Manfred Claassen

The quantitative proteome of a human cell line

The generation of mathematical models of biological processes, the simulation of these processes under different conditions, and the comparison and integration of multiple data sets are explicit goals of systems biology that require the knowledge of the absolute quantity of the systems components. To date, systematic estimates of cellular protein concentrations have been exceptionally scarce. Here, we provide a quantitative description of the proteome of a commonly used human cell line in two functional states, interphase and mitosis. We show that these human cultured cells express at least ∼10 000 proteins and that the quantified proteins span a concentration range of seven orders of magnitude up to 20 000 000 copies per cell. We discuss how protein abundance is linked to function and evolution.

Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry

Comprehensive characterization of a proteome is a fundamental goal in proteomics. To achieve saturation coverage of a proteome or specific subproteome via tandem mass spectrometric identification of tryptic protein sample digests, proteomics data sets are growing dramatically in size and heterogeneity. The trend toward very large integrated data sets poses so far unsolved challenges to control the uncertainty of protein identifications going beyond well established confidence measures for peptide-spectrum matches. We present MAYU, a novel strategy that reliably estimates false discovery rates for protein identifications in large scale data sets. We validated and applied MAYU using various large proteomics data sets. The data show that the size of the data set has an important and previously underestimated impact on the reliability of protein identifications. We particularly found that protein false discovery rates are significantly elevated compared with those of peptide-spectrum matches. The function provided by MAYU is critical to control the quality of proteome data repositories and thereby to enhance any study relying on these data sources. The MAYU software is available as standalone software and also integrated into the Trans-Proteomic Pipeline.

false discovery rate estimation for cross- linked peptides identified by mass spectrometry

The mass spectrometric identification of chemically cross-linked peptides (CXMS) specifies spatial restraints of protein complexes; these values complement data obtained from common structure-determination techniques. Generic methods for determining false discovery rates of cross-linked peptide assignments are currently lacking, thus making data sets from CXMS studies inherently incomparable. Here we describe an automated target-decoy strategy and the software tool xProphet, which solve this problem for large multicomponent protein complexes.

The Mtb Proteome Library: A Resource of Assays to Quantify the Complete Proteome of Mycobacterium tuberculosis

Research advancing our understanding of Mycobacterium tuberculosis (Mtb) biology and complex host-Mtb interactions requires consistent and precise quantitative measurements of Mtb proteins. We describe the generation and validation of a compendium of assays to quantify 97% of the 4,012 annotated Mtb proteins by the targeted mass spectrometric method selected reaction monitoring (SRM). Furthermore, we estimate the absolute abundance for 55% of all Mtb proteins, revealing a dynamic range within the Mtb proteome of over four orders of magnitude, and identify previously unannotated proteins. As an example of the assay library utility, we monitored the entire Mtb dormancy survival regulon (DosR), which is linked to anaerobic survival and Mtb persistence, and show its dynamic protein-level regulation during hypoxia. In conclusion, we present a publicly available research resource that supports the sensitive, precise, and reproducible quantification of virtually any Mtb protein by a robust and widely accessible mass spectrometric method.

Absolute quantification of microbial proteomes at different states by directed mass spectrometry.

Over the past decade, liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) has evolved into the main proteome discovery technology. Up to several thousand proteins can now be reliably identified from a sample and the relative abundance of the identified proteins can be determined across samples. However, the remeasurement of substantially similar proteomes, for example those generated by perturbation experiments in systems biology, at high reproducibility and throughput remains challenging. Here, we apply a directed MS strategy to detect and quantify sets of pre‐determined peptides in tryptic digests of cells of the human pathogen Leptospira interrogans at 25 different states. We show that in a single LC–MS/MS experiment around 5000 peptides, covering 1680 L. interrogans proteins, can be consistently detected and their absolute expression levels estimated, revealing new insights about the proteome changes involved in pathogenic progression and antibiotic defense of L. interrogans. This is the first study that describes the absolute quantitative behavior of any proteome over multiple states, and represents the most comprehensive proteome abundance pattern comparison for any organism to date.

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 R2 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.

Directed mass spectrometry: towards hypothesis-driven proteomics.

To date, the vast majority of the proteomic data sets collected by mass spectrometry (MS) have been generated by nondirected methods, whereby the identified precursor ions are stochastically selected for sequencing from complex sample mixtures. Recently, new MS approaches have been developed in which the mass spectrometer is directed to select and fragment sets of precursor ions that represent the most informative peptides in a sample mixture. These directed MS methods have shown superior performance for the fast, sensitive, and highly reproducible generation of consistent data sets at low redundancy. In this manuscript we summarize recent technical advances in directed MS and discuss important applications to quantitative proteomics.

Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability

How proteomes take the heat Living organisms are very sensitive to temperature, and much of this is attributed to its effect on the structure and function of proteins. Leuenberger et al. explored thermostability on a proteome-wide scale in bacteria, yeast, and human cells by using a combination of limited proteolysis and mass spectrometry (see the Perspective by Vogel). Their results suggest that temperature-induced cell death is caused by the loss of a subset of proteins with key functions. The study also provides insight into the molecular and evolutionary bases of protein and proteome stability. Science, this issue p. eaai7825; see also p. 794 Proteomic analysis provides insight into the molecular and evolutionary bases of proteins and proteome thermal stability. INTRODUCTION Temperature is crucially important to life. Small temperature changes can differentiate optimal and lethal growth conditions of living organisms. Because of the higher abundance and lower stability of proteins as compared with those of other biological macromolecules, thermally induced cell death is thought to be due to protein denaturation, but the determinants of thermal sensitivity of proteomes remain largely uncharacterized. RATIONALE To determine the thermal stability of proteins on a proteome-wide scale and with domain-level resolution, we developed a structural proteomic approach that relies on limited proteolysis (LiP) and mass spectrometry (MS) applied over a range of temperatures. RESULTS Our LiP-MS strategy was validated through analysis of purified proteins in the presence and absence of a biologically relevant matrix. We then obtained proteome-wide thermal denaturation profiles for Escherichia coli, Saccharomyces cerevisiae, Thermus thermophilus, and human cells. In contrast to previous predications that proteome instability derives from the simultaneous and generalized loss of hundreds of proteins, we observed that at a temperature at which cells experience temperature-induced physiological impairment, a subset of essential proteins undergoes denaturation. Confirming results of previous studies on the basis of comparison of genomes of thermophilic and mesophilic bacteria, we observed enrichment for lysine residues and β-sheet structures in thermostable proteins. We also found that unstable proteins have a higher content of aspartic acid than that of stable proteins and observed an inverse correlation between protein length and thermal stability. Further, thermostable proteins are substantially less prone to thermal aggregation than unstable proteins. Relative domain thermostability was conserved both within species and across organisms. Thermal stability was not generally similar for proteins encoded by orthologous genes. This suggests that the melting temperatures of proteins are affected by the reshuffling of protein domains, despite the conservation of domain stability. According to the “translational robustness” theory, highly expressed proteins must tolerate translational errors that can lead to the accumulation of toxic misfolded species. Our data show a clear direct relationship between protein thermal stability and intracellular abundance and an inverse relationship between protein stability and aggregation or local unfolding. Increasing the thermodynamic stabilities of the folds of abundant proteins will broaden the range of amino acid replacements that a protein can tolerate before misfolding. Our findings suggest that over the course of evolution, the burden of intracellular misfolding has been reduced by increasing the thermodynamic stability of abundant proteins. Last, although up to 30% of proteomes have been predicted to consist of intrinsically disordered proteins (IDPs), our data revealed that about half of these proteins showed two-state denaturation profiles in the cellular matrix. This suggests that many IDPs are globally or locally structured in cells. CONCLUSION Our study contributes insight into the molecular and evolutionary bases of protein and proteome thermostability and provides a blueprint for future studies on the stability of proteomes and thermal denaturation. Protein-protein interaction network of E. coli. Node color indicates protein thermostability. Blue, unstable; yellow, medium-stable; orange, stable; gray, not measured. At the temperature of thermal cell death of E. coli, a subset of highly connected protein nodes involved in key cellular processes undergoes temperature-induced denaturation. Temperature-induced cell death is thought to be due to protein denaturation, but the determinants of thermal sensitivity of proteomes remain largely uncharacterized. We developed a structural proteomic strategy to measure protein thermostability on a proteome-wide scale and with domain-level resolution. We applied it to Escherichia coli, Saccharomyces cerevisiae, Thermus thermophilus, and human cells, yielding thermostability data for more than 8000 proteins. Our results (i) indicate that temperature-induced cellular collapse is due to the loss of a subset of proteins with key functions, (ii) shed light on the evolutionary conservation of protein and domain stability, and (iii) suggest that natively disordered proteins in a cell are less prevalent than predicted and (iv) that highly expressed proteins are stable because they are designed to tolerate translational errors that would lead to the accumulation of toxic misfolded species.

Inference and validation of protein identifications

Discovery or shotgun proteomics has emerged as the most powerful technique to comprehensively map out a proteome. Reconstruction of protein identities from the raw mass spectrometric data constitutes a cornerstone of any shotgun proteomics workflow. The inherent uncertainty of mass spectrometric data and the complexity of a proteome render protein inference and the statistical validation of protein identifications a non-trivial task, still being a subject of ongoing research. This review aims to survey the different conceptual approaches to the different tasks of inferring and statistically validating protein identifications and to discuss their implications on the scope of proteome exploration.

Generic Comparison of Protein Inference Engines

Protein identifications, instead of peptide-spectrum matches, constitute the biologically relevant result of shotgun proteomics studies. How to appropriately infer and report protein identifications has triggered a still ongoing debate. This debate has so far suffered from the lack of appropriate performance measures that allow us to objectively assess protein inference approaches. This study describes an intuitive, generic and yet formal performance measure and demonstrates how it enables experimentalists to select an optimal protein inference strategy for a given collection of fragment ion spectra. We applied the performance measure to systematically explore the benefit of excluding possibly unreliable protein identifications, such as single-hit wonders. Therefore, we defined a family of protein inference engines by extending a simple inference engine by thousands of pruning variants, each excluding a different specified set of possibly unreliable identifications. We benchmarked these protein inference engines on several data sets representing different proteomes and mass spectrometry platforms. Optimally performing inference engines retained all high confidence spectral evidence, without posterior exclusion of any type of protein identifications. Despite the diversity of studied data sets consistently supporting this rule, other data sets might behave differently. In order to ensure maximal reliable proteome coverage for data sets arising in other studies we advocate abstaining from rigid protein inference rules, such as exclusion of single-hit wonders, and instead consider several protein inference approaches and assess these with respect to the presented performance measure in the specific application context.