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.     

An overview of human protein databases and their application to functional proteomics in health and disease

Functional proteomics can be defined as a strategy to couple proteomic information with biochemical and physiological analyses with the aim of understanding better the functions of proteins in normal and diseased organs. In recent years, a variety of publicly available bioinformatics databases have been developed to support protein-related information management and biological knowledge discovery. In addition to being used to annotate the proteome, these resources also offer the opportunity to develop global approaches to the study of the functional role of proteins both in health and disease. Here, we present a comprehensive review of the major human protein bioinformatics databases. We conclude this review by discussing a few examples that illustrate the importance of these databases in functional proteomics research.     

Current trends in computational inference from mass spectrometry-based proteomics

Mass spectrometry offers a high-throughput approach to quantifying the proteome associated with a biological sample and hence has become the primary approach of proteomic analyses. Computation is tightly coupled to this advanced technological platform as a required component of not only peptide and protein identification, but quantification and functional inference, such as protein modifications and interactions. Proteomics faces several key computational challenges such as identification of proteins and peptides from tandem mass spectra as well as their quantitation. In addition, the application of proteomics to systems biology requires understanding the functional proteome, including how the dynamics of the cell change in response to protein modifications and complex interactions between biomolecules. This review presents an overview of recently developed methods and their impact on these core computational challenges currently facing proteomics.     

Advances in quantitative proteomics via stable isotope tagging and mass spectrometry

The high-throughput identification and accurate quantification of proteins are essential components of proteomic strategies for studying cellular functions and processes. Techniques that are largely based on stable isotope protein or peptide labeling and automated tandem mass spectrometry are increasingly being applied in quantitative proteomic studies. Over the past year, significant progress has been made toward improving and diversifying these technologies with respect to the methods for stable isotope labeling, process automation and data processing and analysis. Advances in stable isotope protein labeling and recent biological studies that used stable isotope based quantitative proteomics techniques are reviewed.     

Gel-based mass spectrometric and computational approaches to the mitochondrial proteome of Neurospora

We have used gel electrophoretic techniques including isoelectric focusing, blue native, acid-urea, 16-benzyldimethyl-n-hexadecylammonium chloride or SDS first dimensions and SDS Laemmli or tricine second dimensions to separate the proteins from highly-purified Neurospora mitochondria and sub-mitochondrial fractions (membrane, soluble, protein complexes and ribonucleoproteins). The products of 260 genes, many of them in multiple processed or modified forms, were identified by MALDI-TOF mass spectrometry. This work confirms the existence, expression, and mitochondrial localization of the products of 55 Neurospora genes previously annotated only as predicted or hypothetical, and of 101 genes not identified in previous mass spectrometry studies. Combined with previous mass spectrometry studies, and re-evaluation of genome annotations, we have compiled a curated list of 358 proteins identified in proteomic studies that are likely to be bona fide mitochondrial proteins plus 80 other identified proteins that may be mitochondrial. Literature data mining and computational predictions suggest that Neurospora mitochondria also contain another 299 proteins not yet identified in proteomics projects. Taken together, these data suggest that the products of at least 738 genes comprise the Neurospora mitochondrial proteome.     

PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics

In recent years proteomics became increasingly important to functional genomics. Although a large amount of data is generated by high throughput large‐scale techniques, a connection of these mostly heterogeneous data from different analytical platforms and of different experiments is limited. Data mining procedures and algorithms are often insufficient to extract meaningful results from large datasets and therefore limit the exploitation of the generated biological information. In our proteomic core facility, which almost exclusively focuses on 2‐DE/MS‐based proteomics, we developed a proteomic database custom tailored to our needs aiming at connecting MS protein identification information to 2‐DE derived protein expression profiles. The tools developed should not only enable an automatic evaluation of single experiments, but also link multiple 2‐DE experiments with MS‐data on different levels and thereby helping to create a comprehensive network of our proteomics data. Therefore the key feature of our “PROTEOMER” database is its high cross‐referencing capacity, enabling integration of a wide range of experimental data. To illustrate the workflow and utility of the system, two practical examples are provided to demonstrate that proper data cross‐referencing can transform information into biological knowledge.     

植物蛋白质组学研究进展

 蛋白质组学是后基因组时代功能基因组学研究的新兴学科和热点领域。该文简要介绍了蛋白质组学产生的科学背景、研究方法和研究内容。蛋白质组学研究方法主要有双向聚丙烯酰胺凝胶电泳（2D-PAGE）、质谱(Mass-spectrometric)技术、蛋白质芯片（Protein chips）技术、酵母双杂交系统（Yeast two-hybrid system）、植物蛋白质组数据库等。其应用的范围包括植物群体遗传学、在个体水平上植物对生物和非生物环境的适应机制、植物的发育和组织器官的分化过程,以及不同亚细胞结构在生理生态过程中的作用等诸多方面。同时对植物蛋白质组学的发展前景进行了展望。     

Rapid proteomic remodeling of cardiac tissue caused by total body ionizing radiation

Accidental nuclear scenarios lead to environmental contamination of unknown level. Immediate radiation‐induced biological responses that trigger processes leading to adverse health effects decades later are not well understood. A comprehensive proteomic analysis provides a promising means to identify and quantify the initial damage after radiation exposure. Early changes in the cardiac tissue of C57BL/6 mice exposed to total body irradiation were studied, using a dose relevant to both intentional and accidental exposure (3 Gy gamma ray). Heart tissue protein lysates were analyzed 5 and 24 h after the exposure using isotope‐coded protein labeling (ICPL) and 2‐dimensional difference‐in‐gel‐electrophoresis (2‐D DIGE) proteomics approaches. The differentially expressed proteins were identified by LC‐ESI‐MS‐MS. Both techniques showed similar functional groups of proteins to be involved in the initial injury. Pathway analyses indicated that total body irradiation immediately induced biological responses such as inflammation, antioxidative defense, and reorganization of structural proteins. Mitochondrial proteins represented the protein class most sensitive to ionizing radiation. The proteins involved in the initial damage processes map to several functional categories involving cardiotoxicity. This prompts us to propose that these early changes are indicative of the processes that lead to an increased risk of cardiovascular disease after radiation exposure.     

Proteomics of Protein Secretion by Bacillus subtilis: Separating the “Secrets” of the Secretome

Secretory proteins perform a variety of important “remote-control” functions for bacterial survival in the environment. The availability of complete genome sequences has allowed us to make predictions about the composition of bacterial machinery for protein secretion as well as the extracellular complement of bacterial proteomes. Recently, the power of proteomics was successfully employed to evaluate genome-based models of these so-called secretomes. Progress in this field is well illustrated by the proteomic analysis of protein secretion by the gram-positive bacterium Bacillus subtilis, for which ~90 extracellular proteins were identified. Analysis of these proteins disclosed various “secrets of the secretome,” such as the residence of cytoplasmic and predicted cell envelope proteins in the extracellular proteome. This showed that genome-based predictions reflect only ~50% of the actual composition of the extracellular proteome of B. subtilis. Importantly, proteomics allowed the first verification of the impact of individual secretion machinery components on the total flow of proteins from the cytoplasm to the extracellular environment. In conclusion, proteomics has yielded a variety of novel leads for the analysis of protein traffic in B. subtilis and other gram-positive bacteria. Ultimately, such leads will serve to increase our understanding of virulence factor biogenesis in gram-positive pathogens, which is likely to be of high medical relevance.     

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.     

The need to revisit published data: A concept and framework for complementary proteomics

Tandem proteomic strategies based on large‐scale and high‐resolution mass spectrometry have been widely applied in various biomedical studies. However, protein sequence databases and proteomic software are continuously updated. Proteomic studies should not be ended with a stable list of proteins. It is necessary and beneficial to regularly revise the results. Besides, the original proteomic studies usually focused on a limited aspect of protein information and valuable information may remain undiscovered in the raw spectra. Several studies have reported novel findings by reanalyzing previously published raw data. However, there are still no standard guidelines for comprehensive reanalysis. In the present study, we proposed the concept and draft framework for complementary proteomics, which are aimed to revise protein list or mine new discoveries by revisiting published data.     

Whole genome searching with shotgun proteomic data: applications for genome annotation

High-throughput genome sequencing continues to accelerate the rate at which complete genomes are available for biological research. Many of these new genome sequences have little or no genome annotation currently available and hence rely upon computational predictions of protein coding genes. Evidence of translation from proteomic techniques could facilitate experimental validation of protein coding genes, but the techniques for whole genome searching with MS/MS data have not been adequately developed to date. Here we describe GENQUEST, a novel method using peptide isoelectric focusing and accurate mass to greatly reduce the peptide search space, making fast, accurate, and sensitive whole human genome searching possible on common desktop computers. In an initial experiment, almost all exonic peptides identified in a protein database search were identified when searching genomic sequence. Many peptides identified exclusively in the genome searches were incorrectly identified or could not be experimentally validated, highlighting the importance of orthogonal validation. Experimentally validated peptides exclusive to the genomic searches can be used to reannotate protein coding genes. GENQUEST represents an experimental tool that can be used by the proteomics community at large for validating computational approaches to genome annotation.     

Proteomics of the lysosome

Defects in lysosomal function have been associated with numerous monogenic human diseases typically classified as lysosomal storage diseases. However, there is increasing evidence that lysosomal proteins are also involved in more widespread human diseases including cancer and Alzheimer disease. Thus, there is a continuing interest in understanding the cellular functions of the lysosome and an emerging approach to this is the identification of its constituent proteins by proteomic analyses. To date, the mammalian lysosome has been shown to contain approximately 60 soluble luminal proteins and approximately 25 transmembrane proteins. However, recent proteomic studies based upon affinity purification of soluble components or subcellular fractionation to obtain both soluble and membrane components suggest that there may be many more of both classes of protein resident within this organelle than previously appreciated. Discovery of such proteins has important implications for understanding the function and the dynamics of the lysosome but can also lead the way towards the discovery of the genetic basis for human diseases of hitherto unknown etiology. Here, we describe current approaches to lysosomal proteomics and data interpretation and review the new lysosomal proteins that have recently emerged from such studies.     

Binding mode analysis of a major T3SS translocator protein PopB with its chaperone PcrH from Pseudomonas aeruginosa

Pseudomonas aeruginosa, a Gram‐negative pathogen uses a specialized set of Type III secretion system (T3SS) translocator proteins to establish virulence in the host cell. An understanding of the factors that govern translocation by the translocator protein–chaperone complex is thus of immense importance. In this work, experimental and computational techniques were used to probe into the structure of the major translocator protein PopB from P. aeruginosa and to identify the important regions involved in functioning of the translocator protein. This study reveals that the binding sites of the common chaperone PcrH, needed for maintenance of the translocator PopB within the bacterial cytoplasm, which are primarily localized within the N‐terminal domain. However, disordered and flexible residues located both at the N‐ and C‐terminal domains are also observed to be involved in association with the chaperone. This intrinsic disorderliness of the terminal domains is conserved for all the major T3SS translocator proteins and is functionally important to maintain the intrinsically disordered state of the translocators. Our experimental and computational analyses suggest that a “disorder‐to‐order” transition of PopB protein might take place upon PcrH binding. The long helical coiled‐coil part of PopB protein perhaps helps in pore formation while the flexible apical region is involved in chaperone interaction. Thus, our computational model of translocator protein PopB and its binding analyses provide crucial functional insights into the T3SS translocation mechanism. Proteins 2014; 82:3273–3285. © 2014 Wiley Periodicals, Inc.     

Genome annotating proteomics pipelines: available tools

Proteomics based on tandem mass spectrometry is a powerful tool for identifying novel biomarkers and drug targets. Previously, a major bottleneck in high-throughput proteomics has been that the computational techniques needed to reliably identify proteins from proteomic data lagged behind the ability to collect the immense quantity of data generated. This is no longer the case, as fully automated pipelines for peptide and protein identification exist, and these are publicly and privately accessible. Such pipelines can automatically and rapidly generate high-confidence protein identifications from large datasets in a searchable format covering multiple experimental runs. However, the main challenge for the community now is to use these resources as they are, by taking full advantage of the pooling of information, so that the next barrier in our understanding of biology may be broken. There are currently two pipelines in the public domain that provide such potential: PeptideAtlas and the Genome Annotating Proteomic Pipeline. This review will introduce their features in the context of high-throughput proteomics, and provide indicative results as to their usefulness and usability through a side-by-side comparison of results obtained when processing a set of human plasma samples.     

Genome annotating proteomics pipelines: available tools

Proteomics based on tandem mass spectrometry is a powerful tool for identifying novel biomarkers and drug targets. Previously, a major bottleneck in high-throughput proteomics has been that the computational techniques needed to reliably identify proteins from proteomic data lagged behind the ability to collect the immense quantity of data generated. This is no longer the case, as fully automated pipelines for peptide and protein identification exist, and these are publicly and privately accessible. Such pipelines can automatically and rapidly generate high-confidence protein identifications from large datasets in a searchable format covering multiple experimental runs. However, the main challenge for the community now is to use these resources as they are, by taking full advantage of the pooling of information, so that the next barrier in our understanding of biology may be broken. There are currently two pipelines in the public domain that provide such potential: PeptideAtlas and the Genome Annotating Proteomic Pipeline. This review will introduce their features in the context of high-throughput proteomics, and provide indicative results as to their usefulness and usability through a side-by-side comparison of results obtained when processing a set of human plasma samples.