B2. Sequence Determination Using Mass Spectrometry - Biology

B2. Sequence Determination Using Mass Spectrometry - Biology

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Mass spectrometry is supplanting more tradition methods (see above) as the choice to determine the molecular mass and structure of a protein. Its power comes from its exquisite sensitivity and modern computational methods to determine structure through comparisons of ion fragment data with computer databases of known protein structures. In mass spectrometry, a molecule is first ionized in an ion source. The charged particles are then accelerated by an electric field into a mass analyzer where they are subjected to an external magnetic field. The external magnetic field interacts with the magnetic field arising from the movement of the charged particles, causing them to deflect. The deflection is proportional to the mass to charge ratio, m/z. Ions then enter the detector which is usually a photomultiplier. Sample introduction into the ion source occurs though simple diffusion of gases and volatile liquids from a reservoir, by injection of a liquid sample containing the analyte by spraying a fine mist, or for very large proteins by desorbing a protein from a matrix using a laser. Analysis of complex mixtures is done by coupling HPLC with mass spectrometry in a LCMS.

Ion source: There are many methods to ionize molecules, including atmospheric pressure chemical ionization (APCI), chemical ionization (CI), or electron impact (EI). The most common methods for protein/peptide analyzes are electrospray ionization (ESI) and matrix assisted laser desorption ionization (MALDI).

Electrospray ionization (ESI) - The analyte, dissolved in a volatile solvent like methanol or acetonitrile, is injected through a fine stainless steel capillary at a slow flow rate into the ion source. A high voltage (3-4 kV) is maintained on the capillary giving it a positive charge with respect to the other oppositely charged electrode. The flowing liquid becomes charged with same polarity as the polarity of the positively charged capillary. The high field leads to the emergence of the sample as a charged aerosol spray of charged microdrops which reduces electrostatic repulsions in the liquid. This method essentially uses electrical energy to produce the aerosol instead of mechanical energy to produce a liquid aerosol, as in the case of a perfume atomizer. Surrounding the capillary is a flowing gas (nitrogen) which helps to move the aerosol towards the mass analyzer. The microdrops become smaller in size as the volatile solvent evaporates, increasing the positive charge density on the drops. Eventually electrostatic repulsions cause the drops to explode in a series of steps, ultimately producing analyte devoid of solvent. This gentle method of ionization produces analytes that are not cleaved but ready for introduction into the mass analyzer. Protein emerge from this process with a roughly Gaussian distribution of positive charges on basic side chains. In organic chemistry you studied mass spectrums of small molecules induced by electron bombardment. This produces ions of +1 charge as an electron is stripped away from the neutral molecule. The highest m/z peak in the spectrum is the parent ion or M+ ion. The highest m/z ratio detectable in the mass spectrum is in the thousands. However, large peptides and proteins with large molecular masses can be detected and resolved since the charge on the ions are great than +1. In 2002, John Fenn was awarded a Noble Prize in Chemistry for the development and use of ESI to study biological molecules.

An example of an ESI spectrum of apo-myoglobin is shown below. Note the roughly Gaussian distribution of the peaks, each of which represents the intact protein with charges differing by +1. Protein have positive charges by virtue of both protonation of amino acid side chains as well as charges induced during the electrostray process itself. Based on the amino acid sequence of myoglobin and the assumption that the pKa of the side chains are the same in the protein as for isolated amino acids, the calculated average net charges of apoMb would be approximately +30 at pH 3.5, +20 at pH 4.5, +9 at pH 6, and 0 at pH 7.8 (the calculated pI). The mass spectrum below was taken by direct injection into the MS of apoMb in 0.1% formic acid (pH 2.8). Charges on the peptide are a combined results of charges present on the peptide before the electrospray and changes in charges induced during the process. .

Figure: ESI Mass Spectrum of Apo-Myoglobin

The molecular mass of the protein can be determined by analyzing two adjacent peaks, as shown in the figure below.

If M is the molecular mass of the analyte protein, and n is the number of positive charges on the protein represented in a given m/z peak, then the following equations gives the molecular mass M of the protein for each peak:

[Mpeak2 = n(m/z)peak 2 - n(1.008)]

[Mpeak1 = (n + 1) (m/z)peak 1 - (n +1) (1.008)]

where 1.008 is the atomic weight of H. Since there is only one value of M, the two equations can be set equal to each other, giving:

[n(m/z)peak 2 - n(1.008) = (n + 1) (m/z)peak 1 - (n +1) (1.008)]

Solving for n gives:

[n = [(m/z)peak 1- 1.008]/[(m/z)peak 2 -(m/z)peak 1]. ]

Knowing n, the molecular mass M the protein can be calculated for each m/z peak. The best value of M can then be determined by averaging the M values determined from each peak (16,956 from the above figure). For peaks from m/z of 893-1542, the calculated values of n ranged from +18 to +10.

Matrix assisted laser desorption ionization (MALDI): In this technique, used for larger biomolecules like proteins and polysaccharides, the analyte is mixed with an absorbing matrix material. Laser excitation is used to excite the matrix, leading to energy transfer that results in ionization and "launching" of the matrix and analyte in ion form from the solid mixture. Parent ion peaks of (M+H)+ and (M-H)- are formed.

Mass Analyzer

Quadrupole ion trap (used in ESI) - A complex mixture of ions can be contained (or trapped) in this type of mass analyzer. Two common type are linear and 3D quadrupoles.

Figure: Linear and 3D Quadrupoles

As dipoles display positive and negative charge separation on a linear axis, quadrupoles have either opposite electrical charges or opposite magnetic fields at the opposing ends of a square or cube. In charge separation, the monopole (sum of the charges) and dipoles cancel to zero, but the quadrupole moment does not. The quadrupole traps ions using a combination of fixed and alternating electric fields. The trap contains He at 1 mTorr. For the 3D trap, The ring electrode has a oscillating RF voltage which keeps the ions trapped. The end caps also have an AC voltage. Ions oscillate in the trap with a "secular" frequency determined by the frequency of the RF voltage, and of course, the m/z ratio. By increasing the the amplitude of the RF field across the ring electron, ion motion in the trap becomes destabilized and leads to ion ejection into the detector. When the secular frequency of ion motion matches the applied AC voltage to the endcap electrodes, resonance occurs and the amplitude of motion of the ions increases, also allowing leakage out of the ion trap into the detector.

Time of Flight (TOF) tube (used in MALDI) - a long tubes is used and the time required for ion detection is determined. The small molecular mass ions take the shortest time to reach the detector.

Tandem Mass Spectrometry (MS/MS)

Quadrupole mass analyzers which can select ions of varying m/z ratios in the ion traps are commonly used in for tandem mass spectrometry (MS/MS). In this technique, the selected ions are further fragmented into smaller ions by a process called collision induced dissociation (CID). When performed on all of the initial ions present in the ion trap, the sequence of a peptide/protein can be determined. This techniques usually requires two mass analyzers with a collision cell in-between where selected ions are fragmented by collision with an inert gas. It can also be done in a single mass analyzer using a quadrupole ion trap.

In a typical MS/MS experiment to determine a protein sequence, a protein is cleaved into protein fragments with an enzyme such as trypsin, which cleaves on carboxyl side of positively charge Lys and Arg side chains. The average size of proteins in the human proteome is approximately 50,000. If the average molecular mass of an amino acid in a protein is around 110 (18 subtracted since water is released on amide bond formation), the average number of amino acids in the protein would be around 454. If 10% of the amino acids are Arg and Lys, the on average there would be approximately 50 Lys and Arg, and hence 50 tryptic peptides of average molecular mass of 1000. The fragments are introduced in the MS where a peptide fragment fingerprint analysis can be performed. The MWs of the fragments can be identified and compared to known peptide digestion fragments from known proteins to identify the analyte protein.

To get sequence information, a tryptic peptide with a specific m/z ratio (optimally with a single +1 charge) is further selected in the ion trap and fragmented on collision with an inert agent (MS/MS). Since the m/e range of mass spectrometers is in the thousands, tryptic fragments with a single charge can easily be detected and targeted for MS/MS. The likely and observed cleavages for a tetrapeptide and the resulting ions with a +1 charge are illustrated below. Ions with the original N terminus are denoted as a, b, and c, while ions with the original C terminus are denoted as x, y, and z. c and y ions gain an extra proton from the peptide to form positively charged -NH3+ groups. The actual ions observed depend on many factors including the sequence of the peptide, its original charge, the energy of the collision inducing the fragmentation, etc. Low energy fragmentation of peptides in ion traps usually produce a, b, and y ions, along with peaks resulting from loss of NH3 (a*, b* and y*) or H2O (ao, bo and yo). No peaks resulting from fragmentation of side chains are observed. Fragmentation at two sites in the peptide (usually at b and y sites in the backbone) form an internal fragment.

Figure: Peptide Fragmentation and Sequencing by MS/MS

The y1 peak represents the C-terminal Lys or Arg (in this example) of the tryptic peptide. Peak y2 has one addition amino acid compared to y1 and the molecular mass difference identifies the extra amino acid. Peak y3 is likewise one amino acid larger than y2. All three y fragments peaks have a common Lys/Arg C-terminal and the charged fragment contains the C-terminal end of the original peptide. All b fragment peaks for a given peptide contain a common N terminal amino acid with b1 the smallest. Note that the subscript represents the number of amino acids in the fragment. By identifying b and y peaks the actual sequence of small peptide can be determined. Usually spectra are match to databases to identify the structure of each peptide and ultimately that of the protein. The actual m values for fragments can be calculated as follows, where (N is the molecular mass of the neutral N terminal group, (C) is the molecular mass of the neutral c terminal group, and (M) is the molecule mass of the neutral amino acids.

  • a: (N)+(M)-CHO
  • b: (N)+(M)
  • y: (C)+(M)+H (note in the figure above that the amino terminus of the y peptides has an extra proton in the +1 charged peptides.)

m/z values can be calculated from the calculated m values and by adding the one H mass to the overall z if the overall charge is +1, etc.

Table: Masses of amino acid residues in a protein. (For N terminal amino acid, add 1 H. for C terminus add OH)



Monoisotopic Mass

Average Mass












































































As an example, sing these MW values, the sequence of the human Glu1- fibrinopeptide B can be determined from MS/MS spectra shown in an annotated form below. Note that most of the b peaks are b* resulting from lost of NH3 from the N terminus.

Figure: Annotated MS/MS spectra of human Glu1- fibrinopeptide B

12.4: Interpreting Mass Spectra

When interpreting fragmentation patterns, you may find it helpful to know that, as you might expect, the weakest carbon-carbon bonds are the ones most likely to break. You might wish to refer to the table of bond dissociation energies when attempting problems involving the interpretation of mass spectra.

This page looks at how fragmentation patterns are formed when organic molecules are fed into a mass spectrometer, and how you can get information from the mass spectrum.


Dramatic technological advances in the biological sciences over the past few years have forged a new era of research including the emerging field of systems biology. Although the understanding of living organisms at the molecular system level is still in its infancy, it is evident that comprehensive investigations of the “omics cascade” with genomics, transcriptomics, proteomics, and metabolomics are important building blocks and will play a central role in this new science (see Fig. 1 ). The integrative analysis of an organism’s response to a perturbation on the transcriptome, proteome, and metabolome levels will lead to a better understanding of the biochemical and biological mechanisms in complex systems. However, whereas genomics, transcriptomics, and proteomics have made significant strides in technology development, the tools for the comprehensive examination of the metabolome are still emerging (Bino et al., 2004). Although metabolomics is the endpoint of the “omics cascade” and is the closest to phenotype, there is no single-instrument platform that currently can analyze all metabolites. Possibly, because there is at least the perception that the other “omic” approaches can be handled by a single platform, metabolomics has lagged behind the other technologies. This is illustrated in Figure 2 , showing the bibliographic search containing the words metabolomics, metabonomics, and proteomics in Chemical Abstracts Plus (SciFinder Scholar). While in 1999 three articles containing the keywords metabolomics or metabonomics were published, the number increased to 147 articles in 2003 and 203 in 2004. Moreover, the journal Metabolomics (Springer) was recently launched, which is dedicated to publish research results related to metabolomics technology development, data analysis and storage, integrated studies with other “omics” techniques, and metabolomics applications. The rising number of publications in the field demonstrates that metabolomics is not just a new “omics” word but a valuable emerging tool to study phenotype and changes in phenotype caused by environmental influences, disease, or changes in genotype. The comprehensive investigation of the metabolome is being complicated by its enormous complexity and dynamics. Metabolite distributions are subjected to high temporal and spatial variability for example, circadian fluctuations in mammalian organisms are well known. In addition, diet-dependent biological variability in mammalian systems can complicate the analysis (Vigneau-Callahan et al., 2001). A careful experimental design is therefore mandatory for the success of these types of investigations. The metabolome represents a vast number of components that belong to a wide variety of compound classes, such as amino acids, lipids, organic acids, nucleotides, etc. These compounds are very diverse in their physical and chemical properties and occur in a wide concentration range. For example, within lipids alone, not only high-abundance compounds, such as fatty acids, triglycerides, or phospholipids, are encountered, but also trace level components with important regulatory effects, such as eicosanoids derived from arachidonic acid. According to Beecher, 2,000 major metabolites seems to be a good estimate for humans (Beecher, 2003). This number can of course be vastly larger as one considers secondary metabolites. Some of these metabolites may be chemical mediators of great biological importance. Up to 200,000 metabolites can be encountered in the plant kingdom (Weckwerth, 2003). Consequently, studying the metabolome is a major challenge to analytical chemistry and a metabolomic analysis in its true sense, namely the quantitative analysis of all metabolites, cannot be achieved with the current analytical instrumentation.

The “Omics” cascade comprises complex datasets that as an entity comprehensively describe the response of biological systems to disease, genetic, and environmental perturbations. The most powerful database will integrate data from all omic levels. However, of these databases the metabolome is the most predictive of phenotype.

Bibliographic search in Chemical Abstracts Plus containing the keywords metabolomics and metabonomics using SciFinder Scholar (as of June 10th, 2005). A total of 696 journal articles were found. The dataset was further mined using the search parameter mass spectrometry or NMR. The diagram shows the frequency of total metabolomics publications (black squares), publications that mention mass spectrometry (black bars) and NMR (gray bars) from 1999 to 2004.

Currently, two complementary approaches are used for metabolomic investigations: metabolic profiling and metabolic fingerprinting (see Fig. 3 ) (Dettmer & Hammock, 2004). A summary of metabolomics-related definitions is given in Table 1 . Metabolic profiling focuses on the analysis of a group of metabolites either related to a specific metabolic pathway or a class of compounds. The quantitative analysis of fatty acids as fatty acid methyl esters by GC-FID (flame ionization detection) or the analysis of amino acids are examples for metabolic profiling. An even more directed approach is target analysis that aims at the measurement of selected analytes, such as biomarkers of disease or toxicant exposure, or substrates and products of enzymatic reactions (Fiehn, 2002). In most cases metabolic profiling is a hypothesis-driven approach rather than a hypothesis-generating one. Based on the questions asked, metabolites are selected for analysis and specific analytical methods are developed for their determination. The tremendous technology advances over the past few years allow a constant expansion of the number of analytes that are quantified simultaneously in a single analysis. Technologically, the analysis of single biomarker is often as complex as profiling all related key metabolites in a given biochemical pathway. However, the latter results will give a more complete and detailed description of metabolic perturbations than a single biomarker can provide. The results of metabolic profiling are quantitative and ideally independent of the technology used for data acquisition. Consequently, the data can be used to build databases that can be integrated with pathway maps or other “omics” data, which will enhance biological understanding. Although quantitative metabolite data from different model organisms are abundant in the literature, their integration in global databases has yet to be accomplished.

Strategies for metabolomic investigations.


MetaboliteSmall molecules that participate in general metabolic reactions and that are required for the maintenance, growth and normal function of a cell * .
MetabolomeThe complete set of metabolites in an organism.
MetabolomicsIdentification and quantification of all metabolites in a biological system.
Metabolic profilingQuantitative analysis of set of metabolites in a selected biochemical pathway or a specific class of compounds. This includes target analysis, the analysis of a very limited number of metabolites, e.g. single analytes as precursors or products of biochemical reactions.
Metabolic fingerprintingUnbiased, global screening approach to classify samples based on metabolite patterns or 𠇏ingerprints” that change in response to disease, environmental or genetic perturbations with the ultimate goal to identify discriminating metabolites.
Metabolic footprintingFingerprinting analysis of extra-cellular metabolites in cell culture medium as a reflection of metabolite excretion or uptake by cells.

The disadvantage of metabolic profiling is that the system is not a global or true “omics” approach. However, numerous quantitative metabolic profiling methods analyzing different metabolite classes have already been developed and are routinely used. If these methods measuring key metabolites from different biochemical pathways are assembled as building blocks to study the metabolome, a powerful metabolomics approach will evolve.

The second approach towards metabolomics is metabolic fingerprinting. Initially in this approach the intention is not to identify each observed metabolite, but to compare patterns or 𠇏ingerprints” of metabolites that change in response to disease, toxin exposure, environmental or genetic alterations. A typical, but simplified workflow for a metabolic fingerprinting analysis is shown in Figure 4 . Metabolic fingerprinting has been performed in a wide variety of biological matrices, such as urine, plasma or serum, saliva, and tissues or cells. In addition to metabolic fingerprinting of intracellular metabolites in cell culture systems, the analysis of extracellular metabolites excreted into the culture medium or taken up from the medium by cells can provide valuable information on their phenotype and physiological state. Pattern analysis of metabolites in conditioned cell culture media is called metabolic footprinting (Allen et al., 2003, 2004). Since metabolic fingerprinting can be simultaneously applied to a wide range of metabolites, it is a true “omics” approach. The implementation of nuclear magnetic resonance (NMR)-based metabolic fingerprinting has marked the beginning of a metabolomics approach as a tool in biochemistry and has proven to be extremely powerful in screening samples for a variety of signature patterns or clusters. Metabolic fingerprinting can be used as a diagnostic tool, for example, by evaluating a patient’s metabolic fingerprint in comparison to healthy and diseased subjects. In addition, the success of treatment strategies can be monitored by observing if the metabolic phenotype shifts back to the healthy state, or in other words if a sample after treatment falls in the cluster of healthy subjects. However, using metabolomics exclusively for fingerprinting without identifying the metabolites that cause clustering of experimental groups will only deliver a classification tool but not directly contribute to biochemical knowledge and understanding of underlying mechanisms of action. The real power of metabolomics is realized when qualitative and quantitative analyses are performed. The knowledge of metabolite identity and their quantitative perturbation as descriptors of differences in specific phenotypes will provide information that can be interpreted in the light of biochemical pathways. Therefore, metabolites causing group segregation in the fingerprinting approach need to be identified and quantitative methods for the analysis of these metabolites and related compounds should be developed, which will tie metabolic fingerprinting and profiling together. Consequently, the annotation of the metabolome is an important building block for successful metabolomics investigations.

Simplified workflow for a metabolic fingerprinting analysis.

Both metabolic fingerprinting and profiling can be used in the search for new biomarkers. The value of blood glucose and cholesterol tests in medical diagnostics illustrates the value of even simple biomarkers. Metabolomics can yield new biomarkers that can reach the clinic as tools to diagnose health status, disease, or outcome of pharmacological treatment. Metabolomics is not limited to individual biomarkers. It rather represents a new approach to diagnostics where large data sets can be employed in total to develop understanding. For example, evaluating related biochemical pathways in response to drug treatment will give a more complete description of feedback mechanisms or crosstalk than single biomarkers can deliver. Moreover, the concept of individualized health including nutrition but also tailored pharmacological treatment based on metabolic phenotype will rely strongly on metabolomics technology (Watkins & German, 2002). The promise of the technology in clinical medicine to move from milliliter to microliter samples and to move from a few to thousands of analytes is exciting, but the potential of the technology to generate biological understanding is possibly still more significant.

Numerous analytical platforms have been used for metabolomic applications, such as NMR (Nicholson & Wilson, 2003), Fourier transform-infrared spectroscopy (FT-IR) (Harrigan et al., 2004 Johnson et al., 2004) and mass spectrometry (MS) coupled to separation techniques, or using direct flow injection. The great advantages of NMR are the potential for high-throughput fingerprinting, minimal requirements for sample preparation, and the non-discriminating and non-destructive nature of the technique. However, only medium to high abundance metabolites will be detected with this approach and the identification of individual metabolites based on chemical shift signals that cause sample clustering in multivariate analysis is challenging in complex mixtures. Mass spectrometry-based metabolomics offers quantitative analyses with high selectivity and sensitivity and the potential to identify metabolites. Combination with a separation technique reduces the complexity of the mass spectra due to metabolite separation in a time dimension, provides isobar separation, and delivers additional information on the physico-chemical properties of the metabolites. However, mass spectrometry-based techniques usually require a sample preparation step, which can cause metabolite losses, and based on the sample introduction system and the ionization technique used, specific metabolite classes may be discriminated. Therefore, parallel application of several techniques, for example, GC-MS and LC-MS is desired to study the metabolome comprehensively. Currently, mass spectrometry-based metabolomics is a dynamically emerging field with a number of annual publications exceeding published NMR-based investigations (see Fig. 2 ).

However, not only the choice of analytical techniques requires careful consideration, but the whole metabolomics experiment should be planned as an integrated unit, because the instrumental data are only as good as the experimental design and sample treatment. In this context Bino et al. (2004) proposed, the minimum information about a metabolomics (MIAMET) experiment, which should be reported with each study in order to facilitate the exchange of information and the establishment of databases. Similar recommendations regarding meta-data have been made for the other “omics” technologies.

In general, for every type of MS-based metabolomics experiment the following steps need to be addressed during method development and validation:

Sample analysis including metabolite separation and MS detection,

Mass spectrometry

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Treatment methods for the determination of δ 2 H and δ 18 O of hair keratin by continuous-flow isotope-ratio mass spectrometry

The structural proteins that comprise ∼90% of animal hair have the potential to record environmentally and physiologically determined variation in δ 2 H and δ 18 O values of body water. Broad, systematic, geospatial variation in stable hydrogen and oxygen isotopes of environmental water and the capacity for rapid, precise measurement via methods such as high-temperature conversion elemental analyzer/isotope ratio mass spectrometry (TC/EA-IRMS) make these isotope systems particularly well suited for applications requiring the geolocation of hair samples. In order for such applications to be successful, however, methods must exist for the accurate determination of hair δ 2 H and δ 18 O values reflecting the primary products of biosynthesis. Here, we present the results of experiments designed to examine two potential inaccuracies affecting δ 2 H and δ 18 O measurements of hair: the contribution of non-biologic hydrogen and oxygen to samples in the form of sorbed molecular water, and the exchange of hydroxyl-bound hydrogen between hair keratin and ambient water vapor. We show that rapid sorption of molecular water from the atmosphere can have a substantial effect on measured δ 2 H and δ 18 O values of hair (comprising ∼7.7% of the measured isotopic signal for H and up to ∼10.6% for O), but that this contribution can be effectively removed through vacuum-drying of samples for 6 days. Hydrogen exchange between hair keratin and ambient vapor is also rapid (reaching equilibrium within 3–4 days), with 9–16% of the total hydrogen available for exchange at room temperature. Based on the results of these experiments, we outline a recommended sample treatment procedure for routine measurement of δ 2 H and δ 18 O in mammal hair. Copyright © 2005 John Wiley & Sons, Ltd.

Ethics declarations

Competing interests

The authors declare an ongoing collaboration with Protein Metrics in establishing an HDX data processing platform, a topic that is not related to this protocol. The recommendation of using Protein Metrics as preferred data processing software for FPOP data predates the ongoing collaboration. M.L.G. is an unpaid member of the scientific advisory board of GenNext Technologies, which provides products and services for hydroxyl radical footprinting.


The foundation of mass spectroscopy was laid in 1898, when Wilhelm Wien, a German physicist, discovered that beams of charged particles could be deflected by a magnetic field. In more refined experiments carried out between 1907 and 1913, the British physicist J.J. Thomson, who had already discovered the electron and observed its deflection by an electric field, passed a beam of positively charged ions through a combined electrostatic and magnetic field. The two fields in Thomson’s tube were situated so that the ions were deflected through small angles in two perpendicular directions. The net result was that the ions produced a series of parabolic curves on a photographic plate placed in their paths. Each parabola corresponded to ions of a particular mass-to-charge ratio with the specific position of each ion dependent on its velocity the lengths of the parabolic curves provided a measure of the range of ion energies contained in the beam. Later, in an attempt to estimate the relative abundances of the various ion species present, Thomson replaced the photographic plate with a metal sheet in which was cut a parabolic slit. By varying the magnetic field, he was able to scan through a mass spectrum and measure a current corresponding to each separated ion species. Thus he may be credited with the construction of the first mass spectrograph and the first mass spectrometer.

The most noteworthy observation made with the parabola spectrography was the spectrum of rare gases present in the atmosphere. In addition to lines due to helium (mass 4), neon (mass 20), and argon (mass 40), there was a line corresponding to an ion of mass 22 that could not be attributed to any known gas. The existence of forms of the same element with different masses had been suspected since it had been found that many pairs of radioactive materials could not be separated by chemical means. The name isotope (from the Greek for “same place”) was suggested by the British chemist Frederick Soddy in 1913 for these different radioactive forms of the same chemical species, because they could be classified in the same place in the periodic table of the elements. The ion of mass 22 was, in fact, a stable heavy isotope of neon.

<p>This section provides information on the quaternary structure of a protein and on interaction(s) with other proteins or protein complexes.<p><a href='/help/interaction_section' target='_top'>More. </a></p> Interaction i

<p>This subsection of the <a href="">'Interaction'</a> section provides information about the protein quaternary structure and interaction(s) with other proteins or protein complexes (with the exception of physiological receptor-ligand interactions which are annotated in the <a href="">'Function'</a> section).<p><a href='/help/subunit_structure' target='_top'>More. </a></p> Subunit structure i

Homo/heterodimer, or complexes of higher-order. The structure of beta-crystallin oligomers seems to be stabilized through interactions between the N-terminal arms (By similarity).

<p>This subsection of the '<a href="">Interaction</a>' section provides information about binary protein-protein interactions. The data presented in this section are a quality-filtered subset of binary interactions automatically derived from the <a href="">IntAct database</a>. It is updated at every <a href="">UniProt release</a>.<p><a href='/help/binary_interactions' target='_top'>More. </a></p> Binary interactions i


GO - Molecular function i

Protein-protein interaction databases

The Biological General Repository for Interaction Datasets (BioGRID)

Protein interaction database and analysis system

Molecular INTeraction database

STRING: functional protein association networks

Miscellaneous databases

RNAct, Protein-RNA interaction predictions for model organisms.

The basics of mass spectrometry

Since their inception in 1912, mass spectrometers have undergone continuous development, and these sophisticated bioanalytical instruments have now reached unrivalled detection limits, speed and diversity in applications. They detect the presence and abundance of peptides (or other biomolecules such as metabolites, lipids and proteins) using fundamental properties of molecules, such as mass, and net charge. When peptides obtain a net charge (usually through gain of protons), they are referred to as peptide ions.

All mass spectrometers have three fundamental components: an ion source, mass analyser and detector (Figure 1A). As mass spectrometers can only analyse gaseous ions, methods such as electrospray ionization (ESI) are needed to convert peptides from the liquid phase to gaseous ions. The liquid containing the peptides is pumped through a micrometre-sized orifice held at a high voltage (2–4 kV). Upon reaching this emitter, the steady stream of liquid disintegrates into extremely small, highly charged and rapidly evaporating charged droplets, leaving peptide ions in the gas phase. Even 20 years after John Fenn received the Nobel Prize for this discovery, the exact mechanisms are not completely understood. We know that the abundance of gaseous peptide ions is proportional to their original concentration, making it beneficial to use the lowest flow rates possible, thereby maximizing sensitivity. It is common in proteomics to separate peptide mixtures using high-performance liquid chromatography (HPLC) systems with flow rates of only a few hundred nanolitres per minute rather than millilitres in conventional HPLC.

Overview of sample preparation and instrumentation used in MS-based proteomics. (A) Proteins are digested into peptides using sequence-specific proteases. Optionally, post-translational modification (PTM)-containing peptides can be enriched using beads with specific surface chemistry or coupled antibodies. High-performance liquid chromatography (HPLC) separates peptides based on hydrophobicity, and they are subsequently analysed by a TOF mass spectrometer. (B) Alternatively, peptides can be analysed by an Orbitrap mass spectrometer, which is a mainstream instrument in proteomics.

The principal role of a mass analyser is to separate ions by their mass-to-charge ratios (m/z). Fundamentally, all ions are separated by modulating their trajectories in electrical fields. Mass analysers differ in the principle they use for separating ions, and this defines their preferred application areas. Quadrupoles, usually combined with time-of-flight (TOF) or Orbitrap analysers, are the most common in proteomics. Quadrupole mass analysers separate ions using an oscillating electrical field between four cylindrical rods in a parallel arrangement, where each pair of rods produces a radio frequency electrical field with a phase offset. The resulting electrical fields define a pseudo-potential surface that is configured to allow the transmission of all ions, or to selectively transmit ions of a specific m/z window.

TOF mass analysers separate ions based on the differences in velocities after acceleration to about 20 kV and subsequent different arrival times at the detector. A TOF can measure mass differences of one part per million (ppm) by detecting time differences of sub-microseconds. In contrast, the Orbitrap mass analyser distinguishes ions based on their oscillation frequencies. Ions are tangentially injected and then trapped in the Orbitrap, and they move along the length axis of a central metal spindle (Figure 1B). Although an Orbitrap is only a few centimetres long, the ions can rapidly travel up to several kilometres, enabling very high resolution (typically tens of thousands) and low ppm mass accuracy.

In proteomics, the quadrupole element is normally followed by a ‘collision cell’, which is a quadrupole where the ions can be fragmented. Either intact peptide ions or fragment ions enter the final stage that also contains the detector – the resulting spectra are called MS 1 or precursor ion spectra in the former case and MS 2 or product or MS/MS spectra in the latter. TOF instruments have microchannel plate (MCP) detectors, where each individual ion ejects electrons from a surface that are then amplified. Individual ions can be readily measured with MCPs, but this exquisite sensitivity comes with the caveat that the detector can easily saturate in case of high signals. In Orbitrap analysers, the ‘image current’ induced by the rapidly oscillating ions is measured, and it represents a quantitative readout of the strength of the individual ion packages. The current is recorded in the time domain and is converted into the frequency domain using Fourier transformation. Advances in signal processing algorithms have repeatedly doubled the achievable resolution with a given transient time of the signal, but these are still orders of magnitude slower than those of TOF analysers (tens to hundreds of milliseconds vs typically 100 microseconds for a single TOF pulse).

How do the MS instruments sequence or identify peptides? Precursor ions with a specific m/z are first isolated by the quadrupole and fragmented through collision with inert gases such as N2, He or Ar. This causes them to break apart at the lowest energy bonds – typically, some of the amide bonds (peptide bonds) connecting the amino acid residues – and leaves MS/MS spectra with incomplete ladders of peaks differing by amino acid masses. This information is incredibly specific and is used for identification of the peptide sequence. A sequence of just a few amino acids and the flanking masses – a peptide sequence tag – is sufficient for identifying a peptide from the entirety of human proteome. More usually, database identification involves generating all possible fragmentation spectra and then statistically scoring them against the experimental spectra.

The chromatographic retention time is an important level of information when matching a dataset against a previous measurement and is key to ‘targeted proteomics’ technologies. Furthermore, ion mobility analysis, an additional dimension of separation of peptide ions, has recently become mainstream. Ions are either filtered based on their cross-section (FAIMS, field asymmetric ion mobility spectrometry) or actually separated during their analysis (T-Wave or TIMS, trapped ion mobility spectrometry). TIMS is the basis of the parallel accumulation–serial fragmentation (PASEF) technology, which multiplies sequencing speed 10-fold while improving sensitivity.

Broad Institute

Mass spectrometry is an analytical tool useful for measuring the mass-to-charge ratio (m/z) of one or more molecules present in a sample. These measurements can often be used to calculate the exact molecular weight of the sample components as well. Typically, mass spectrometers can be used to identify unknown compounds via molecular weight determination, to quantify known compounds, and to determine structure and chemical properties of molecules.

How does a mass spectrometer perform such a feat? Every mass spectrometer consists of at least these three components:

1. The Ionization Source

Molecules are converted to gas-phase ions so that they can be moved about and manipulated by external electric and magnetic fields. In our laboratory we use a technique called nanoelectrospray ionization, which is somewhat similar to how cars are industrially painted. This method allows for creating positively or negatively charged ions, depending on the experimental requirements. Nanoelectrospray ionization can directly couple the outlet of a small-scale chromatography column directly to the inlet of a mass spectrometer. The flow from the column is passed through a needle that is 10-15 um at its tip.

2. The Mass Analyzer

Once ionized, the ions are sorted and separated according to mass-to-charge (m/z) ratios. There are a number of mass analyzers currently available, each of which has trade-offs relating to speed of operation, resolution of separation, and other operational requirements. The specific types in use at the Broad Institute are discussed in the next section. The mass analyzer often works in concert with the ion detection system.

3. Ion Detection System

The separated ions are then measured and sent to a data system where the m/z ratios are stored together along with their relative abundance. A mass spectrum is simply the m/z ratios of the ions present in a sample plotted against their intensities. Each peak in a mass spectrum shows a component of unique m/z in the sample, and heights of the peaks connote the relative abundance of the various components in the sample.

Watch the video: Amino Acid sequencing-Edman method (September 2022).


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