practical and highly robust approach for the proteomics analysis of FFPE tissues. FFPE and matched frozen pancreatic tissues obtained from mice (n = 8) were analyzed using 1D‐nanoLC‐MS(MS)² following work up with commercially available kits. The chosen approach for FFPE tissues was found to be highly comparable to that of frozen. In addition, the total number of unique peptides identified between the two groups was highly similar, with 958 identified for FFPE and 1070 identified for frozen, with protein identifications that corresponded by approximately 80%. This approach was then applied to archived human FFPE pancreatic cancer specimens (n = 11) as compared to uninvolved tissues (n = 8), where 47 potential pancreatic ductal adenocarcinoma markers were identified as significantly increased, of which 28 were previously reported. Further, these proteins share strongly overlapping pathway associations to pancreatic cancer that include estrogen receptor α. Together, these data support the validation of an approach for the proteomic analysis of FFPE tissues that is straightforward and highly robust, which can also be effectively applied toward translational studies of disease.     

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.     

Serum protein profiling by solid phase extraction and mass spectrometry: A future diagnostics tool?

Serum protein profiling by MS is a promising method for early detection of disease. Important characteristics for serum protein profiling are preanalytical factors, analytical reproducibility and high throughput. Problems related to preanalytical factors can be overcome by using standardized and rigorous sample collection and sample handling protocols. The sensitivity of the MS analysis relies on the quality of the sample; consequently, the blood sample preparation step is crucial to obtain pure and concentrated samples and enrichment of the proteins and peptides of interest. This review focuses on the serum sample preparation step prior to protein profiling by MALDI MS analysis, with particular focus on various SPE methods. The application of SPE techniques with different chromatographic properties such as RP, ion exchange, or affinity binding to isolate specific subsets of molecules (subproteomes) is advantageous for increasing resolution and sensitivity in the subsequent MS analysis. In addition, several of the SPE sample preparation methods are simple and scalable and have proven easy to automate for higher reproducibility and throughput, which is important in a clinical proteomics setting.     

Metabolomics‐proteomics profiles delineate metabolic changes in kidney fibrosis disease

Kidney fibrosis (KF) is a common process that leads to the progression of various types of kidney disease including kidney‐yang deficiency syndrome, however, little is known regarding the underlying biology of this disorder. Fortunately, integrated omics approaches provide the molecule fingerprints related to the disease. In an attempt to address this issue, we integrated metabolomics–proteomics profiles analyzed pathogenic mechanisms of KF based on rat model. A total 37 serum differential metabolites were contributed to KF progress, involved several important metabolic pathways. Using iTRAQ‐based quantitative proteomics analysis, 126 differential serum proteins were identified and provide valuable insight into the underlying mechanisms of KF. These proteins appear to be involved in complement and coagulation cascades, regulation of actin cytoskeleton, MAPK signaling pathway, RNA transport, etc. Interestingly, pathway/network analysis of integrated proteomics and metabolomics data firstly reveals that these signaling pathways were closely related with KF. It further indicated that most of these proteins play a pivotal role in the regulation of metabolism pathways.