Elastase digests: New ammunition for shotgun membrane proteomics

47 Pages·2008·2.33 MB·English

MCP Papers in Press. Published on December 30, 2008 as Manuscript M800223-MCP200
Elastase digests: New ammunition for shotgun membrane
proteomics

Benjamin Rietschel1*, Tabiwang N. Arrey1*, Bjoern Meyer1, Sandra Bornemann1, Malte
Schuerken1, Michael Karas1, and Ansgar Poetsch2‡

1Institute for Pharmaceutical Chemistry, Goethe-University, Max-von-Laue-Str. 9, D-60438,
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Frankfurt, Germany; 2Plant Biochemistry, Ruhr-University, Universitaetsstr. 150, D-44801 nlo
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Bochum, Germany fro
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‡To whom correspondence should be addressed. [email protected], Tel. .m
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[phone], Fax. [phone], Plant Biochemistry, Ruhr-University, Universitaetsstr. lin
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150, D-44801 Bochum, Germany by/
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*Both authors contributed equally t o
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Running Title , 2
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Elastase digests

Abbreviations
α-CHCA, α-cyano-4-hydroxycinnamic acid; BR, bacteriorhodopsin; CB, Corynebacterium
glutamicum; LIT, linear ion trap; MudPIT, multidimensional protein identification technique;
PM, purple membrane; PMF, peptide mass fingerprint; S/N, signal-to-noise; TM, transmembrane
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Copyright 2008 by The American Society for Biochemistry and Molecular Biology, Inc.
Summary
Despite many advances in membrane proteomics during the last decade the fundamental problem
of accessing the transmembrane regions itself has only been addressed to some extent. The
present study establishes a method for the nanoLC-based analysis of complex membrane
proteomes on the basis of a methanolic porcine pancreatic elastase digest to increase
transmembrane coverage. Halobacterium salinarium purple and Corynebacterium glutamicum
membranes were successfully analyzed by using the new protocol.
We demonstrate that elastase digests yield a large proportion of transmembrane peptides, D
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facilitating membrane protein identification. The potential for characterization of a membrane a
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protein through full sequence coverage using elastase is given, but is restricted to the higher m
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abundant protein components. Compatibility of the workflow with the two most common mass ://w
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spectrometric ionization techniques, ESI and MALDI, was shown. Currently, better results are cp
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obtained using ESI, mainly due to the low response of MALDI for strictly neutral peptides. .o
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New findings concerning elastase specificity in complex protein mixtures reveal a new prospect g
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beyond the application in shotgun experiments. Furthermore, peptide mass fingerprinting with D
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less specific enzymes might be done in near future, but requires an adaptation of current search be
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algorithms to the new proteases. 0
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Introduction
Upon the introduction of modern mass spectrometric ionization techniques, such as MALDI (1)
and ESI (2), extremely powerful and valuable tools were given to researchers for the
identification and characterization of proteins. Nevertheless, the intricate analysis of membrane
subproteomes still represents one of the major challenges, despite the amount of “success”
reported in literature. Until now, there is no general protocol available to address membrane
proteomes as a whole, demonstrating their degree of difficulty and complexity.
During the past years, two different strategies in proteome analysis have evolved: the more D
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widespread bottom-up and top-down proteomics. The latter approach has been shown to provide a
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access to the transmembranal regions of membrane proteins and has the power to characterize m
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complete protein primary structures including labile covalent modifications (3,4). Nevertheless, ://w
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limitations are given through sample complexity, emphasizing the need for a liquid cp
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chromatographic separation on the protein level, which is comparable to LC peptide separation. .o
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Therefore, the bottom-up variant is the most commonly used method for the proteomic analysis g
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of complex membrane samples. Improvements in the bottom-up workflow were achieved by D
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adapting sample cleanup, pre-fractionation processes (5-10) and subsequently, by the be
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development of modified and optimized separation techniques. The two main workflows, the gel- 0
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based approach, mainly carried out with 2D-SDS-PAGE (11,12) and the shotgun identification of
proteolytically digested protein mixtures and their multidimensional separation via liquid
chromatography (13) had to be adapted. The separation of proteins via classical 2D-SDS-PAGE
is only possible up to a GRAVY score of approximately 0.4 (14,15). Despite lacking the
separation power of the IEF-SDS-PAGE system, derived techniques like the doubled SDS- (16)
and 16-BAC/SDS-PAGE (17) represent an improvement for hydrophobic proteins. Advances in
the nLC-separation of hydrophobic peptides, e.g. the separation at elevated temperatures (18) and
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the use of LC-compatible detergents (19), yielded significant success. Finally the combination of
both prominent separation techniques, the 1D-SDS-LC analysis proved to be more effective than
2D-PAGE as well (20,21).
One of the remaining steps, still offering room for improvements, is the proteolytic procedure.
The original MudPIT approach using trypsin has been successfully improved by the application
of proteinase K under high-pH conditions, yielding an increased number of membrane protein
identifications (22). Further modification of this method by re-cleaving the isolated membranous
parts with cyanogen bromide increased the accessibility to membrane-spanning peptides (18). D
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In spite of proteinase K’s suitability for the shotgun analysis of membrane proteomes, trypsin is a
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the prevalent enzyme choice in most current proteomic approaches, due to its very specific m
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cleavage behavior. Calculations have implied that alternative proteases are preferentially suited ://w
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for the analysis of membrane proteomes (23). Proteases other than trypsin have been utilized only cp
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to a vanishing degree and mostly for the targeted analysis of protein complexes. Pepsin, for .o
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example, was used for the characterization of an aquaporin (24) and elastase and subtilisin as g
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proteases in a triple digest approach of protein complexes and lens tissue (25). Further D
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improvements in the accessibility of the membrane proteome have been shown for tryptic (26, be
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27) and tryptic/chymotryptic (28) digests, by performing the proteolytic treatment in presence of 0
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methanol.
During membrane proteomic method development, purple membranes from Halobacterium sp.
NRC-1, consisting to a large extent of the H+-ATPase bacteriorhodopsin, have widely been used
as reference in a variety of cases. Full BR sequence coverage has been reported by several
publications, using different techniques (27,29). Such exorbitant sequence coverage amounts are
only achievable for the most abundant proteins within a complex mixture. The identification of
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less expressed proteins, which certainly are more in the researcher’s interest, occurs via lower
peptide numbers (27).
Generally, the basic tryptic cleavage sites are predominantly located in the loops facing the
cytoplasm, to a lesser extent in the extracellular or periplasmatic loops, but are very scarce within
the transmembrane helix stretches. This mostly limits the potential tryptic fragments to loop
components and larger peptides containing at least one TM helix. The use of another protease,
preferentially cleaving after neutral aliphatic residues, should not succumb to this lack of
cleavage sites. D
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One of the previously mentioned proteases, porcine pancreatic elastase, was reported to possess a
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potential cleavage specificity at the carboxyterminal side of small neutral amino acids (30). It has m
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previously been used for the examination of single protein phosphorylations (31) but was ://w
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described not to be suited for the cleavage of complex membrane-containing samples, due to a cp
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supposedly limited activity when applied to these (22). In spite of this finding, elastase has been .o
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successfully utilized in a mass spectrometric analysis as early as 1974, when it was regarded as g
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an ideal protease for future mass spectrometric studies (32). At the same time, most of the D
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experiments to characterize elastase by analyzing its S1 pocket (nomenclature, ref. 33) binding ber 2
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capabilities (34) as well as cleavage preferences of ester and protein substrates (35-39) were 0
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performed.
Based on previous research concerning elastase, we set up a nLC-based membrane proteomic
analysis. This large data set was used to characterize the protease behavior of elastase and the
detected peptides’ physicochemical properties. The PM model was used to establish a method to
analyze more complex Corynebacterium glutamicum membranes, which have previously been
analyzed using different 2D-techniques (7,28). We demonstrated that a combination of elastase
and methanol is particularly suitable for a nLC-based membrane proteome analysis, and results in
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a significantly increased number of TM peptides. Additionally, the promising PMF application of
elastase digests is discussed.

Experimental Procedures
Bacterial Cells and Chemicals - Corynebacterium glutamicum strain DM 1698 (derived from
ATCC 21527) membranes were purified as described before (7). Purple Membranes from
Halobacterium salinarium were a generous gift from Norbert Dencher (Physical Biochemistry,
TU Darmstadt, Germany). TCPK-treated dimethylated trypsin was purchased from Sigma- D
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Aldrich (Hamburg, Germany), porcine elastase (Grade II) from Roche Diagnostics (Mannheim, a
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Germany). All other chemicals and solvents were obtained from Carl Roth (Karlsruhe, Germany), m
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if not otherwise noted. Only HPLC-grade or better solvents were used throughout the ://w
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experiments in this study. cp
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Proteolytic Digest - The proteolytic treatment represents a modified version of the specific g
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integral membrane peptide level enrichment (SIMPLE) as described in (28). One hundred D
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micrograms of PM was washed using 25 mM NH4HCO3 buffer (pH 8.0). After a 2 min ber 2
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centrifugation at 10,000 g the supernatant was removed and the pellet was gently resuspended in 0
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methanol. Following a 20-minute period of sonication, the methanol was diluted to 60% using 25
mM NH HCO buffer. The proteolytic digest was started by adding either 2 µg of trypsin or 10
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µg of elastase and incubated for 24 hours at 30 °C. To avoid any adhesion of the membrane
slurry to the tubes, the whole digest process was performed without any external shaking. Prior to
storing the peptide-containing supernatant at -20 °C, the sample was mixed and centrifuged at
10,000 g for 2 min.
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In the case of the more complex CB, which protein content was determined after the method of
Lowry (40), an additional digestive step before the actual proteolytic treatment was introduced in
order to remove the loosely attached cytoplasmic contaminants. Two hundred micrograms of
washed membranes were digested with the appropriate enzyme in 25 mM NH HCO for 12 hours
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at 30 °C. Then the supernatant was discarded after centrifugation and the pellet underwent the
same procedure as described above, starting with the methanol addition.

MALDI MS - Mass spectra of in solution digests were acquired using either an Applied D
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Biosystems/MDS Sciex 4800 TOF/TOF™ Analyzer or a Bruker Daltonics Ultraflex™ TOF/TOF. loa
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Appropriate dilutions of the digest solutions were spotted on a target via the standard dried m
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droplet preparation protocol. The matrix solution containing 2 mg/ml α-cyano-4- ://w
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hydroxycinnamic acid and all dilutions were prepared with 50% ACN, 50% H2O, 0.5% TFA cpo
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solvent. Reduction of salt contaminations was achieved by washing each preparation with ice .o
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cold 5% formic acid. g
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nLC-MALDI MS/MS - Nanoflow LC separation was carried out using an Agilent Technologies be
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1100 series HPLC system consisting of a nano-capillary pump, solvent degasser, a fraction 0
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collector and a thermostatic autosampler. Sample containing 25 µg of PM or 200 µg of CB
proteins were vacuum centrifuged to a volume of about 8 µl. Solvent A (92% H O, 8% ACN,
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0.1% TFA) was added to obtain a total volume of 18 µl. This peptide mixture was injected onto
the pre-column (Agilent Zorbax™ 300 SB C18, 75 µm x 3 mm). Pre-concentration and desalting
was carried out by flushing with Solvent A at a flow rate of 20 µl/min. The separation of the
peptide mixture was performed on a 75 µm x 150 mm, C18 3.5, 100 Å, LC packings Dionex
Pepmap™ 100 column at a flow rate of 300 nl/min with increasing ACN concentration. A step
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gradient mobile-phase system was used: 3-55% Solvent B (95% ACN, 5% H O, 0.1 % TFA) in
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110 min, 55-100% B in 10 min. 100% B for 10 min, 100-3% in 3 min and 2 min at 3% B were
applied. The separated peptides were mixed on a tee (Upchurch Scientific, USA) with matrix
solution supplied by an auxiliary pump (flow rate, 1.2 µl/min). This solution contained 3 mg/ml
α-CHCA (Bruker Daltonics, Bremen, Germany) dissolved in 70% ACN, 30% H O, 0.1% TFA,
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spiked with 20 fmol final amount of [Glu1]-Fibrinopeptide B (Bachem, Weil, Germany) per spot
for internal calibration. The final mixture was directly spotted every 15 seconds on a blank 123 x
81 mm Opti-TOF™ LC/MALDI Insert metal target. D
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Subsequent MALDI-TOF/TOF measurements were carried out using the 4800 TOF/TOF™ loa
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Analyzer. All peptides used for calibration were taken from the Sequazyme™ Peptide Mass from
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Standards Kit by Applied Biosystems. Spectra were acquired in the positive reflector mode ://w
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between 700 and 6000 m/z with fixed laser intensity. A total of 750 laser shots per spot were cp
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accumulated. An 8-point plate model default calibration, followed by internal recalibration to the .o
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spiked [Glu1]-Fibrinopeptide B mass was performed, in order to ensure proper MS/MS precursor g
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accuracy within 30 ppm. The precursor selection for MS/MS was carried out via the instrument’s D
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software job-wide interpretation feature to avoid unnecessary multiple selections of identical be
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precursor peptides. Up to 6 precursors per spot were selected for fragmentation, each requiring a 0
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minimum signal-to-noise-ratio of 50. The fragmentation of the selected precursors was performed
at a collision energy of 1 kV using air as collision gas at a pressure of 1 x 10-6 Torr. Depending
on the spectral quality, 1250 up to 2500 laser shots were recorded. Potential matrix cluster signals
were removed from precursor selection, by excluding all masses in the range from 700 to 1400
m/z having values of .030 +/- .1 m/z as well as the internal calibrant mass.

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nLC-ESI MS/MS – Twenty-five µg PM or 200 µg of CB protein digest were pre-purified with
C-18 syringe tips (C18-AR, Varian), the peptides were eluted with Solvent B (80% ACN, 0.1%
formic acid), vacuum concentrated, and reconstituted in 20 µl Solvent A (2% ACN, 0.1% formic
acid). The nLC-ESI-MS/MS system was operated generally as described previously (28,41),
except that mass spectra were recorded on an LTQ-Orbitrap instead of an LTQ. The linear ion
trap and orbitrap were operated in parallel, i.e. during a full MS scan on the orbitrap at a
resolution of 60,000, MS/MS spectra of the four most intense precursors were detected in the ion
trap. Further instrument settings were used as described by Fischer et al. (28). The solvent D
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gradient was from 0-50% solvent B in 120 min, followed by 10 min solvent B and 10 min solvent a
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A with a flow rate of ~ 250 nl/min on the capillary column. m
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Database searches - Mascot Generic Format (MGF) files were retrieved from each nLC-MALDI cp
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MS/MS run using the built-in Peaks2Mascot feature, exporting up to 65 peaks per MS/MS .o
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spectrum, each requiring a minimum signal-to-noise of 5. The corresponding ESI files were g
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generated by first extracting all scans from the Xcalibur binary files using Thermo Electrons D
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export_msn tool. All settings were at default, except for the number of grouped scans which was be
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reduced to 1. The generated .dta files containing the MS/MS data were then merged into a single 0
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.mgf file using the merge.pl script hosted on the Mascot™ site (http://www.matrixscience.com).
MS/MS peak lists from 3 to 6 replicate runs were concatenated into one single file for the
database search.
MS/MS queries were processed using the Mascot™ database search engine v2.2.03 (Matrix
Science Ltd., UK; ref. 42). Data was analyzed using the following settings: < 30 ppm MS
precursor mass tolerance, < 0.5 Da (MALDI-TOF/TOF); < 5 ppm MS precursor mass tolerance,
< 0.8 Da MS/MS mass tolerance (ESI-LTQ-Orbitrap). When tryptic searches were performed, up
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to 4 missed cleavages were taken into consideration. In all elastase searches, the number of
missed cleavages was set to the maximum value of 9, if any enzyme specificity was defined.
Amino-terminal pyroglutamate formation was considered as variable modification in all PM
database searches, in addition to methionine oxidation into the resulting sulfoxide which was
considered for both samples. For this purpose a custom Halobacterium database was generated
from SwissProt/TrEMBL containing 2490 entries as at November 30th 2007. With respect to
database searches involving CB samples, a Corynebacterium glutamicum ATCC 13032 Bielefeld
database containing 4008 sequences was provided by Jörn Kalinowski (43). False discovery rates D
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given are those originating from the internal Mascot™ decoy database search function. A decoy loa
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database consisting of same-length random protein sequences is automatically generated and m
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searched. False discovery rate is calculated by dividing the absolute number of decoy hits ://w
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through the sum of decoy and true hits against the actual database. cp
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Result interpretation and data analysis - All statistical analyses in this study are exclusively g
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based on peptides having Mascot™ MS/MS ions scores exceeding the “identity or extensive n D
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homology threshold” (p < 0.05). The required threshold value for each individual peptide is be
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stated in Table S1. Neither general PMF data nor low-scoring peptides were included, even if 0
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after manual inspection a true hit was obtained. In the case of multiple fragmentations of identical
precursors, due to recurrence in repetitive runs, only data from the highest scoring peptide is kept.
PMF and protein sequence coverages were calculated using biotools™ 2.2 from Bruker Daltonics.
Peptide pIs were iterated based on amino acid pI values given in Lehninger’s Biochemistry (44).
Hydrophobicity was estimated via GRAVY score calculation (14). Protein transmembrane
stretches were predicted using the TMHMM algorithm (45) and displayed in the TMRPres2D
viewer (46).
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