Mass-spectrometric exploration of proteome structure and function

Journal name:
Nature
Volume:
537,
Pages:
347–355
Date published:
DOI:
doi:10.1038/nature19949
Received
Accepted
Published online

Abstract

Numerous biological processes are concurrently and coordinately active in every living cell. Each of them encompasses synthetic, catalytic and regulatory functions that are, almost always, carried out by proteins organized further into higher-order structures and networks. For decades, the structures and functions of selected proteins have been studied using biochemical and biophysical methods. However, the properties and behaviour of the proteome as an integrated system have largely remained elusive. Powerful mass-spectrometry-based technologies now provide unprecedented insights into the composition, structure, function and control of the proteome, shedding light on complex biological processes and phenotypes.

At a glance

Figures

  1. Bottom-up proteomics workflows.
    Figure 1: Bottom-up proteomics workflows.

    a, All bottom-up proteomics workflows begin with a sample-preparation stage in which proteins are extracted and digested by a sequence-specific enzyme such as trypsin. Present methods of protein preparation are highly efficient and can be performed in 96-well plates with robotic assistance. Peptides are then separated by means of chromatography and electrosprayed, after which they are introduced into the vacuum of a mass spectrometer. Three classes of methods are shown. In DDA methods, a full spectrum of the peptides (at the MS1 level) is acquired, followed by the collection of as many fragmentation spectra (at the MS2 level) as possible, within a cycle time of about 1 second. A quadrupole–orbitrap mass analyser is depicted, although other types of analyser are also used in DDA. Results are interpreted using software packages such as MaxQuant100 and the downstream Perseus environment101. In targeted analysis, a peptide of known mass-to-charge ratio (m/z) is selected in the first quadrupole, then the peptide is fragmented and several fragments are monitored over time. These transitions are multiplexed and their specificity is checked using software packages such as SkyLine102. In DIA methods, which are exemplified by sequential window acquisition of all theoretical fragment-ion spectra (SWATH)–MS103, ranges of m/z values (that typically span 25 m/z units) are selected and peptides are fragmented, followed by the acquisition of the fragments in a time-of-flight mass spectrometer. The instrument rapidly and seamlessly cycles through the entire mass range within a few seconds. The multiplexed fragment spectra are interpreted — often with the help of known fragment spectra from large spectral libraries — by software such as OpenSWATH104. b, Peptide quantities can be determined at the MS1 level by integrating the signal from peaks of the precursor ions that elute from the high-performance liquid chromatography column. An arbitrary number of runs (stacked mass spectra, left) can be compared using sophisticated alignment and normalization procedures. Quantitative comparison of the isotopic cluster of the same peptide over two runs can be performed. Peptide identities can also be transferred when the peptide is fragmented in only one of the runs but matches precisely the mass and elution time of an aligned peak (known as the 'match between runs' feature in MaxQuant100). Absolute quantities can be estimated by adding up the peak volumes of all peptides that identify a particular protein then determining the proportion of the (known) total proteome mass that has been analysed. Peptides can also be subjected to label-free quantification at the MS2 level (right). In this case, the fragment-ion intensities that are unique to a specific peptide are used for quantification, in a way that is analogous to the use of precursor-ion signal intensities for quantification using MS1-level data. In multiplexed shotgun proteomics, up to ten samples are labelled differentially so that they release reporter ions that can be distinguished in the MS2 spectra. In DIA-based methods, the intensities of fragments that belong to the same precursor ion are extracted to yield a measure of peptide abundance104, 105. Q, quadrupole.

  2. Analysis of post-translational modifications.
    Figure 2: Analysis of post-translational modifications.

    a, In post-translational modification, proteins are modified through the attachment of a chemical moiety such as a phosphate group, usually by a dedicated and highly specific system of enzymes. The most commonly studied post-translational modifications are listed (centre) and these are accompanied by hundreds of other less-well-studied or unknown types of modifications. Such modifications can lead to: alterations in protein conformation (through phosphorylation) and subsequent allosteric regulation; changes in enzyme activity; crosstalk that results from the same amino-acid residue being targeted by more than one type of modification; alterations in the subcellular localization of proteins; changes in protein binding; and alterations in protein lifetimes (for example, through the attachment of the small protein ubiquitin). Ac, acetyl; ERK, extracellular signal-related kinase; Me, methyl; MEK, mitogen-activated protein kinase kinase; MYC, transcription factor cMYC; P, phosphate; RAF, RAF kinase; RAS, RAS GTPase; Ub, ubiquitin. b, After a modified peptide has been identified from the fragment spectra, the amino acid in the peptide chain to which the post-translational modification is attached must be determined. The location of the modification within the three-dimensional structure of the protein can often also be determined, which provides clues about function. c, Global interrogation of the changes in a signalling pathway can be achieved readily by quantitative phosphoproteomics. For example, the suppression of aberrant signalling in cancer cells by drugs known as kinase inhibitors can be followed. d, Detailed time-course experiments yield information on the temporal ordering of events such as the activation of a kinase upstream of one of its substrates. The proportion of proteins that are modified by a particular post-translational modification (also termed the occupancy or stoichiometry) can change drastically depending on the biological conditions (not shown). It can be derived from the changes in protein level and the levels of the modified and unmodified peptide in two cellular states106. e, The modification of a protein often determines its subcellular localization — that is, whether it is found in the nucleus or the cytosol, for instance. Many types of stimuli can be applied to biological systems, after which the level of a particular post-translational modification can be determined. f, The structure of the perturbation matrix that results reveals the regulated sites and how they correlate between stimuli, as indicated by hot spots in the heat map. m, number of modification sites quantified; n, number of stimuli applied.

  3. Interaction proteomics and structural proteomics.
    Figure 3: Interaction proteomics and structural proteomics.

    a, Schematic representations of a protein interaction network with bait proteins (teal), core complex members (dark green) and weak interactors (light green). A bait protein is precipitated with its interaction partners and is measured in replicates by one of the workflows described in Fig. 1. By considering the interaction stoichiometry (the molar ratio of prey proteins and the bait protein expressed under endogenous control) and the relative cellular abundances of the proteins, stable core complexes can be distinguished from weak interactions and unspecific interactions, as well as from asymmetric interactions between proteins of different abundances55. b, A wild-type protein complex and the same complex with mutations (*) are investigated using complementary structural techniques, collectively termed integrative or hybrid structural analysis. For example, XL–MS can reveal information about subunit topology and direct domain–domain interactions. Hydrogen–deuterium exchange mass spectrometry (HDX–MS) is able to determine the interaction surfaces and solvent-exposed regions. Native mass spectrometry (native MS), in which entire protein complexes are electrosprayed into the mass spectrometer, can infer the stoichiometry and the assembly pathway of such complexes, and cryo-EM can obtain their overall shape and their density maps. The heterogeneous structural restraints are integrated in a common computational framework that evaluates subunit configurations (known as conformational sampling). Consensus models that represent the structures of the wild-type and mutated complexes can then be derived.

  4. Proteotype states and phenotypes.
    Figure 4: Proteotype states and phenotypes.

    The proteotype, which is the acute state of the proteome, is shown as a modular network of interacting protein entities (coloured shapes). The composition of the proteotype and the organization of individual proteins into functional modules and interaction networks are determined by the combined effects of genotype and external perturbations, which include physical or chemical stimuli, cell–cell interactions or the microbiota. Genotypic differences such as allele differences or somatic mutations might perturb the proteotype. The relationship between genetic loci and the abundance of a protein can be described by a pQTL. These are identified by associating the abundance of a specific protein with particular alleles in genetically characterized sample populations such as genetic reference panels. In turn, the proteotype determines phenotypes, including clinical phenotypes. Association studies can identify relationships between proteotypes and phenotypes. Establishing such associations requires the generation of quantitatively accurate and highly reproducible datasets in which the same proteins are quantified across a large number of samples (for example, genetic reference panels or cohorts of patients). Datasets that support such association studies can now be generated using various mass spectrometry techniques.

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  1. Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, 8093 Zürich, Switzerland.

    • Ruedi Aebersold
  2. Faculty of Science, University of Zürich, 8093 Zürich, Switzerland.

    • Ruedi Aebersold
  3. Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany.

    • Matthias Mann
  4. Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.

    • Matthias Mann

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