Craig Lawless

Global absolute quantification of a proteome: Challenges in the deployment of a QconCAT strategy

In this paper, we discuss the challenge of large‐scale quantification of a proteome, referring to our programme that aims to define the absolute quantity, in copies per cell, of at least 4000 proteins in the yeast Saccharomyces cerevisiae. We have based our strategy on the well‐established method of stable isotope dilution, generating isotopically labelled peptides using QconCAT technology, in which artificial genes, encoding concatenations of tryptic fragments as surrogate quantification standards, are designed, synthesised de novo and expressed in bacteria using stable isotopically enriched media. A known quantity of QconCAT is then co‐digested with analyte proteins and the heavy:light isotopologues are analysed by mass spectrometry to yield absolute quantification. This workflow brings issues of optimal selection of quantotypic peptides, their assembly into QconCATs, expression, purification and deployment.

CONSeQuence: Prediction of Reference Peptides for Absolute Quantitative Proteomics Using Consensus Machine Learning Approaches

Mass spectrometric based methods for absolute quantification of proteins, such as QconCAT, rely on internal standards of stable-isotope labeled reference peptides, or “Q-peptides,” to act as surrogates. Key to the success of this and related methods for absolute protein quantification (such as AQUA) is selection of the Q-peptide. Here we describe a novel method, CONSeQuence (consensus predictor for Q-peptide sequence), based on four different machine learning approaches for Q-peptide selection. CONSeQuence demonstrates improved performance over existing methods for optimal Q-peptide selection in the absence of prior experimental information, as validated using two independent test sets derived from yeast. Furthermore, we examine the physicochemical parameters associated with good peptide surrogates, and demonstrate that in addition to charge and hydrophobicity, peptide secondary structure plays a significant role in determining peptide “detectability” in liquid chromatography-electrospray ionization experiments. We relate peptide properties to protein tertiary structure, demonstrating a counterintuitive preference for buried status for frequently detected peptides. Finally, we demonstrate the improved efficacy of the general approach by applying a predictor trained on yeast data to sets of proteotypic peptides from two additional species taken from an existing peptide identification repository.

Prediction of Missed Proteolytic Cleavages for the Selection of Surrogate Peptides for Quantitative Proteomics

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 .

A critical appraisal of techniques, software packages, and standards for quantitative proteomic analysis.

New methods for performing quantitative proteome analyses based on differential labeling protocols or label-free techniques are reported in the literature on an almost monthly basis. In parallel, a correspondingly vast number of software tools for the analysis of quantitative proteomics data has also been described in the literature and produced by private companies. In this article we focus on the review of some of the most popular techniques in the field and present a critical appraisal of several software packages available to process and analyze the data produced. We also describe the importance of community standards to support the wide range of software, which may assist researchers in the analysis of data using different platforms and protocols. It is intended that this review will serve bench scientists both as a useful reference and a guide to the selection and use of different pipelines to perform quantitative proteomics data analysis. We have produced a web-based tool ( http://www.proteosuite.org/?q=other_resources ) to help researchers find appropriate software for their local instrumentation, available file formats, and quantitative methodology.

Direct and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction Monitoring

Defining intracellular protein concentration is critical in molecular systems biology. Although strategies for determining relative protein changes are available, defining robust absolute values in copies per cell has proven significantly more challenging. Here we present a reference data set quantifying over 1800 Saccharomyces cerevisiae proteins by direct means using protein-specific stable-isotope labeled internal standards and selected reaction monitoring (SRM) mass spectrometry, far exceeding any previous study. This was achieved by careful design of over 100 QconCAT recombinant proteins as standards, defining 1167 proteins in terms of copies per cell and upper limits on a further 668, with robust CVs routinely less than 20%. The selected reaction monitoring-derived proteome is compared with existing quantitative data sets, highlighting the disparities between methodologies. Coupled with a quantification of the transcriptome by RNA-seq taken from the same cells, these data support revised estimates of several fundamental molecular parameters: a total protein count of ∼100 million molecules-per-cell, a median of ∼1000 proteins-per-transcript, and a linear model of protein translation explaining 70% of the variance in translation rate. This work contributes a “gold-standard” reference yeast proteome (including 532 values based on high quality, dual peptide quantification) that can be widely used in systems models and for other comparative studies.

Investigating protein isoforms via proteomics: A feasibility study

Alternative splicing (AS) and processing of pre‐messenger RNAs explains the discrepancy between the number of genes and proteome complexity in multicellular eukaryotic organisms. However, relatively few alternative protein isoforms have been experimentally identified, particularly at the protein level. In this study, we assess the ability of proteomics to inform on differently spliced protein isoforms in human and four other model eukaryotes. The number of Ensembl‐annotated genes for which proteomic data exists that informs on AS exceeds 33% of the alternately spliced genes in the human and worm genomes. Examining AS in chicken via proteomics for the first time, we find support for over 600 AS genes. However, although peptide identifications support only a small fraction of alternative protein isoforms that are annotated in Ensembl, many more variants are amenable to proteomic identification. There remains a sizeable gap between these existing identifications (10–52% of AS genes) and those that are theoretically feasible (90–99%). We also compare annotations between Swiss‐Prot and Ensembl, recommending use of both to maximize coverage of AS. We propose that targeted proteomic experiments using selected reactions and standards are essential to uncover further alternative isoforms and discuss the issues surrounding these strategies.

Quantitative analysis of chaperone network throughput in budding yeast.

The network of molecular chaperones mediates the folding and translocation of the many proteins encoded in the genome of eukaryotic organisms, as well as a response to stress. It has been particularly well characterised in the budding yeast, Saccharomyces cerevisiae, where 63 known chaperones have been annotated and recent affinity purification and MS/MS experiments have helped characterise the attendant network of chaperone targets to a high degree. In this study, we apply our QconCAT methodology to directly quantify the set of yeast chaperones in absolute terms (copies per cell) via SRM MS. Firstly, we compare these to existing quantitative estimates of these yeast proteins, highlighting differences between approaches. Secondly, we cast the results into the context of the chaperone target network and show a distinct relationship between abundance of individual chaperones and their targets. This allows us to characterise the ‘throughput’ of protein molecules passing through individual chaperones and their groups on a proteome‐wide scale in an unstressed model eukaryote for the first time. The results demonstrate specialisations of the chaperone classes, which display different overall workloads, efficiencies and preference for the sub‐cellular localisation of their targets. The novel integration of the interactome data with quantification supports re‐estimates of the level of protein throughout going through molecular chaperones. Additionally, although chaperones target fewer than 40% of annotated proteins we show that they mediate the folding of the majority of protein molecules (∼62% of the total protein flux in the cell), highlighting their importance.

Collagen Sequence Analysis of the Extinct Giant Ground Sloths Lestodon and Megatherium

For over 200 years, fossils of bizarre extinct creatures have been described from the Americas that have ranged from giant ground sloths to the ‘native’ South American ungulates, groups of mammals that evolved in relative isolation on South America. Ground sloths belong to the South American xenarthrans, a group with modern although morphologically and ecologically very different representatives (anteaters, armadillos and sloths), which has been proposed to be one of the four main eutherian clades. Recently, proteomics analyses of bone collagen have recently been used to yield a molecular phylogeny for a range of mammals including the unusual ‘Malagasy aardvark’ shown to be most closely related to the afrotherian tenrecs, and the south American ungulates supporting their morphological association with condylarths. However, proteomics results generate partial sequence information that could impact upon the phylogenetic placement that has not been appropriately tested. For comparison, this paper examines the phylogenetic potential of proteomics-based sequencing through the analysis of collagen extracted from two extinct giant ground sloths, Lestodon and Megatherium. The ground sloths were placed as sister taxa to extant sloths, but with a closer relationship between Lestodon and the extant sloths than the basal Megatherium. These results highlight that proteomics methods could yield plausible phylogenies that share similarities with other methods, but have the potential to be more useful in fossils beyond the limits of ancient DNA survival.

The HUPO initiative on Model Organism Proteomes, iMOP

The community working on model organisms is growing steadily and the number of model organisms for which proteome data are being generated is continuously increasing. To standardize efforts and to make optimal use of proteomics data acquired from model organisms, a new Human Proteome Organisation (HUPO) initiative on model organism proteomes (iMOP) was approved at the HUPO Ninth Annual World Congress in Sydney, 2010. iMOP will seek to stimulate scientific exchange and disseminate HUPO best practices. The needs of model organism researchers for central databases will be better represented, catalyzing the integration of proteomics and organism‐specific databases. Full details of iMOP activities, members, tools and resources can be found at our website http://www.imop.uzh.ch/ and new members are invited to join us.

Absolute protein quantification of the yeast chaperome under conditions of heat shock

Chaperones are fundamental to regulating the heat shock response, mediating protein recovery from thermal‐induced misfolding and aggregation. Using the QconCAT strategy and selected reaction monitoring (SRM) for absolute protein quantification, we have determined copy per cell values for 49 key chaperones in Saccharomyces cerevisiae under conditions of normal growth and heat shock. This work extends a previous chemostat quantification study by including up to five Q‐peptides per protein to improve confidence in protein quantification. In contrast to the global proteome profile of S. cerevisiae in response to heat shock, which remains largely unchanged as determined by label‐free quantification, many of the chaperones are upregulated with an average two‐fold increase in protein abundance. Interestingly, eight of the significantly upregulated chaperones are direct gene targets of heat shock transcription factor‐1. By performing absolute quantification of chaperones under heat stress for the first time, we were able to evaluate the individual protein‐level response. Furthermore, this SRM data was used to calibrate label‐free quantification values for the proteome in absolute terms, thus improving relative quantification between the two conditions. This study significantly enhances the largely transcriptomic data available in the field and illustrates a more nuanced response at the protein level.