Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics

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Highlights

• •Unified identification and quantification error rates for protein quantification.
• •Error propagation using graphical models and Bayesian statistics.
• •Account for uncertainty of missing values instead of overconfident point estimates.
• •Control of differential expression false discovery rate at increased sensitivity.

     

pseudoQC: A Regression‐Based Simulation Software for Correction and Normalization of Complex Metabolomics and Proteomics Datasets

Various types of unwanted and uncontrollable signal variations in MS‐based metabolomics and proteomics datasets severely disturb the accuracies of metabolite and protein profiling. Therefore, pooled quality control (QC) samples are often employed in quality management processes, which are indispensable to the success of metabolomics and proteomics experiments, especially in high‐throughput cases and long‐term projects. However, data consistency and QC sample stability are still difficult to guarantee because of the experimental operation complexity and differences between experimenters. To make things worse, numerous proteomics projects do not take QC samples into consideration at the beginning of experimental design. Herein, a powerful and interactive web‐based software, named pseudoQC, is presented to simulate QC sample data for actual metabolomics and proteomics datasets using four different machine learning‐based regression methods. The simulated data are used for correction and normalization of the two published datasets, and the obtained results suggest that nonlinear regression methods perform better than linear ones. Additionally, the above software is available as a web‐based graphical user interface and can be utilized by scientists without a bioinformatics background. pseudoQC is open‐source software and freely available at https://www.omicsolution.org/wukong/pseudoQC/ .     

Robust Summarization and Inference in Proteome-wide Label-free Quantification

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Highlights

• •In depth performance assessment of leading tools for differential protein abundance.
• •Novel fast modular framework MSqRobSum for robust protein summarization and inference.
• •MsqRobSum outperforms leading protein summarization-based tools.
• •MSqRobSum is on par with top-performing peptide based tool MSqRob.

     

SeqTrace: A Graphical Tool for Rapidly Processing DNA Sequencing Chromatograms

Modern applications of Sanger DNA sequencing often require converting a large number of chromatogram trace files into high-quality DNA sequences for downstream analyses. Relatively few nonproprietary software tools are available to assist with this process. SeqTrace is a new, free, and open-source software application that is designed to automate the entire workflow by facilitating easy batch processing of large numbers of trace files. SeqTrace can identify, align, and compute consensus sequences from matching forward and reverse traces, filter low-quality base calls, and end-trim finished sequences. The software features a graphical interface that includes a full-featured chromatogram viewer and sequence editor. SeqTrace runs on most popular operating systems and is freely available, along with supporting documentation, at http://seqtrace.googlecode.com/.     

DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis

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Highlights

• •DEqMS is a method for statistical analysis of quantitative MS-data.
• •Variance estimates based on the actual MS-data structure.
• •Improved statistical power and accuracy in protein differential analysis.
• •DEqMS is available as a user-friendly R package in Bioconductor.

     

Analytical Guidelines for co-fractionation Mass Spectrometry Obtained through Global Profiling of Gold Standard Saccharomyces cerevisiae Protein Complexes

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Highlights

• •Guidelines for studying protein complexes via co-fractionation mass spectrometry.
• •A novel procedure for profiling gold standard protein complexes in CF-MS data.
• •Recommendations for efficient CF-MS fractionation collection.
• •Scoring metric recommendations for precise and sensitive CF-MS data analysis.

     

The Mitochondrial Acyl-carrier Protein Interaction Network Highlights Important Roles for LYRM Family Members in Complex I and Mitoribosome Assembly

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Highlights

• •The interaction network of NDUFAB1 reveal associations with 9 known LYRM proteins as well as more than 20 other proteins involved in mitochondrial respiratory chain complex and mitochondrial ribosome assembly.
• •The LYRM protein AltMiD51 is required for the stability of assembly factor MALSU1 and optimal assembly of the mitoribosome.
• •LYRM2 is important for integration of the N-module into respiratory chain complex I.

     

规模化蛋白质鉴定中母离子的准确检测技术研究

蛋白质组学的基础研究之一是蛋白质鉴定.规模化的蛋白质鉴定通常采用"鸟枪法",即选择一些酶切肽段(母离子)碎裂生成二级谱图,通过二级谱图及其母离子质量鉴定肽段,再推断对应的蛋白质.在鉴定过程中,母离子质量是一个关键参数.母离子是否是肽段的单同位素峰决定了正确肽段是否能进入候选,母离子的质量精度决定了候选肽段的数目.本文从判断单同位素峰和系统误差校准这两个角度研究了母离子的准确检测技术.判断单同位素峰的技术在蛋白质上已有研究,包括电荷判断、单同位素峰判断和重叠同位素峰判断.可以借鉴蛋白质水平的技术研究母离子的单同位素峰判断方法.同时母离子的系统误差校准也有较为成熟的方法.这两个角度的研究有助于提高规模化蛋白质的鉴定率.     

Simultaneous Quantification of Protein Expression and Modifications by Top-down Targeted Proteomics: A Case of the Sarcomeric Subproteome

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Highlights

• •A new strategy for simultaneous quantification of protein expression and modification.
• •This top-down LC/MS-based method shows high reproducibility and high throughput.
• •Quantification at the intact protein level with results comparable to Western blot.
• •This top-down proteomics method is applicable to different species and tissues.

     

Comparative genomics and interactomics of polyadenylation factors for the prediction of new parasite targets: Entamoeba histolytica as a working model

Protein–protein interactions (PPI) play a key role in predicting the function of a target protein and drug ability to affect an entire biological system. Prediction of PPI networks greatly contributes to determine a target protein and signal pathways related to its function. Polyadenylation of mRNA 3′-end is essential for gene expression regulation and several polyadenylation factors have been shown as valuable targets for controlling protozoan parasites that affect human health. Here, by using a computational strategy based on sequence-based prediction approaches, phylogenetic analyses, and computational prediction of PPI networks, we compared interactomes of polyadenylation factors in relevant protozoan parasites and the human host, to identify key proteins and define potential targets for pathogen control. Then, we used Entamoeba histolytica as a working model to validate our computational results. RT-qPCR assays confirmed the coordinated modulation of connected proteins in the PPI network and evidenced that silencing of the bottleneck protein EhCFIm25 affects the expression of interacting proteins. In addition, molecular dynamics simulations and docking approaches allowed to characterize the relationships between EhCFIm25 and Ehnopp34, two connected bottleneck proteins. Interestingly, the experimental identification of EhCFIm25 interactome confirmed the close relationships among proteins involved in gene expression regulation and evidenced new links with moonlight proteins in E. histolytica, suggesting a connection between RNA biology and metabolism as described in other organisms. Altogether, our results strengthened the relevance of comparative genomics and interactomics of polyadenylation factors for the prediction of new targets for the control of these human pathogens.     

Improving Identification of In-organello Protein-Protein Interactions Using an Affinity-enrichable,Isotopically Coded,and Mass Spectrometry-cleavable Chemical Crosslinker

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Highlights

• •Used affinity-enrichable, isotopically coded, and MS-cleavable crosslinker.
• •Targeted acquisition strategy based on isotopic-coding described and evaluated.
• •Novel data analysis pipeline developed provides improved crosslink identification.
• •Large dataset reveals hundreds of mitochondrial protein-protein interactions.

     

CalibraCurve: A Tool for Calibration of Targeted MS‐Based Measurements

Targeted proteomics techniques allow accurate quantitative measurements of analytes in complex matrices with dynamic linear ranges that span up to 4–5 orders of magnitude. Hence, targeted methods are promising for the development of robust protein assays in several sensitive areas, for example, in health care. However, exploiting the full method potential requires reliable determination of the dynamic range along with related quantification limits for each analyte. Here, a software named CalibraCurve that enables an automated batch‐mode determination of dynamic linear ranges and quantification limits for both targeted proteomics and similar assays is presented. The software uses a variety of measures to assess the accuracy of the calibration, namely precision and trueness. Two different kinds of customizable graphs are created (calibration curves and response factor plots). The accuracy measures and the graphs offer an intuitive, detailed, and reliable opportunity to assess the quality of the model fit. Thus, CalibraCurve is deemed a highly useful and flexible tool to facilitate the development and control of reliable SRM/MRM‐MS‐based proteomics assays.     

Temporal Quantitative Proteomics of mGluR-induced Protein Translation and Phosphorylation in Neurons

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Highlights

• •Integrated phosphoproteomics and analyses of newly synthesized proteins in neurons.
• •Resource of temporal mGluR-induced signaling pathways upon DHPG stimulation.
• •Validation of PKC, MAPK1, CAMKIIa, and CDK2 in mGluR-activation and signaling.
• •Validation of Intersectin-1 in DHPG-induced AMPAR internalization.

     

基于全局角度网络策略的复杂疾病风险通路识别

复杂疾病的发生发展与机体内生物学通路的功能紊乱有密切联系,从高通量数据出发,利用计算机辅助方法来研究疾病与通路间的关系具有重要意义.本文提出了一个新的基于网络的全局性通路识别方法.该方法利用蛋白质互作信息和通路的基因集组成信息构建复杂的蛋白质-通路网.然后,基于表达谱数据,通过随机游走算法从全局层面优化疾病风险通路.最终,通过扰动方式识别统计学显著的风险通路.将该网络运用于结肠直肠癌风险通路识别,识别出15个与结肠直肠癌发生与发展过程显著相关的通路.通过与其他通路识别方法(超几何检验,SPIA)相比较,该方法能够更有效识别出疾病相关的风险通路.     

A Comparison of Protein Quantitation Assays for Biopharmaceutical Applications

Dye-based protein determination assays are widely used to estimate protein concentration, however various reports suggest that the response is dependent on the composition and sequence of the protein, limiting confidence in the resulting concentration estimates. In this study a diverse set of model proteins representing various sizes of protein and covalent modifications, some typical of biopharmaceuticals have been used to assess the utility of dye-based protein concentration assays. The protein concentration assays (Bicinchoninic acid (BCA), Bradford, 3-(4-carboxybenzoyl)quinoline-2-carboxaldehyde (CBQCA), DC, Fluorescamine and Quant-i) were compared to the 'gold standard' assay, quantitative amino acid analysis (AAA). The assays that displayed the lowest variability between proteins, BCA and DC, also generated improved estimates when BSA was used as a standard, when compared to AAA derived concentrations. Assays read out by absorbance tended to display enhanced robustness and repeatability, whereas the fluorescence based assays had wider quantitation ranges and lower limits of detection. Protein modification, in the form of glycosylation and PEGylation, and the addition of excipients, were found to affect the estimation of protein concentration for some of the assays when compared to the unmodified protein. We discuss the suitability and limitations of the selected assays for the estimation of protein concentration in biopharmaceutical applications.     

Rapid development of proteomics in China: from the perspective of the Human Liver Proteome Project and technology development

Proteomics focuses on the systematic identification and quantification of entire proteomes and interpretation of proteins’biological functions.During the last decade,proteomics in China has grown much faster than other research fields in life sciences.At the beginning of the second decade of the 21st century,the rapid development of high-resolution and high-speed mass spectrometry makes proteomics a powerful tool to study the mechanisms underlying physiological/pathological processes in organisms.This article provides a brief overview of proteomics technology development and representative scientific progress of the Human Liver Proteome Project in China over the past three years.     

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.     

Multivariate analysis of single quadrupole LC‐MS spectra for routine characterization and quantification of intact proteins

Modern high‐throughput proteomic platforms allow incomparable protein mixture resolution and identification. However, such sophisticated facilities are expensive and not always accessible for routine analysis of simple mixtures. In this paper, we propose a simple methodology, based on detection of intact, nondigested proteins by LC coupled to single quadrupole MS (sqLC‐MS), followed by the analysis of the resulting spectra by multivariate analysis (MA). By doing so, even large molecular weight (MW) proteins, generating complex spectra, can be characterized to a level that allows isoform discrimination, while standard algorithms, such as MS spectrum deconvolution, cannot. To demonstrate the effectiveness of the proposed approach, we have analyzed the spectra of a set of purified, intact albumins from seven different organisms (bovine, human, rabbit, rat, sheep, mouse, and pig) as a model of microheterogenous proteins, using Projection to Latent Structure Discriminant Analysis (PLS‐DA). Although these proteins are very similar (less than 1% difference in MW), sqLC‐MS/MA allowed their classification, and the identification of unknown source samples. In addition, MA allowed precise protein quantification from the same data (calibration curve R² = 0.9966). The ability to rapidly characterize and quantify proteins, together with simplicity and affordability, could make of combined sqLC‐MS/MA a routine method for the characterization of simple mixture of known proteins.