Combinatorial peptidomics: a generic approach for protein expression profiling
© Soloviev et al; licensee BioMed Central Ltd. 2003
Received: 28 May 2003
Accepted: 03 July 2003
Published: 03 July 2003
Traditional approaches to protein profiling were built around the concept of investigating one protein at a time and have long since reached their limits of throughput. Here we present a completely new approach for comprehensive compositional analysis of complex protein mixtures, capable of overcoming the deficiencies of current proteomics techniques. The Combinatorial methodology utilises the peptidomics approach, in which protein samples are proteolytically digested using one or a combination of proteases prior to any assay being carried out. The second fundamental principle is the combinatorial depletion of the crude protein digest (i.e. of the peptide pool) by chemical crosslinking through amino acid side chains. Our approach relies on the chemical reactivities of the amino acids and therefore the amino acid content of the peptides (i.e. their information content) rather than their physical properties. Combinatorial peptidomics does not use affinity reagents and relies on neither chromatography nor electrophoretic separation techniques. It is the first generic methodology applicable to protein expression profiling, that is independent of the physical properties of proteins and does not require any prior knowledge of the proteins. Alternatively, a specific combinatorial strategy may be designed to analyse a particular known protein on the basis of that protein sequence alone or, in the absence of reliable protein sequence, even the predicted amino acid translation of an EST sequence. Combinatorial peptidomics is especially suitable for use with high throughput micro- and nano-fluidic platforms capable of running multiple depletion reactions in a single disposable chip.
Keywordspeptidomics combinatorial peptidomics proteomics biotechnology mass spectrometry proteins peptides
Gerardus Mulder, a Dutch chemist who was the first to purify proteins in the middle of the 19th century, defined them to be "without doubt the most important of all substances of the organic kingdom, and without it life on our planet would probably not exist". However, despite more than one and a half centuries of scientific effort, proteins are routinely being studied using traditional technologies, which have long since reached their limits of throughput. Techniques such as 2D gel electrophoresis, chromatography or a combination of the two are now widely available, but have a number of disadvantages in that they do not allow a highly parallel approach due to their physical limitations, large sample consumption and high costs. Protein staining on gels is biased towards highly abundant proteins and yields only qualitative information. In addition, in all proteomics applications based on electrophoretic or chromatographic separation of complex protein mixtures, the purity of final preparations is inversely proportional to the quantity of the materials obtained. This means that larger amounts of highly complex protein mixtures and more purification steps (or separation dimensions) are required in order to yield enough material of sufficient purity for subsequent mass spectrometry (MS) or other applications. Most chromatography based techniques suffer from poor reproducibility. An alternative approach using isotope-coated affinity tags (ICAT) has been developed to allow relative quantitation of proteins by MS . ICAT utilises isotope coding to quantitate differential protein expression, but the peptide pools obtained are too complex for convenient resolution by MS. More recently another completely different technology has been applied for proteomics research. This technology employs arrays of affinity ligands (antibodies or other agents) immobilised on a variety of solid supports [2, 3]. Using arrayed affinity ligands avoids the need for protein separation, as all of the spotted reagents are spatially separated and their positions known. The use of fluorescently labelled protein mixtures further simplifies protein detection. Additional increases in protein array sensitivity and signal-to-noise ratio were reported using time resolved fluorescence  and planar waveguides as protein immobilisation substrates [5, 6]. However, unlike DNA chips, protein chip based proteomics faces significant difficulties due to the much more heterogeneous character of proteins compared to nucleic acids. A significant improvement in protein microarray technology has been achieved through the use of competitive displacement strategies . However, a whole cell protein repertoire is extremely complex and different proteins may require different solubilisation and separation techniques. The less than reproducible character of the protein sample preparation is capable of compromising any protein assay which follows. Current state-of-the-art in protein biochemistry has not yielded universal solubilisation and affinity assay conditions applicable to all cellular proteins, e.g., small and large, hydrophobic and hydrophilic, soluble and membrane associated, basic and acidic proteins. Protein heterogeneity significantly limits the applicability of affinity-based systems to small subsets of proteins having very similar physical characteristics.
We have previously shown that the composition of a protein mixture can be determined by directly assaying the peptides from crude tryptic or otherwise digested protein preparations using immobilised antibodies. The Peptidomics approach  resolves many of the problems associated with multiplex affinity assays (e.g. arrays of antibodies) by allowing single optimised reaction conditions to be used irrespective of the starting material (small soluble proteins or large transmembrane receptors). This has been achieved through proteolytic digestion of the protein sample, for example with Trypsin. Since protein samples are to be digested, protein solubilisation is less of an issue and may be omitted altogether. Peptidomics enables the high throughput screening of proteins in a microarray format and has several advantages over affinity capture of intact proteins. These include, the homogeneity of digested proteins (typically in the form of tryptic peptides), which results in a more uniform pool of target species, allowing more regular quantitation. As peptides are much more stable and robust than proteins, protein degradation is not an issue. Also, antibody reagents can be more easily generated, such as by chemical synthesis of in silico predicted peptides against which antibodies are raised, e.g. by phage display techniques [9–12] or using in-vitro evolution [13–15] and DNA-protein fusions [16–18]. Such affinity reagents can be obtained in a truly high throughput manner and their specificities and affinities can be more easily controlled. In Peptidomics, each protein is broken down into many smaller components, resulting in the availability of a range of peptides thus allowing multiple independent assays for the same "original" protein to be performed using most antigenic species. Peptides are also particularly suited to detection by mass spectrometric techniques, such as MALDI-TOF-MS for direct analysis of samples on a solid substrate such as microarrays. The peptide mass range is such that isotopic resolution is easily achieved and hence their masses can be accurately determined, allowing for mass matching database searches to be performed to confirm the specificity of the affinity capture.
Digestion of cellular fractions or even intact tissues results in the release of peptides, which will be mostly hydrophilic, thus further improving the assay. In contrast to a traditional affinity assay, Peptidomics allows multiple antibody-peptide pairs to be used to assay the same protein target (similarly to Affymetrix DNA oligonucleotide arrays, where up to 20 oligonucleotides may be generated against the same mRNA sequence http://www.affymetrix.com), thus increasing the reliability of the assay. One of the major drawbacks of any affinity assay-based technique, including Peptidomics, is the availability and the cost of capture agents. Unlike nucleic acids, which are both information carriers and perfect affinity ligands, every protein requires the production of its own unique affinity reagent (e.g. an antibody) the development of which, unlike the synthesis of an oligonucleotide or purification of a PCR product, may require significant amounts of time and resources.
Principles behind combinatorial peptidomics
Frequently used protein cleavage reagents
Preferred cleavage site
C-terminal to Lys, Ala and Tyr
C-terminal to hydrophobic residues, e.g., Phe, Tyr, Trp. Less sensitive with Leu, Met, Ala
C-terminal to Arg residues
N-terminal to Gly (X-Gly) in Pro-X-Gly-Pro
C-terminal to amino acids with small hydrophobic side chains
C-terminal to Arg residues
N-terminal to Asp and Cys
C-terminal to Asp and Glu
C-terminal to Lys
C-terminal to Arg in Gly-Arg-X
uncharged or aromatic amino acids
C-terminal to Arg in (Phe-Arg-X or Leu-Arg-X)
Broad specificity; preference for cleavage C-terminal to Phe, Leu, and Glu
N-terminal to amino acids with bulky hydrophobic side chains, e.g., Ile, Leu, Val, and Phe 5
C-terminal to Arg
C-terminal to Lys and Arg
C-terminal to Glu, less active with Asp
Preferred cleavage site
Asp – Pro
Hydroxylamine (alkaline pH)
Asn – Gly
N-bromosuccinimide (NBS) or N- chlorosuccinimide
Examples of amino acid side-chain specific chemistries
α Haloacetyl compounds: Iodoacetate; α haloacetamides; bromotrifluoroacetone; N chloroacetyliodotyramine
Cys, His, Met, Tyr
NH2 groups (slow at low pH)
N Maleimide derivatives: N ethylmaleimide (at pH < = 7)
NH2 groups (slow at low pH)
Mercurial compounds (most specific): p chloromercuribenzoate(PCMB)/p hydroxymercuribenzoate(PHMB) in H2O (optimum at pH 5, competitive displacement possible)
Disulphide reagents (reversible): 5,5 dithiobis (2 nitrobenzoic acid) (DTNB); 4,4 dithiodipyridine; methyl 3 nitro 2 pyridyl disulphide; methyl 2 pyridyl disulphide
NH2 groups (slow)
Diazonium compounds (optimum at pH9, unstable)
NH2, Trp, Cys and Arg (slow)
Dicarbonyl compounds (pH > = 7): glyoxal; phenylglyoxal; 2,3 butanedione; 1,2 cyclohexanedione
Lys at pH < = 7
p toluenesulphonylphenyl alaninechloromethyl ketone (TPCK); p toluenesulphonyllysine chloromethyl ketone (TLCK); Methyl-p-nitrobenzenesulphonate
Diethylpyrocarbonate (reversible at pH > = 7)
His (at pH4)
2 hydroxy 5 nitrobenzyl bromide (HNBB)
p nitrophenylsulphenyl chloride
α Haloacetyl compounds
Met at pH3; also Cys, His, Tyr
NH2 groups (slow at low pH)
The choice of proteases, crosslinking chemistries and of their combinations is important and is determined by the degree of depletion required and the frequency of the amino acids being targeted. The frequency (F) with which any amino acid occurs in proteins varies, but could approximately be taken as 1/20 to illustrate the principle:
F = 1/20
Number of chances (C) to find any particular amino acid "1" in the peptide containing n amino acids will therefore be approximately equal:
C 1 = n × F 1 = n/20 (approx)
If our assumption (F = 1/20) is correct, each 20 amino acid long peptide has on average one chance of being covalently linked to any single "filter". If two filters are used (in parallel or consecutively), then the number of chances (C) to precipitate a peptide containing n amino acids, i.e. to find any two amino acids "1" and "2" in such peptide will be equal approximately:
C 1+2 = n × F 1 + n × F 2 = n/10 (approx)
For any 3 amino acid filtering steps:
C 1+2+3 = n × F 1 + n × F 2 + n × F 3 = n/7 (approx)
Any 4 amino acids:
C 1+2+3+4 = n × F 1 + n × F 2 + n × F 3 + n × F 4 = n/5 (approx)
Any 5 amino acids:
C 1+2+3+4+5 = n × F 1 + n × F 2 + n × F 3 + n × F 4 + n × F 5 = n/4 (approx)
Any 6 amino acids:
C 1+2+3+4+5+6 = n × F 1 + n × F 2 + n × F 3 + n × F 4 + n × F 5 + n × F 6 = n/3.5 (approx)
To deplete a complex peptide mixture by amino acid-specific sorption a different number of filters may be needed, depending on the range of peptide lengths, which depends on the cleavage technique. Using our assumption that F = 1/20, a 3 – 4 amino acid long peptide may on average be crosslinked once (i.e. has on average one chance to be removed from the sample) through one of its amino acid side chains using all six filters. The degree of the depletion also depends on the average peptide length (see Table 1). The degree of depletion needs to be adjusted such as to yield a sufficiently depleted peptide pool suitable for direct analysis by a mass spectrometer, i.e. having ~1000 peptides in the sample. Generally speaking, the shorter the range of peptide fragment lengths, the greater the number of filters required for the same degree of depletion. The origin of the protein sample (whether whole cellular proteome or partially purified narrow subfraction, containing only few proteins) is another key factor determining the required degree of peptide depletion.
Peptide depletion using Methionine-reactive amino acid filter
Synthetic peptides used in this study
In affinity based systems the equilibrium state always includes both free and bound analyte (i.e. peptide or protein), with their ratio being dependant on the dissociation constant KD. Unlike an affinity recognition event, the chemical reaction can be brought to completion more easily. Because of its quantitative character, the depletion by irreversible chemical cross-linking is preferred to any affinity-based separation because of the inherently incomplete character of the latter. Accordingly, the MS spectrum of the depleted mixture, shown on Figure 3B, reveals no Met-containing peptides. Thus the single depletion step has reduced the complexity of this model peptide mixture two fold.
Relative quantitation of depleted peptide mixtures
Characterization of the complement of expressed proteins from a single genome is a central focus of the evolving field of proteomics and can only be accomplished using a high-throughput, generic process. The number of expressed genes in a cell is estimated to be of the order of 10,000, resulting in up to 100,000 proteins, including splice forms and post-translational modifications. Any single protein could theoretically be identified by a single peptide using a TOF or TOF/TOF MS, meaning that an order of 100,000 non-identical "random" peptides may be required to cover a complete cellular proteome. A single mass spectrum is capable of resolving approximately 1000 different peptide peaks within the mass range of 500 to 3500 Da, corresponding to 5 to ~35 amino acid long peptides (significantly larger numbers of peptides cannot be resolved due to the resolution capabilities of TOF mass spectrometry). Therefore only 10–20 such non-degenerate spectra (from non-identically depleted samples) may be sufficient to reveal on average a single peptide from each cellular protein, not including isoforms. Statistically significant results, or detailed isoform analysis may require more different spectra, e.g. a 96-well plate worth of non-degenerate low-complexity samples. If splicing and PTMs are of no concern and for pre-fractionated proteomes, the number of representative spectra (whichever way arising) may be much lower. Recent developments in the field of the FT-MS capable of sub-ppm resolution (e.g. the APEX series of machines developed at Bruker-Daltonics, http://www.daltonics.bruker.com) may further reduce the number of representative spectra required, ultimately down to just few or one per proteome.
So far two main ideologies have been followed to decrease the complexity of samples submitted to MS. One relied on protein fractionation, followed by digestion and MS. In another approach, samples are digested first, followed by peptide fractionation. All fractionation techniques to date have utilised physical properties of proteins/peptides (i.e. size, charge, hydrophilicity/hydrophobicity, affinity interactions, etc.) resulting in poorly reproducible, not very quantitative techniques and expensive affinity reagents.
Amino acid side chain specific "filters"
Average Peptide length in mixture
Number of amino acid filters required for near complete depletion
Maximum number of possible combinations of amino acid filters
6 (i.e. either"1" or "2" etc., etc.)
15 (i.e. either "1+2" or "1+3" etc.)
20 (i.e. either "1+2+3" or "1+2+4" etc.)
15 (i.e. "1+2+3+4" or "1+2+3+5" etc.)
6 (i.e. "all but 1" or "all but 2" etc.)
1 (i.e. "1+2+3+4+5+6")
Depletion versus enrichment
Comparison of the depletion and enrichment approaches to combinatorial peptidomics
1. Individual steps per each "filtering" stage
1 step process, i.e. bind to beads (beads discarded)
3 step process, i.e. bind to beads, wash and elute
2. Number of combinations using 6 amino acid filters
3. Range of suitable peptide lengths
Longer peptides require less "filtering" stages (i.e. 10 or more amino acid residues preferred)
Shorter peptides require less "filtering" stages (i.e. 10 or less amino acid residues preferred)
4. Complexity of peptide mixtures
5. Amino acid compositional complexity of the remaining peptides
Decreased (by the number of "filters" used)
Not changed (20 amino acids)
6. Quantitative analysis
A single-stage depletion is more straightforward and quantitative than a triple-stage enrichment
Enrichment approach is less straightforward and robust than the depletion
7. Scaling up
Possible (larger "filters" or consecutive stages)
Possible (larger "filters" or parallel reactions)
8. Scaling down
Possible (low fmol level MS sensitivity requires high pmol filter binding capacities)
Especially suitable : low fmol level MS sensitivity requires fmol binding capacities
9A. Limitations (overloading)
Large binding capacity of the "filters" is crucial – overloading will allow all peptides to pass the "filter"
Overloading of the "filters" is not an issue, excess of sample may be applied
9A. Limitations (incompletely digestion)
Products of incomplete digestion will be mostly eliminated
Products of incomplete digestion will be mostly retained and may interfere with the downstream purification and analysis steps
Problematic due to limitation (see above) – excess of binding sites required to maintain efficient separation. Suitable for micro-fluidic applications
Suitable for nano-applications, since smaller number of binding sites required (compared to depletion strategy)
Differential labelling may be very useful if absolute quantitation is required (as opposed to relative quantitation). This can be exemplified further as follows. The acylating reagents, such as described in Section 2.3 above, could, for example, be modified with either Fluorine, Chlorine, Bromine or Iodine (i.e. use bromine-isothiocyanate versus iodine-isothiocyanate). Alternatively these could be "mono-", "di-" or "tri-" etc. modifications (i.e. use fluorophenyl-isothiocyanate vs. difluorophenyl-isothiocyanate) or the amino-reactive chemistry could be modified itself (i.e. use isocyanate vs. isothiocyanate), or the isocyanates could be derivatised differently (i.e. isocyanate vs. phenyl-isocyanates etc). A combination of the above could lead to 100s of differential molecular tags (far exceeding the capabilities of isotope labelling) by using isocyanates or derivatives alone. The Δ m/z mass difference introduced could be made as low as 2 (1 is less preferable due to potential difficulties in resolving naturally occurring isotopes of the peptides), as in the case with 4-fluorophenyl-isothiocyanate (Δ m/z = 153) and 3,5-difluorophenyl-isocyanate (Δ m/z = 155), and there is practically no limit on the upper range of the mass differences. However, larger mass differences will complicate the resolution of corresponding peptides in complex mixtures and are therefore less preferable. The use of chemical labelling allows much smaller Δ m/z to be used and the number of chemical labels is not limited by the number of available isotopes (as in ICAT method, known in the field). Also more than two complex peptide mixtures could be differentially labelled and analysed simultaneously unlike the ICAT method  or D0-/D3-per-methyl esterification approach  or acrylamide/deuterated acrylamide labelling method  (two samples only in each method). In addition to the ability to easily choose the desired Δ m/z, the chemical modifications allow the mass range of the analysed peptides to be shifted upwards. So the low molecular weight peptides, otherwise subject to interference from MALDI matrix related species, could be shifted into high MW range (in addition to, or instead of being differentially labelled).
High throughput and nano-applications
Combinatorial peptidomics is suitable for use with a variety of platforms, including traditional systems (column-based co-IP), arrayed affinity reagents (antibody microarrays, e.g. on MALDI plates) and a variety of micro- and nano-fluidic applications. Except for a few obvious and easily accepted reasons for miniaturisation, such as increasing the throughput of an assay by packing more reaction chambers into the same volume, and decreasing the assays cost, miniaturisation is especially suitable if combinatorial peptidomics approaches are sought. Miniaturisation has the potential to increase the reaction kinetics due to much smaller reaction volumes (and therefore faster reagent diffusion times) and significantly increased surface to volume ratios. This is especially applicable to combinatorial peptidomics, since both depletion and enrichment involves immobilising amino acids and peptides onto the solid surfaces. Truly miniature applications do not require the use of porous resins and careful selection of pore sizes, since a high surface to volume ratio of a small capillary channel, which is also easier to control, may be sufficient.
The Enrichment strategy, as opposed to Depletion, would better suit nano-fluidic or other applications where the size of reaction chambers matters (e.g. chips, micro- or nano-fluidics devices, etc). Since no excess of binding sites is required and because of its stability towards sample overloading, the overall size of such a device could be made smaller if the enrichment approach is used. For example, if only a single enrichment step is used, and peptide capture is totally reversible, then as few as low femtomole amounts of crosslinking chemistries may be required to retain peptide amounts sufficient for MS detection. Overloading of such a "filter" should not lead to an increase in the retained and released peptides, since the binding capacity of such a "filter" is limited. Although depletion may be performed faster, the overall binding capacity of a single "depletion filter" may need to be order(s) of magnitude larger to secure the reaction kinetics and to secure stability against sample overloading.
Modern proteomics aims to systematically analyse proteins for their identity, quantity and function and therefore requires new more generic and truly multiplex approaches for protein research. The combinatorial peptidomics approaches presented in this manuscript rely on the quantitative depletion or enrichment of peptide pools by chemical crosslinking through amino acid side chains. Chemical depletion reduces both the complexity of a peptide pool and the amino acid compositional complexity to a degree required to allow direct MS analysis. The combinatorial approach utilises commonly used proteolytic digestion techniques and widely available chemistries, many of them being well documented. The combinatorial peptidomics approach allows the determining of the composition of a protein mixture by assaying peptides directly from crude tryptic digests without using antibodies or any other affinity selection and therefore enables protein identification on a proteome-wide scale. Using all possible combinations of enzymatic/chemical protein digestion methods plus all combinations of the adsorption chemistries may allow thousands of peptides to be identified. The method presented greatly advances the effort of identifying all cellular proteins in "one go". The simplicity and predictability of the combinatorial approach (i.e. on the basis of protein sequence alone) provides for optimization of strategies for quantitative analysis of known proteins by using calculated and predicted combinations of digestion and separation features. Combinatorial peptidomics is suitable for studying both novel protein targets and for routine diagnostic applications. Combinatorial peptidomics is the first generic proteomics technology, reliant on the information content of proteins and peptides for their separation and analysis. The Combinatorial peptidomics approach compares to existing affinity based separation systems just as digital signal processing compares to analogue systems – it forms the basis of the high throughput proteomics technologies of the future.
Materials and Methods
Peptide depletion using Methionine-reactive amino acid filter
All peptides were obtained from SIGMA-Genosys. The Met-reactive beads (obtained from The Nest Group, Southborough, MA, USA) were activated as follows: beads from one "Pi3" isolation pack (approx 10 ul dry settled volume) were washed ×5 times with ×400 ul Methanol, followed by ×3 washes with 10% Acetic Acid using a spin column. Following washing, the beads were resuspended in 400 ul 10% Acetic Acid and transferred to a 1.5 ml microcentrifuge tube. Beads were precipitated by centrifugation and the supernatant (Acetic Acid) was removed. Peptide samples were prepared as follows: 75 ul of a peptide mixture (Table 3), containing approximately 75 ug peptides in total, was mixed with 25 ul of glacial Acetic Acid. The peptide mixture was divided into two 50 ul aliquots. One aliquot was transferred to the microcentrifuge tubes with the activated Met-reactive beads, whilst another aliquot was incubated without beads. Samples were left at 22°C for 18 hours. Following the incubation tube with beads was spun for 1 min at 10,000 RPM in a microcentrifuge and supernatant was transferred to a fresh tube.
Mass spectrometric analysis was performed as described previously [7, 8]. Briefly, 5 ul aliquots were taken from each sample. The volume was then made up to 10 ul in 0.1 % TFA and the overall amount of TFA adjusted to 0.1 %. Each sample was bound to a Zip Tip, washed in 0.1 % TFA and eluted in 1 ul of a solution containing alpha-cyano-4-hydroxycinnamic acid (~2.5 mg/ml in 3:2 methanol/0.1% TFA) and deposited directly onto a target for MALDI-TOF-MS. All spectra were acquired in the standard reflector mode of a Voyager DE STR (Applied Biosystems, Foster City, CA). Four hundred laser shots were fired and the resulting mass spectra were averaged to produce each final trace.
For the relative quantitation study each graph was obtained from 3 separate mass spectra taken from three separately prepared samples (9 spectra altogether) for peptide mixtures both prior to depletion and after depletion with Met-reactive beads. All spectra were obtained under identical MS settings and all other details were the same. Values for each individual peptide were calculated as individual peak areas taken relative to the average peak areas from the 5 peptides (not-containing Met) for each separate spectrum. The normalised values were then plotted as means for each peptide (see above, +/- STDEV, n = 9) on both plots.
Figure 5A shows the mass spectrum of the NFHQYSVEGGK peptide, used for differential labelling. The labelling was done with either 4-fluorophenyl-isothiocyanate or 3,5-difluorophenyl-isocyanate. Labelling was carried out at 4°C. The peptide solution (5 ul) was added to 85 ul of H2O and the buffer concentration was adjusted to 20 mM Na-CO3 (pH9.5). Following that 10 ul of 10% solution of acylating reagents in DMSO was added to the peptide, mixed and incubated overnight. The peptide was chemically labelled with 4-fluorophenyl-isothiocyanate (introduced Δ m/z = 153), see Figure 5B and with 3,5-difluorophenyl-isocyanate (introduced Δ m/z = 155), see Figure 5C.
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