The optimal exponent base for emPAI is 6.5

Exponentially Modified Protein Abundance Index (emPAI) is an established method of estimating protein abundances from peptide counts in a single LC-MS/MS experiment. EmPAI is defined as 10(PAI) minus one, where PAI (Protein Abundance Index) denotes the ratio of observed to observable peptides. EmPAI was first proposed by Ishihama et al [1] who found that PAI is approximately proportional to the logarithm of absolute protein concentration. I define emPAI65 = 6.5(PAI)-1 and show that it performs significantly better than emPAI, while it is equally easy to compute. The higher accuracy of emPAI65 is demonstrated by analyzing three data sets, including the one used in the original study [1]. I conclude that emPAI65 ought to be used instead of the original emPAI for protein quantitation.     

Comparative evaluation of label-free SINQ normalized spectral index quantitation in the central proteomics facilities pipeline

Normalized spectral index quantification was recently presented as an accurate method of label‐free quantitation, which improved spectral counting by incorporating the intensities of peptide MS/MS fragment ions into the calculation of protein abundance. We present SINQ, a tool implementing this method within the framework of existing analysis software, our freely available central proteomics facilities pipeline (CPFP). We demonstrate, using data sets of protein standards acquired on a variety of mass spectrometers, that SINQ can rapidly provide useful estimates of the absolute quantity of proteins present in a medium‐complexity sample. In addition, relative quantitation of standard proteins spiked into a complex lysate background and run without pre‐fractionation produces accurate results at amounts above 1 fmol on column. We compare quantitation performance to various precursor intensity‐ and identification‐based methods, including the normalized spectral abundance factor (NSAF), exponentially modified protein abundance index (emPAI), MaxQuant, and Progenesis LC‐MS. We anticipate that the SINQ tool will be a useful asset for core facilities and individual laboratories that wish to produce quantitative MS data, but lack the necessary manpower to routinely support more complicated software workflows. SINQ is freely available to obtain and use as part of the central proteomics facilities pipeline, which is released under an open‐source license.     

Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry

For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the "best flyer" hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R(2) of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.     

Bacterial proteins with cleaved or uncleaved signal peptides of the general secretory pathway

Correct protein compartmentalization is a key step for molecular function and cell viability, and this is especially true for membrane and externalized proteins of bacteria. Recent proteomic reports of Bacillus subtilis have shown that many proteins with Sec-like signal peptides and absence of a transmembrane helix domain are still observed in membrane-enriched fractions, but further evidence about signal peptide cleavage or soluble protein contamination is still needed. Here we report a proteomic screening of identified peptides in culture filtrate, membrane fraction and whole cell lysate of Mycobacterium tuberculosis. We were able to detect peptide sequencing evidence that shows that the predicted signal peptide was kept uncleaved for several types of proteins such as mammalian cell entry (Mce) proteins and PE or PE-PGRS proteins. Label-free quantitation of all proteins identified in each fraction showed that the majority of these proteins with uncleaved signal peptides are, indeed, enriched in the Triton X-114 lipid phase. Some of these proteins are likely to be located in the inner membrane while others may be outer membrane proteins.     

Large scale protein profiling by combination of protein fractionation and multidimensional protein identification technology (MudPIT)

In the past decade, shotgun proteomic analysis has been utilized extensively to answer complex biological questions. New challenges arise in large scale proteomic profiling when dealing with complex biological mixtures such as the mammalian cell lysate. In this study, we explored the approach of protein separation prior to the shotgun multidimensional protein identification technology (MudPIT) analysis. We fractionated the mammalian cancer cell lysate using the PF 2D ProteomeLab system and analyzed the distribution of molecular weight, isoelectric point, and cellular localization of the eluted proteins. As a result, we were able to reduce sample complexity by protein fractionation and increase the possibility of detecting proteins with lower abundance in the complex protein mixture.     

Isobaric Labeling and Data Normalization without Requiring Protein Quantitation

Isobaric multiplexed quantitative proteomics can complement high-resolution sample isolation techniques. Here, we report a simple workflow exponentially modified protein abundance index (emPAI)-MW deconvolution (EMMOL) for normalizing isobaric reporter ratios within and between experiments, where small or unknown amounts of protein are used. EMMOL deconvolutes the isobaric tags for relative and absolute quantification (iTRAQ) data to yield the quantity of each protein of each sample in the pool, a new approach that enables the comparison of many samples without including a channel of reference standard. Moreover, EMMOL allows using a sufficient quantity of control sample to facilitate the peptide fractionation (isoelectric-focusing was used in this report), and mass spectrometry MS/MS sequencing yet relies on the broad dynamic range of iTRAQ quantitation to compare relative protein abundance. We demonstrated EMMOL by comparing four pooled samples with 20-fold range differences in protein abundance and performed data normalization without using prior knowledge of the amounts of proteins in each sample, simulating an iTRAQ experiment without protein quantitation prior to labeling. We used emPAI,¹ the target protein MW, and the iTRAQ reporter ratios to calculate the amount of each protein in each of the four channels. Importantly, the EMMOL-delineated proteomes from separate iTRAQ experiments can be assorted for comparison without using a reference sample. We observed no compression of expression in iTRAQ ratios over a 20-fold range for all protein abundances. To complement this ability to analyze minute samples, we report an optimized iTRAQ labeling protocol for using 5 μg protein as the starting material.     

Identification of highly expressed, soluble proteins using an improved, high-throughput pooled ORF expression technology

This article describes an improved pooled open reading frame (ORF) expression technology (POET) that uses recombinational cloning and solution-based tandem mass spectrometry (MS/MS) to identify ORFs that yield high levels of soluble, purified protein when expressed in Escherichia coli. Using this method, three identical pools of 512 human ORFs were subcloned, purified, and transfected into three separate E. coli cultures. After bulk expression and purification, the proteins from the three separate pools were digested into tryptic peptides. Each of these samples was subsequently analyzed in triplicate using reversed-phase high-performance liquid chromatography (LC) coupled directly online with MS/MS. The abundance of each protein was determined by calculating the average exponentially modified protein abundance index (emPAI) of each protein across the three protein pools. Human proteins that consistently gave high emPAI values were subjected to small-scale expression and purification. These clones showed high levels of expression of soluble protein. Conversely, proteins that were not observed by LC-MS/MS did not show any detectable soluble expression in small-scale validation studies. Using this improved POET method allows the expression characteristics of hundreds of proteins to be quickly determined in a single experiment.     

Prediction of missed proteolytic cleavages for the selection of surrogate peptides for quantitative proteomics

Abstract Quantitative proteomics experiments are usually performed using proteolytic peptides as surrogates for their parent proteins, inferring protein amounts from peptide-level quantitation. This process is frequently dependent on complete digestion of the parent protein to its limit peptides so that their signal is truly representative. Unfortunately, proteolysis is often incomplete, and missed cleavage peptides are frequently produced that are unlikely to be optimal surrogates for quantitation, particularly for label-mediated approaches seeking to derive absolute values. We have generated a predictive computational tool that is able to predict which candidate proteolytic peptide bonds are likely to be missed by the standard enzyme trypsin. Our cross-validated prediction tool uses support vector machines and achieves high accuracy in excess of 0.94 precision (PPV), with attendant high sensitivity of 0.79, across multiple proteomes. We believe this is a useful tool for selecting candidate quantotypic peptides, seeking to minimize likely loss owing to missed cleavage, which will be a boon for quantitative proteomic pipelines as well as other areas of proteomics. Our results are discussed in the context of recent results examining the kinetics of missed cleavages in proteomic digestion protocols, and show agreement with observed experimental trends. The software has been made available at http://king.smith.man.ac.uk/mcpred .     

De novo peptide sequencing and quantitative profiling of complex protein mixtures using mass-coded abundance tagging

Proteomic studies require efficient, robust, and practical methods of characterizing proteins present in biological samples. Here we describe an integrated strategy for systematic proteome analysis based on differential guanidination of C-terminal lysine residues on tryptic peptides followed by capillary liquid chromatography-electrospray tandem mass spectrometry. The approach, termed mass-coded abundance tagging (MCAT), facilitates the automated, large-scale, and comprehensive de novo determination of peptide sequence and relative quantitation of proteins in biological samples in a single analysis. MCAT offers marked advantages as compared with previously described methods and is simple, economic, and effective when applied to complex proteomic mixtures. MCAT is used to identify proteins, including polymorphic variants, from complex mixtures and measure variation in protein levels from diverse cell types.     

Hexapeptide libraries for enhanced protein PTM identification and relative abundance profiling in whole human saliva

Dynamic range compression (DRC) by hexapeptide libraries increases MS/MS-based identification of lower-abundance proteins in complex mixtures. However, two unanswered questions impede fully realizing DRC's potential in shotgun proteomics. First, does DRC enhance identification of post-translationally modified proteins? Second, can DRC be incorporated into a workflow enabling relative protein abundance profiling? We sought to answer both questions analyzing human whole saliva. Addressing question one, we coupled DRC with covalent glycopeptide enrichment and MS/MS. With DRC we identified ～2 times more N-linked glycoproteins and their glycosylation sites than without DRC, dramatically increasing the known salivary glycoprotein catalog. Addressing question two, we compared differentially stable isotope-labeled saliva samples pooled from healthy and metastatic breast cancer women using a multidimensional peptide fractionation-based workflow, analyzing in parallel one sample portion with DRC and one portion without. Our workflow categorizes proteins with higher absolute abundance, whose relative abundance ratios are altered by DRC, from proteins of lower absolute abundance detected only after DRC. Within each of these salivary protein categories, we identified novel abundance changes putatively associated with breast cancer, demonstrating feasibility and benefits of DRC for relative abundance profiling. Collectively, our results bring us closer to realizing the full potential of DRC for proteomic studies.     

Absolute SILAC for accurate quantitation of proteins in complex mixtures down to the attomole level

Mass spectrometry based proteomics can routinely identify hundreds of proteins in a single LC-MS run, and methods have been developed for relative quantitation between differentially treated samples using stable isotopes. However, absolute quantitation has so far required addition of a labeled standard late in the experimental workflow, introducing variability due to sample preparation. Here we present a new variant of the stable isotope labeling by amino acids in cell culture (SILAC) technique termed "Absolute SILAC" that allows accurate quantitation of selected proteins in complex mixtures. SILAC-labeled recombinant proteins produced in vivo or in vitro are used as internal standards, which are directly mixed into lysates of cells or tissues. This minimizes differences in sample processing between the isotope-labeled standard and its endogenous counterpart. We show that it is possible to quantify over several orders of magnitude, even in the background of a whole cell lysate. We furthermore devise a strategy to quantify peptides at or below their signal-to-noise level on hybrid ion trap instruments, shown here for the LTQ-Orbitrap. The data system triggers on peptides of the SILAC-labeled protein, initiating ion collection in a narrow mass range including the endogenous and labeled peptide. This strategy extends the regular detection limit of an LTQ-Orbitrap by at least an order of magnitude and accurately quantifies down to 150 attomole of protein in a cell lysate without any fractionation prior to LC-MS. We use Absolute SILAC to determine the copy number per cell of growth factor receptor-bound protein 2 (Grb2) in HeLa, HepG2, and C2C12 cells to 5.5 x 10(5), 8.8 x 10(5), and 5.7 x 10(5), respectively, in the exponential growth phase.     

emPAI-assisted strategy enhances screening and assessment of Mycobacterium tuberculosis infection serological markers

Discovering new serological markers of Mycobacterium tuberculosis (MTB) infection and establishing a rapid and efficient detection technology is of great significance for the prevention and control of tuberculosis. In this study, we established an exponentially modified protein abundance index (emPAI) value-assisted strategy to investigate and improve the screening efficiency of serological biomarkers of tuberculosis. First, we used LC-MS/MS to analyse MTB culture filtrate proteins (MTB-CFPs), and 632 MTB proteins were identified. Then, the characteristic values of MTB-CFPs – including emPAI value, molecular weight (Mw), isoelectric point (pI), grand average of hydropathy (GRAVY), transmembrane domain (TMD) and functional groups were calculated. Next, we successfully prepared 10 MTB proteins with emPAI value > 1.0 and recombinantly expressed these proteins in Escherichia coli. At the same time, 3 MTB proteins with emPAI between 0.1 and 0.5 were randomly selected as the control groups, and the immunogenicity of the recombinant MTB proteins was detected using ELISA. The sensitivity and receiver operating characteristic (ROC) curves were calculated for each recombinant MTB protein. The results showed that the areas under the curve (AUC) value of Rv2031c, Rv0577, Rv0831c, Rv0934 and Rv3248c were all higher than those of Rv3875 (AUC, 0.6643). Further analysis of the relationship between emPAI value and antibody sensitivity, AUC value and antibody affinity in mice immunized with recombinant MTB protein showed that emPAI values were positively correlated with them, and R-squared value ranged from 0.64 to 0.79. The only exception was ESAT-6 (encoded by the Rv3875 gene), which AUC value was relatively low owing to its strong immunosuppressive properties. This study provides a rationale for the serological marker screening of emPAI-assisted tuberculosis clinical test. The results also provide new technical support for the screening of candidate serological markers of infectious diseases in the future.     

Sensitive single-molecule protein quantification and protein complex detection in a microarray format

Single-molecule protein analysis provides sensitive protein quantitation with a digital read-out and is promising for studying biological systems and detecting biomarkers clinically. However, current single-molecule platforms rely on the quantification of one protein at a time. Conventional antibody microarrays are scalable to detect many proteins simultaneously, but they rely on less sensitive and less quantitative quantification by the ensemble averaging of fluorescent molecules. Here, we demonstrate a single-molecule protein assay in a microarray format enabled by an ultra-low background surface and single-molecule imaging. The digital read-out provides a highly sensitive, low femtomolar limit of detection and four orders of magnitude of dynamic range through the use of hybrid digital-analog quantification. From crude cell lysate, we measured levels of p53 and MDM2 in parallel, proving the concept of a digital antibody microarray for use in proteomic profiling. We also applied the single-molecule microarray to detect the p53-MDM2 protein complex in cell lysate. Our study is promising for development and application of single-molecule protein methods because it represents a technological bridge between single-plex and highly multiplex studies.     

Absolute quantitation of isoforms of post-translationally modified proteins in transgenic organism

Post-translational modification isoforms of a protein are known to play versatile biological functions in diverse cellular processes. To measure the molar amount of each post-translational modification isoform (P(isf)) of a target protein present in the total protein extract using mass spectrometry, a quantitative proteomic protocol, absolute quantitation of isoforms of post-translationally modified proteins (AQUIP), was developed. A recombinant ERF110 gene overexpression transgenic Arabidopsis plant was used as the model organism for demonstration of the proof of concept. Both Ser-62-independent (14)N-coded synthetic peptide standards and (15)N-coded ERF110 protein standard isolated from the heavy nitrogen-labeled transgenic plants were employed simultaneously to determine the concentration of all isoforms (T(isf)) of ERF110 in the whole plant cell lysate, whereas a pair of Ser-62-dependent synthetic peptide standards were used to quantitate the Ser-62 phosphosite occupancy (R(aqu)). The P(isf) was finally determined by integrating the two empirically measured variables using the following equation: P(isf) = T(isf) · R(aqu). The absolute amount of Ser-62-phosphorylated isoform of ERF110 determined using AQUIP was substantiated with a stable isotope labeling in Arabidopsis-based relative and accurate quantitative proteomic approach. The biological role of the Ser-62-phosphorylated isoform was demonstrated in transgenic plants.     

Compositional protein analysis of high density lipoproteins in hypercholesterolemia by shotgun LC-MS/MS and probabilistic peptide scoring

A protein of a biological sample is usually quantified by immunological techniques based on antibodies. Mass spectrometry offers alternative approaches that are not dependent on antibody affinity and avidity, protein isoforms, quaternary structures, or steric hindrance of antibody-antigen recognition in case of multiprotein complexes. One approach is the use of stable isotope-labeled internal standards; another is the direct exploitation of mass spectrometric signals recorded by LC-MS/MS analysis of protein digests. Here we assessed the peptide match score summation index based on probabilistic peptide scores calculated by the PHENYX protein identification engine for absolute protein quantification in accordance with the protein abundance index as proposed by Mann and co-workers (Rappsilber, J., Ryder, U., Lamond, A. I., and Mann, M. (2002) Large-scale proteomic analysis of the human spliceosome. Genome Res. 12, 1231-1245). Using synthetic protein mixtures, we demonstrated that this approach works well, although proteins can have different response factors. Applied to high density lipoproteins (HDLs), this new approach compared favorably to alternative protein quantitation methods like UV detection of protein peaks separated by capillary electrophoresis or quantitation of protein spots on SDS-PAGE. We compared the protein composition of a well defined HDL density class isolated from plasma of seven hypercholesterolemia subjects having low or high HDL cholesterol with HDL from nine normolipidemia subjects. The quantitative protein patterns distinguished individuals according to the corresponding concentration and distribution of cholesterol from serum lipid measurements of the same samples and revealed that hypercholesterolemia in unrelated individuals is the result of different deficiencies. The presented approach is complementary to HDL lipid analysis; does not rely on complicated sample treatment, e.g. chemical reactions, or antibodies; and can be used for projective clinical studies of larger patient groups.     

Highly reproducible label free quantitative proteomic analysis of RNA polymerase complexes

The use of quantitative proteomics methods to study protein complexes has the potential to provide in-depth information on the abundance of different protein components as well as their modification state in various cellular conditions. To interrogate protein complex quantitation using shotgun proteomic methods, we have focused on the analysis of protein complexes using label-free multidimensional protein identification technology and studied the reproducibility of biological replicates. For these studies, we focused on three highly related and essential multi-protein enzymes, RNA polymerase I, II, and III from Saccharomyces cerevisiae. We found that label-free quantitation using spectral counting is highly reproducible at the protein and peptide level when analyzing RNA polymerase I, II, and III. In addition, we show that peptide sampling does not follow a random sampling model, and we show the need for advanced computational models to predict peptide detection probabilities. In order to address these issues, we used the APEX protocol to model the expected peptide detectability based on whole cell lysate acquired using the same multidimensional protein identification technology analysis used for the protein complexes. Neither method was able to predict the peptide sampling levels that we observed using replicate multidimensional protein identification technology analyses. In addition to the analysis of the RNA polymerase complexes, our analysis provides quantitative information about several RNAP associated proteins including the RNAPII elongation factor complexes DSIF and TFIIF. Our data shows that DSIF and TFIIF are the most highly enriched RNAP accessory factors in Rpb3-TAP purifications and demonstrate our ability to measure low level associated protein abundance across biological replicates. In addition, our quantitative data supports a model in which DSIF and TFIIF interact with RNAPII in a dynamic fashion in agreement with previously published reports.     

Comparison between procedures using SDS for shotgun proteomic analyses of complex samples

Filter-aided sample preparation (FASP) and a new sample preparation method using a modified commercial SDS removal spin column are quantitatively compared in terms of their performance for shotgun proteomic experiments in three complex proteomic samples: a Saccharomyces cerevisiae lysate (insoluble fraction), a Caenorhabditis elegans lysate (soluble fraction), and a human embryonic kidney cell line (HEK293T). The characteristics and total number of peptides and proteins identified are compared between the two procedures. The SDS spin column procedure affords a conservative fourfold improvement in throughput, is more reproducible, less expensive (i.e. requires less materials), and identifies between 30 and 107% more peptides at q≤0.01, than the FASP procedure. The peptides identified by SDS spin column are more hydrophobic than species identified by the FASP procedure as indicated by the distribution of GRAVY scores. Ultimately, these improvements correlate to as great as a 50% increase in protein identifications with two or more peptides.