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Originally published In Press as doi:10.1074/mcp.M700505-MCP200 on March 24, 2008.
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Molecular & Cellular Proteomics 7:1241-1253, 2008.
© 2008 by The American Society for Biochemistry and Molecular Biology, Inc.

Research

In-cell Selectivity Profiling of Serine Protease Inhibitors by Activity-based Proteomics*,S

Ludovic C. J. Gillet{ddagger}, Kenji Namoto{ddagger}, Alexandra Ruchti§, Sjouke Hoving§, Danielle Boesch{ddagger}, Bruno Inverardi§, Dieter Mueller§, Michele Coulot, Patrick Schindler, Patrick Schweigler{ddagger}, Anna Bernardi{ddagger} and Shirley Gil-Parrado{ddagger},||

From the {ddagger} Center for Proteomic Chemistry/Expertise Platform Proteases, § Developmental and Molecular Pathways, and Biologics Center, Novartis Institutes for BioMedical Research, CH-4002 Basel, Switzerland


   ABSTRACT
TOP
 ABSTRACT
EXPERIMENTAL PROCEDURES
 RESULTS AND DISCUSSION
 REFERENCES
 
Activity-based proteomics is a methodology that is used to quantify the catalytically active subfraction of enzymes present in complex mixtures such as lysates or living cells. To apply this approach for in-cell selectivity profiling of inhibitors of serine proteases, we designed a novel activity-based probe (ABP). This ABP consists of (i) a fluorophosphonate-reactive group, directing the probe toward serine hydrolases or proteases and (ii) an alkyne functionality that can be specifically detected at a later stage with an azide-functionalized reporter group through a Cu(I)-catalyzed coupling reaction ("click chemistry"). This novel ABP was shown to label the active site of several serine proteases with greater efficiency than a previously reported fluorophosphonate probe. More importantly, our probe was cell-permeable and achieved labeling of enzymes within living cells with efficiency similar to that observed for the corresponding lysate fraction. Several endogenous serine hydrolases whose activities were detected upon in-cell labeling were identified by two-dimensional gel and MS analyses. As a proof of principle, cell-permeable inhibitors of an endogenous serine protease (prolyl endopeptidase) were assessed for their potency and specificity in competing for the in situ labeling of the selected enzyme. Altogether these results open new perspectives for safety profiling studies in uncovering potential cellular "side effects" of drugs (unanticipated off-target inhibition or activation) that may be overlooked by standard selectivity profiling methods.

Activity-based proteomics, in comparison with classic genomics and proteomics approaches, has been specially devised to enable the detection of active enzymes. Such a methodology is of particular relevance for example in the protease field where only a subfraction of the total enzyme pool (having undergone successive translocation, post-translational modifications, and proteolytic activation and having escaped binding of endogenous inhibitors) effectively participates in the cellular processes. To profile enzymatic activities in biological samples, activity-based proteomics relies on small reactive marker molecules called activity-based probes (ABPs),1 which covalently and specifically label the accessible active sites of catalytic enzymes (1–3). So far, several directed and non-directed ABPs have been described that allow the monitoring of more than 20 enzyme classes (for extensive reviews, see Refs. 2–5).

The detection of the population of active enzymes is of primary relevance for biological and in particular for pharmaceutical research because it could lead to the discovery of new targets for drug development. Indeed by comparing the activity profile of enzymes under physiological versus pathological conditions (e.g. of normal cells versus parasite-infected cells (6) or versus cancer cells (7–11)), several groups have identified up-regulated active enzymes potentially involved in the development and/or the maintenance of given pathological conditions.

Another underexploited potential of activity-based proteomics is its application to assess the selectivity of enzyme inhibitors (12–16). In comparison with the standard approach that consists of measuring the potency of an inhibitor in consecutive in vitro assays against a limited number of purified recombinant enzymes, activity-based proteomics allows the simultaneous selectivity profiling of inhibitors against the whole set of labeled enzymes endogenously present in complex proteomes (cell or tissue extracts (12–16), living cells (14, 17–20), or animals (17, 18, 21)) by quantifying the decrease of labeling of those enzymes upon preincubation with the inhibitors. Again such an approach would be of particular relevance for the protease field because overlooked off-target inhibition could result in unforeseen side effects, contributing to the failure of protease inhibitors in clinical trials (for a review about challenges in the discovery of protease inhibitors, see Ref. 22).

In this study, we aimed to use the activity-based proteomics approach for in-cell selectivity profiling of inhibitors of serine proteases because the role of these enzymes in disease conditions (e.g. dipeptidyl-peptidase 4 (DPP4) in type 2 diabetes (22) or NS3 protease in hepatitis C virus infection (23)) has led to increasing interest in this protease family as important targets for drug discovery. For our purpose, the fluorophosphonate (FP) probe seemed of particular interest because it has been reported to be a very specific covalent modifier of the active site serine residue of a large number of serine hydrolases, including serine proteases (7, 10, 13, 16, 24–27). Moreover the usefulness of this probe for selectivity profiling of reversible inhibitors has already been validated by in vitro studies (13, 16). Nevertheless the reported FP probes generally carry a bulky reporter group (avidin (13, 24) or fluorescent moieties (24–26)) that might limit active site accessibility and cell permeability. To our knowledge, the use of FP probes for in situ labeling of serine hydrolases has not yet been reported. Here we present the design and preparation of a reporter-free fluorophosphonate ABP carrying an alkyne functionality (FP-alkyne). This novel FP-alkyne probe was shown to achieve efficient in situ labeling of enzymatic activities within living cells and proved to be a valuable tool to assess in-cell selectivity profiling of serine protease inhibitors.


   EXPERIMENTAL PROCEDURES
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
RESULTS AND DISCUSSION
 REFERENCES
 
Synthesis of the Alkyne Probe—
The procedures for the synthesis of the FP-alkyne serine hydrolase probe (1) and of the azide-fluorescein reporter (3) are provided in the supplemental data. The FP-fluorescein probe (2) was prepared as reported previously (13, 25).

Recombinant Proteins and Antibody—
Recombinant human prokallikrein 7 (pro-KLK7) was purchased from R&D Systems and was activated according to the manufacturer's protocol. Other recombinant proteases used in this study were kindly provided by the group of Bernd Gerhartz (Expertise Platform Proteases, Novartis Institutes for Biomedical Research). Anti-prolyl endopeptidase (anti-PEP) polyclonal antibody and rabbit anti-goat peroxidase conjugate were purchased from Novus Biologicals and Rockland, respectively.

IC50 Determination by in Vitro Fluorescence Assay—
The enzymatic assays were performed at 22 °C in a 384-well plate (Cliniplate Black, Labsystems) in 30 µl of the following reaction buffer: 50 mM Tris/HCl, pH 7.4, 50 mM NaCl, 5 mM EDTA, 0.05% (w/v) CHAPS, and 0.1% BSA. In a typical experiment, 10 µl of enzyme (PEP, 30 pM final concentration) was preincubated with 10 µl of inhibitor (0–30 µM final concentration) for 1 h before adding a 10-µl solution of the peptidic substrate Z-Gly-Pro-AMC (30 µM final concentration, Bachem). The residual enzymatic activity was determined by measuring the hydrolysis rate of the fluorescent substrate after 1-h incubation on an Ultra microtiter plate reader (Tecan) equipped with a 350/500-nm filter pair. The IC50 was calculated from the dose-response plot of percentage of enzymatic inhibition as a function of inhibitor concentration using the non-linear regression analysis software XLfit (version 4.2, ID Business Solutions Ltd. (IDBS)).

Labeling of Recombinant Proteins—
The labeling reactions were typically performed in 30 µl of Dulbecco's PBS (DPBS; pH 8) upon 1-h incubation of the recombinant enzymes (DPP4, 0.4 µM; DPP8, 0.2 µM; DPP9, 0.6 µM; PEP, 10 µM; and pro-KLK7/activated KLK7, 0.4 µM) with 10 µM FP-alkyne (1) or 50 µM FP-fluorescein (2). The detection of the FP-alkyne-labeled enzymes was then achieved using click chemistry essentially as described previously (28) by adding the following stock solutions in the following order: 0.6 µl of azide-fluorescein (10 mM in DMSO), 0.6 µl of tris(carboxyethyl)phosphine (TCEP; 25 mM freshly prepared in water), 1.8 µl of ligand synthesized as described previously (29) (1.7 mM in tert-butyl alcohol/DMSO), and 0.6 µl of CuSO4 (50 mM in water). After 1-h incubation in the dark, the reactions were then quenched with 7 µl of 5x SDS sample buffer, separated by 1D electrophoresis on precast Novex 4–20% Tris-glycine gradient gels (Invitrogen) according to the manufacturer's protocol. The 1D gels were scanned on a FluorImager SI (Vistra Fluorescence). Larger scale labeling of KLK7 (100 µl at 3.5 µM in DPBS) was essentially performed as described above with and without the click chemistry step. The excess of reagents was then removed by size exclusion chromatography on Zeba desalting spin columns (Pierce), and the eluted samples were concentrated on a SpeedVac. The samples were taken up in 25 µl of CH3CN:H2O:HCOOH (45:45:10) for direct nano-ESI measurement (see below) or denatured (RapiGest SF protocol, Waters) and trypsinized (sequencing grade modified trypsin, Promega) for nano-LC-MS or ESI-MS/MS analysis (see supplemental data for experimental details on the MS analyses).

Direct Nano-ESI Measurement on Intact, Full-length KLK7—
The MS analysis was performed on a QSTAR Pulsar hybrid quadrupole time-of-flight mass spectrometer equipped with a nanospray ion source (SCIEX/Applied Biosystems). The samples were diluted 10 times in CH3CN:H2O (1:1) supplemented with 0.1% HCOOH and loaded into palladium/gold-coated borosilicate needles (Proxeon). The needle was adjusted in front of the orifice, and the spraying process was started by applying a voltage difference (from 900 to 1300 V). MS spectra were acquired by scanning over the 1000–3000 m/z range in 1 s and accumulating 500 spectra (multichannel analysis mode). Declustering potentials in the orifice were set as follows: declustering potential, 100 V; and focusing potential, 300 V. The acquired spectra were deconvoluted using the Bayesian protein reconstruct program, part of the Bioanalyst QS 1.0 program (SCIEX/Applied Biosystems). Relative quantitation between the different ion species was performed using the area of the selected peaks.

Cell Culture and Lysate Preparation—
CaCo2 human colon colorectal adenocarcinoma cells were purchased from LGC Promochem and cultured until achieving 80–90% confluence in Dulbecco's Eagle's+GlutaMAX-1 (Invitrogen) medium containing 10% fetal bovine serum. For protein identification purposes upon in-cell labeling, typically five T175 cell dishes (Corning) were incubated with a 10 µM concentration of the probe for 1 h at 37 °C and washed three times with DPBS before harvesting. The pelleted cells were rinsed twice more with DPBS and taken up in 500 µl of DPBS before proceeding to the lysis. For in-cell IC50 determination, typically seven T75 cell dishes (Corning) were preincubated each with a different concentration of the inhibitor of interest (or with DMSO for the mock control) at 37 °C for 1.5 h before the addition of the probe (10 µM) and incubating for an additional 1 h. Washing and harvesting were then performed as indicated above, and the final cell pellet was taken up in 80 µl of DPBS before proceeding to the lysis. The lysis was achieved by three consecutive freeze-and-thaw cycles over dry ice. The lysates were centrifuged at 70,000 x g for 1 h. The resulting supernatants, corresponding to the crude cytoplasmic extracts, were assessed for protein concentration on an ND-1000 spectrophotometer (NanoDrop) at 280 nm and yielded a typical protein recovery of 5–20 mg/ml per sample. The lysates were eventually frozen at –80 °C at this stage or processed directly to the next step.

Click Chemistry and Sample Preparation—
The protein concentration of the different samples was normalized to 5 mg/ml for protein identification or 1 mg/ml for IC50 determination. The click chemistry reaction was performed in DPBS in a final volume of 150 µl essentially as described previously (28) by adding in the following order: 1.5 µl of azide fluorescein (10 mM), 3 µl of TCEP (25 mM freshly prepared), 9 µl of ligand (29) (1.7 mM in tert-butyl alcohol/DMSO), and 3 µl of CuSO4 (50 mM). After 1-h incubation at room temperature in the dark, a standard methanol/chloroform precipitation was directly performed on the samples to yield almost quantitative recovery of the proteins with concomitant removal of most excess non-reacted fluorescein. The air-dried precipitate was then redissolved in 150 µl of Rabilloud loading buffer (30) for 2D gel analysis.

Gel Electrophoresis—
2D gel electrophoresis was performed according to previously reported protocols (31, 32). Briefly the first dimension separation was performed using either pH 4–7 linear or pH 3–11 non-linear IPG strips (GE Healthcare) of 7 cm (for competition experiments and IC50 determinations; e.g. Figs. 5 and 6 and supplemental Fig. S6) or 24 cm (for spot picking and protein identification; e.g. Fig. 4 and supplemental Table T1, exp1–2). The IPG strips (pH 4–7) were directly rehydrated overnight with the samples resuspended in 150 or 450 µl of Rabilloud buffer for the smaller 7-cm or larger 24-cm strips, respectively. The IEF (pH 4–7) was performed with a Multiphor II unit (GE Healthcare) at 20 °C. For the smaller 7-cm strips, the following voltage profile was used: linear increase from 0 to 300 V in 1 min, 300 V for 0.5 h, linear increase to 2000 V in 1 h, and 2000 V for 3 h until a total focusing time of about 7300 V-h was reached. For the larger 24-cm strips, the following voltage profile was used: linear increase from 0 to 300 V in 1 min, 300 V for 3 h, linear increase to 3500 V in 5 h, and 3500 V until a total focusing time of about 70–80 kV-h was reached. For the 24-cm-long pH 3–11 non-linear IPG strips (e.g. supplemental Table T1, exp3), the samples were prepared in 150 µl of Rabilloud buffer as usual. The strips were rehydrated in 480 µl of DeStreak rehydration solution supplemented with 1% IPG buffer (GE Healthcare). The sample was then applied by anodic cup loading on an Ettan IPGphor 3 operated under the following voltage profile: direct start at a constant 150 V for 2 h (for the sample to enter the cup), constant 300 V for 2 h, constant 500 V for 2 h, linear increase to 1000 V in 4.5 h, linear increase to 10,000 V in 4.5 h, and constant 10,000 V for 2 h until a total focusing time of about 50,000 V-h. After electrofocusing, the IPG strips were finally equilibrated as described previously (32). The second dimension (SDS-PAGE) separation was performed on 12% polyacrylamide gels on a Dodeca cell system (Bio-Rad). Gel electrophoresis was started at 5–10 mA (for the proteins to enter the gel) and further run at a constant current of 18 mA at 15 °C until the bromphenol blue ran out. The gels were then immediately scanned for the fluorescein signal (excitation, 480 nm; emission, 520 nm; voltage, 400–500 V) on a Typhoon 9400 (Amersham Biosciences/GE Healthcare). The resulting image files were analyzed with ImageQuant (version 5, GE Healthcare) and eventually aligned with the TT900 module of the SameSpots software (version 2, Nonlinear Dynamics). The gels were eventually blotted on Invitrolon PVDF membranes (Invitrogen) for PEP detection (e.g. Fig. 5, A and B, insets) or directly stained and processed for spot picking as described hereafter.


Figure 5
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  		FIG. 5. In situ competition of PEP labeling detected by in-cell labeling with the FP-alkyne probe. A–D, 2D gel analyses of the CaCo2 enzymatic activities detected upon in-cell labeling of mock-treated cells (A) or of cells preincubated for 1.5 h with 5 µM Inhibitor A (B), 10 µM Inhibitor B (C), or 100 µM Inhibitor C (D) prior to in-cell labeling. Excerpts from Western blot analyses with the anti-PEP antibody confirming the presence of similar amounts of PEP are shown in insets in A and B. The spot trains corresponding to the PEP isoforms are circled. The spot train of the newly labeled enzymatic activity appearing upon preincubation with 100 µM Inhibitor C is indicated with an arrow in D.

 

Figure 6
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  		FIG. 6. In-lysate and in-cell IC50 determination for PEP Inhibitor A. 2D gel separation of in-cell (A) and in-lysate (B) competition of labeling experiments with the indicated concentrations of Inhibitor A is shown. Shown are excerpts of the 2D gels depicting the fluorescently labeled spots of PEP (spot train on the right side, circled in green) and of acylamino-acid-releasing enzyme (spot train on the left side). For accurate in-gel fluorescence quantification, see the recommendations in supplemental Fig. S6.

 

Figure 4
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  		FIG. 4. Comparison of in-cell and in-lysate labeling and corresponding labeled enzymes identified on the 2D gels. A, the labeling of CaCo2 enzymes was performed in lysate (lane 1, the cells were mock-treated with DMSO before consecutive lysis, labeling with the probe, and click chemistry detection) or in cell (the cells were incubated with the FP-alkyne probe (lane 4) or with the FP-fluorescein probe (lane 5) before proceeding to lysis (FP-fluorescein) or consecutive lysis and click chemistry detection (FP-alkyne)). Lane 2 shows the fluorescent background observed upon click chemistry when the FP-alkyne probe was omitted. Lane 3 represents the labeling achieved after successive in-cell labeling and in-lysate relabeling. The fluorescence intensities are averaged on five independently labeled enzymes and are normalized on the value quantified for lane 1. B and C, 150 µg of in-lysate (B, scanned fluorescein signal colored in red) or in-cell (C, scanned fluorescein signal colored in green) labeled CaCo2 samples were separated on 2D gels by consecutive electrofocusing on 7-cm-long, linear pH 4–7 strips followed by electrophoresis on a 12% polyacrylamide gel. D, overlaid picture of the two previous gels; the enzymes more than 10 times labeled in lysate or in cell only are circled in red and green, respectively. D, overlaid picture of the fluorescent scan of the in-cell labeled enzymatic activities (C; fluorescein signal colored in black here) and the corresponding total protein Coomassie Blue staining of the gel (in blue). The unambiguously identified labeled enzymes (reported in Table I) are directly annotated at their relevant position on the picture. ACPH, acylamino acid-releasing enzyme; ESTD, esterase D.

 
In-gel Protein Digestion and Sample Preparation—
For spot picking, the gels were fixed and stained with Coomassie Blue G-250 (32). The scanned picture of the Coomassie-stained gel was aligned and overlaid with the previous fluorescein scan with Photoshop CS (Adobe). The spots of the fluorescently labeled enzymes were located relative to defined Coomassie-stained references, excised, and digested in-gel with modified porcine trypsin (Promega) (31, 33). The resulting tryptic peptides were extracted with 5% formic acid, purified on ZipTips (Millipore) from which they were eluted with 2 µl of {alpha}-cyano-4-hydroxycinnamic acid matrix (5 mg/ml in 50% CH3CN, 0.1% TFA in 2 mM NH4H2PO4 containing two internal MS standards at 50 fmol/µl; see below), and directly spotted on an ABI 4700 MALDI 100-well plate by a Genesis ProTeam 150 system (Tecan).

Protein Identification by MALDI-TOF MS and MS/MS—
MALDI spots were analyzed using the Applied Biosystems 4700 Proteomics Analyzer (Applied Biosystems) in automated, combined MS and MS/MS mode. Both MS and MS/MS data were acquired with a neodymium-doped yttrium aluminium garnet (Nd:YAG) laser with a 200-Hz repetition rate; 2000 shots were accumulated for each spectrum in MS mode, and 5000 shots were accumulated for each of up to six selected precursor ions in MS/MS mode. Peak list-generating software used was 4000 Series Explorer (version 3.0 RC1 (November 28, 2004), Applied Biosystems). For peptide mass fingerprints, minimal signal-to-noise ratio (S/N) for peak detection was set to 12, local noise width was 50 m/z, and minimum peak width was one bin; resolution was set to 20,000 at m/z 1300; and only monoisotopic peaks were labeled. For MS/MS spectra, minimal S/N for peak detection was set to 4, local noise width was 50 m/z, and minimum peak width was one bin; resolution was set to 12,000 at m/z 2000; and only monoisotopic peaks were labeled with cluster area S/N optimization on and S/N threshold set to 4. MS spectra were in a first step calibrated externally with a standard mixture of five peptides (des-Arg1-bradykinin, [M + H]+ 904.468; angiotensin I, [M + H]+ 1296.685; [Glu1]fibrinopeptide B, [M + H]+ 1570.677; ACTH-(1–17), [M + H]+ 2093.086; and ACTH-(18–39), [M + H]+ 2465.198 Da) and in a second step internally with fragments of trypsin (108–115, [M + H]+ 842.509; and 58–77, [M + H]+ 2211.104 Da) and/or two reference peptides ([His32,Leu34]neuropeptide Y-(32–36), [M + H]+ 743.442; and ACTH-(18–39), [M + H]+ 2465.198 Da) admixed to the matrix (50 fmol/µl). For calibration peak detection and application minimal S/N was set to 25, mass tolerance was set to 0.8 Da, and minimum number of peaks to match was set to 3. For MS/MS, the most intense precursor ions with a signal-to-noise ratio >25 were selected after exclusion of common background signals (mass tolerance, ±0.2 Da) derived from a blank sample treated with trypsin. The mass range below m/z 900 was excluded because the respective peptides were in general too short for an unambiguous protein assignment. Adduct mass ranges excluded for MS/MS selection were +14, +22, –17, and –18 with a tolerance of ±0.03 Da. MS/MS mode was operated with 1 keV, and products of metastable decomposition at elevated laser power were detected. MS/MS data were calibrated using default instrument calibration, which was updated before measurement of sample sets.

Database Search—
Database searches (UniRef100 database, 3,926,270 sequences; taxonomy, human; 89,881 sequences; November 28, 2006) were performed using the Mascot search engine (version 1.9.05, Matrix Science) integrated in GPS Explorer (version 3.5; part of the ABI 4700 Proteomics Analyzer). With few exceptions (see supplemental Table T1), the search was restricted to human taxonomy because all samples were derived from human cell lines. Final search results were obtained by combining the GPS Explorer results of combined MS and MS/MS searches as well as results from individual MS/MS and peptide mass fingerprint searches. As enzyme, trypsin was specified with one missed cleavage allowed in MS and three missed cleavages allowed in MS/MS search. Mass tolerance was 20 ppm in MS search, and precursor mass tolerance was 40 ppm in MS/MS searches; fragment ion mass accuracy in MS/MS was set to 0.8 Da. In MS/MS search the mass range from 69 to 50 Da below each precursor mass was taken into account. For peptide mass fingerprint searches the masses of the internal calibrants and of trypsin fragments were excluded (see supplemental Table T3). Peak density was filtered to 50 peaks in a 200-Da window. Carbamidomethylation of cysteines was specified as a fixed modification, and oxidation of methionine and protein N-terminal acetylation were specified as variable modifications; oxidation of tryptophan and deamidation of asparagine were considered as additional variable modifications in MS/MS search only. With one exception (sample 70; protein-disulfide isomerase A4 precursor; best ion score, 95%), only proteins within a confidence interval ≥98% (protein score C.I. percent and/or best ion score C.I. percent) as provided from GPS Explorer software were considered as being primarily identified (see supplemental Table T1). Confidence intervals based on normal probability distribution mathematics provide "scores" that are independent of database size; they are listed for individual MS/MS hits (best ion score C.I. percent; supplemental Table T1), for combined MS/MS hits (total ion score C.I. percent; supplemental Table T1), and for combined peptide mass fingerprint (PMF) and MS/MS results (protein score C.I. percent; supplemental Table T1). For PMF results only, the protein score C.I. percent is the crucial value (see for example supplemental Table T1, sample 68, dipeptidyl-peptidase 3). For proteins with scores at the lower end, the final identification was based on manual verification of fragment assignment and a manual check for correct fragment ion intensity distributions in the relevant MS/MS spectra. This included for example a check for pronounced C-terminal cleavage at aspartic acid residues in arginine-containing peptide ions, examination of preferred N-terminal cleavage at proline especially for lysine-terminated peptide ions, and a check for appropriate neutral losses (e.g. loss of methanesulfenic acid from peptide ions containing oxidized methionine). Furthermore charge retention at the most basic fragments was taken as an additional indication for correct assignment of N- or C-terminal ion series. In cases where peptides matched to similar protein sequences, the top scoring proteins are reported (see supplemental Table T1). In the case of protein groups, the accession numbers of all group members are listed. For each protein identified, the number of unique peptides together with their sequences and scores are compiled in supplemental Table T1. For single peptide-based identifications, detailed data are listed in supplemental Table T2, including sequence, precursor ion mass, and scoring data. The corresponding MS/MS spectra were plotted (see Single peptide-based identifications-MS/MS spectra section in the supplemental data). For the identification based on peptide mass fingerprint only, the corresponding annotated MS spectrum together with other relevant data is also included in the supplemental data.


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS AND DISCUSSION
REFERENCES
 
Click Chemistry Serine Hydrolase Probe: Design, Preparation, and Labeling Characterization—
To achieve specific and efficient labeling of serine hydrolases in intact cells, a novel activity-based probe was designed, combining an FP-reactive group with a polyethylene glycol linker and an alkyne functionality (FP-alkyne (1), Fig. 1A). The presence of the alkyne functionality could then be detected in a later step upon a Cu(I)-catalyzed Huisgen [3 + 2] cycloaddition ("click chemistry") reaction (34) with an azide-functionalized fluorescent group (azide-fluorescein (3), Fig. 1B) as described elsewhere (17, 18, 20, 35). The choice to introduce the alkyne rather than the azide functionality on the probe was based on several chemical considerations: although azide-functionalized molecules seem compatible with in vivo labeling conditions (17, 18, 36–38), the chemical propensity of azides to undergo reduction into amines might be of concern. This is especially true if a trypsin digestion (requiring preliminary denaturation, reduction, and alkylation steps) of the labeled proteome fraction prior to the click chemistry reaction is envisaged. Moreover a previous extensive activity-based proteomics study has already compared both alternatives for a phenyl sulfonate ester probe (18). A significantly lower level of background labeling (resulting in an improved signal-to-noise ratio and in increased detection sensitivity) was obtained when the alkyne probe was used in combination with an azide fluorescent reporter. This observation was also recently supported by others (39).


Figure 1
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  		FIG. 1. Design and labeling mechanism of the fluorophosphonate probes used in this study. A, chemical structures of the FP-alkyne (1) and FP-fluorescein (2) serine hydrolase probes (the synthesis schemes, preparation protocols, and characterizations of the FP-alkyne (1) and azide-fluorescein (3) are provided in the supplemental data). B, labeling mechanism of the FP-alkyne on the catalytic serine residue of the active site of serine hydrolases; the FP-alkyne-labeled enzymes are detected in a second stage upon specific click chemistry reaction of the alkyne functionality with the azide-fluorescein (3) reporter molecule in the presence of CuSO4, TCEP as reducing agent, and tris(triazolyl)amine ("ligand") (29).

 
To compare the labeling efficiency of this novel click chemistry serine hydrolase probe we also prepared a previously characterized fluorescein-derivatized fluorophosphonate probe (FP-fluorescein (2), Fig. 1B), which is claimed to present superior labeling properties than the originally reported biotinylated fluorophosphonate probes (25). It is noteworthy that the chemical synthesis of the FP-alkyne probe turned out to be much more challenging than that of the FP-fluorescein. The former readily hydrolyzed in less than 2 days even when kept under its neat form at –20 °C, whereas the latter remained perfectly stable under the same conditions (data not shown). This instability issue was solved by immediately diluting the freshly prepared FP-alkyne probe with the proper amount of anhydrous DMSO to yield a 100 mM stock solution that was kept frozen at –20 °C. Although no obvious chemical explanation can account for this enhanced hydrolysis rate, it is tempting to speculate that the fluorescein moiety present in the FP-fluorescein probe might refold on the fluorophosphonate functionality, thereby preventing excessive hydrolysis in aqueous solution.

A comparison of the labeling efficiencies achieved by the two probes was performed on recombinant proteins (dipeptidyl-peptidases 4, 8, and 9 and prolyl endopeptidase) as well as on CaCo2 cell lysate (Fig. 2A). Although the detection of the enzyme activity with the FP-alkyne requires a two-step reaction (Fig. 1B), namely consecutive labeling of the catalytic serine residue of the enzyme and click chemistry coupling reaction to introduce the fluorescein reporter group, the overall labeling efficiency with the FP-alkyne probe was 5- to over 30-fold higher for all samples tested (Fig. 2A) even though the FP-alkyne probe was used in lower concentrations than the FP-fluorescein (10 versus 50 µM, respectively, yielding optimal labeling under our conditions). Those enhanced labeling capabilities could be attributed either to the smaller size of the FP-alkyne probe, which might result in increased accessibility to the labeling sites, or to an overall higher reactivity of the probe, which correlates with the higher hydrolysis tendency observed in aqueous solutions as mentioned above. Moreover the absence of reporter group on the FP-alkyne probe might prevent unspecific hydrophobic protein binding that could potentially occur with the large fluorescein moiety of the FP-fluorescein probe. Additional experiments confirmed that the FP-alkyne probe labeled only the active form of KLK7 but not the non-processed inactive zymogen (Fig. 2B) or the active enzyme preincubated with a reversible inhibitor (supplemental Fig. S1).


Figure 2
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  		FIG. 2. Comparison of the labeling efficiencies of the fluorophosphonate probes toward recombinant enzymes and CaCo2 cell lysate. A, the labeling of recombinant DPP4 (0.4 µM), DPP8 (0.2 µM), DPP9 (0.6 µM), and PEP (10 µM) as well as CaCo2 whole lysate (1 mg/ml) was performed with 10 µM FP-alkyne followed by click chemistry reaction or with 50 µM FP-fluorescein. B, the labeling of recombinant KLK7 (0.4 µM) before (pro-KLK7) and after (KLK7-activated) proteolytic activation with thermolysin was performed with 10 µM FP-alkyne followed by click chemistry reaction or with 50 µM FP-fluorescein. The fluorescence intensities of the labeled enzymes were calculated relative to the FP-fluorescein labeling of each enzyme separately. a, the fluorescence signal of PEP labeled with FP-alkyne was saturated; therefore the underestimated ratio is indicated. b, the fluorescence intensities of five independent CaCo2 enzymes were compared upon labeling with the FP-fluorescein or with the FP-alkyne; therefore the range of fluorescence intensity ratios is indicated.

 
Finally an extensive characterization of the labeling by the FP-alkyne probe was attempted by MS analysis. At the intact, full-length protein level, the deconvoluted nano-ESI profile revealed the expected molecular weight for the labeled KLK7 with one unique FP-alkyne probe molecule reacted per KLK7 protein (Fig. 3). By comparing ratios of the peaks of the modified versus non-modified enzymes, the labeling efficiency of KLK7 by the FP-alkyne probe could be estimated to reach 80%. A tryptic digest of the labeled KLK7 enzyme was also analyzed by nano-LC-MS/MS and revealed that only the active site peptide of KLK7 was specifically labeled with the FP-alkyne probe (see the Mascot report, supplemental Fig. S2). A detailed analysis of the fragmentation patterns of the labeled peptide was performed by direct nano-ESI-MS/MS and confirmed that the modification occurred on the catalytic serine of the active site (supplemental Fig. S3). It is worth mentioning that the signal of the labeled active site peptide could never be detected after click chemistry reaction with the azide-fluorescein (data not shown), although it is not clear whether those discrepancies may be attributed to a partially complete click chemistry reaction (supplemental Fig. S4) or to intrinsically poorer ionization properties of the labeled peptide due to the presence of the negatively charged fluorescein moiety. Those MS characterizations validated the specific labeling properties of the FP-alkyne probe toward the active site serine residue of KLK7. Altogether these labeling results demonstrate the value of the FP-alkyne probe to detect, with high sensitivity, the presence of active serine hydrolases by performing covalent labeling of the accessible site of those enzymes.


Figure 3
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  		FIG. 3. Deconvoluted nano-ESI MS spectrum of recombinant kallikrein 7 labeled with the FP-alkyne probe. The mass of the labeled recombinant KLK7 enzyme matches the addition of one FP-alkyne probe molecule per protein (24,436 + 282 – 19 – 1 = 24,698 Da; see Fig. 1B). The ratio of the peak areas suggests a labeling yield of about 80%. cps, counts/s; MCA, multichannel analysis.

 
In-cell and In-lysate Labeling of Serine Hydrolases—
Next we compared the ability of the FP-alkyne and the FP-fluorescein probes to perform in situ labeling of enzymes within living CaCo2 cells. The CaCo2 colon adenocarcinoma cell line was selected because it is a well established model system to study transport (absorption/efflux) and pharmacokinetics of drugs in development (40, 41). Analysis by 1D gel electrophoresis of the CaCo2 proteomes labeled in cell or in lysate with the FP-alkyne probe (Fig. 4A) indicated that both in-lysate labeling and in-cell labeling were comparable in terms of quantitative (as indicated by the similar fluorescence intensities of lanes 4 and 1) and qualitative detection of enzymatic activity patterns (similar 9–10 bands of labeled enzymes detectable both in lanes 4 and 1). On the contrary, the FP-fluorescein probe could not cross the cell membrane and could not achieve in situ labeling of cytoplasmic enzymes even at the highest concentrations tested (Fig. 4A, lane 5). To assess the efficiency of the in situ labeling with FP-alkyne, a lysate fraction from the in-cell labeling was reincubated with the probe prior to the click chemistry reaction (Fig. 4A, lane 3). This relabeling experiment yielded only a marginal increase of enzyme labeling (Fig. 4A, compare lanes 3 and 4), suggesting that in-cell labeling with the FP-alkyne probe had already almost reached completion under the conditions used here. Kinetic characterization of in-cell labeling showed that the in situ labeling was complete in less than 1 h of incubation with an optimal concentration of 10 µM FP-alkyne added to the cell supernatant (data not shown). Finally it is worth mentioning that the labeling of most of the enzymes could be efficiently competed with PMSF (data not shown), a known broad spectrum serine protease inhibitor, indicating again the specificity of the FP-alkyne labeling at the active site of those enzymes. Altogether these results demonstrate that the FP-alkyne probe is cell-permeable and achieved in situ labeling of the accessible active sites of cellular enzymes with almost the same efficiency as that obtained in lysate.

To gain better resolution in the separation of the labeled enzymes, the in-lysate (Fig. 4B, fluorescein signal colored in red) and in-cell (Fig. 4C, fluorescein signal colored in green) labeled CaCo2 samples were analyzed by 2D gel electrophoresis: the overlaid picture (Fig. 4D) confirmed a very similar quantitative labeling of the enzymes that is independent of whether the labeling occurred in situ or after cell disruption. Interestingly some enzyme activities were only detectable upon in-cell labeling (Fig. 4D, green circles), suggesting that those enzymes either lost their active conformation or were placed in the presence of endogenous inhibitors during the lysis process as happens for the calpain-calpastatin complex upon purification (42) for instance. Similar discrepancies in detection of enzymatic activities have also been reported by former activity-based proteomics studies, such as for cathepsin L whose labeling was lost upon lysate preparation (43, 44). Conversely the labeling of some other enzymes occurred exclusively in the lysate fraction and not in the intact cells (Fig. 4D, red circles). This appearance/recovery of enzyme activity in the lysate fractions might result from the de novo proteolytic processing of zymogens upon mixing of proteases of separate cellular compartments or from the disruption of the enzyme-endogenous inhibitor complexes, which were associated within living cells, upon dilution during the lysis process. Altogether these results emphasize the biological relevance of the in-cell labeling that provides the most authentic depiction of enzymatic activities present within living cells. This novel cell-permeable FP-alkyne probe represents, in this respect, a clear improvement over the previously reported serine hydrolase probes.

MALDI-TOF MS identification was performed on the in-cell and in-lysate labeled CaCo2 enzymes resolved by 2D gel electrophoresis and yielded a list of proteins that is provided in supplemental Table T1. It is worth mentioning that the identification confidence was higher compared with direct nano-LC-MS-based analysis because of the fact that the proteomes were separated by 2D electrophoresis and that the spots of the labeled enzymes were physically picked from the 2D gels. The hits were thus further validated by the correlation of the expected molecular weight and pI with the observed values according to the 2D gel spot migration. Therefore, even proteins identified from a single peptide hit confirmed by MS/MS were considered as reliably identified candidates. Nevertheless because no probe-based enrichment process was performed prior to 2D gel electrophoresis and MS identification, it is likely that some of the identified proteins do not represent actual covalent adducts with the probe. Rather they may represent abundant proteins that co-migrate with the actual FP-alkyne-labeled targets (as can be foreseen by the overall intensity of the Coomassie Blue protein staining overlaid on the fluorescence scan of the 2D gel in Fig. 4E). As a typical example, the identification of the enzyme spots, circled in red (Fig. 4, D and E), that were specifically labeled in the lysate was hampered by the presence of non-relevant co-migrating highly abundant proteins, e.g. 14-3-3 protein {varepsilon} or glutathione S-transferase Omega 1; see supplemental Table T1. Therefore, we elected to restrict our analysis of potential targets (listed in Table I) to enzymes clearly belonging to the serine hydrolase class and to enzymes already identified by former activity-based proteomics studies using other FP probes on rodent (13, 16, 24, 26) or human (7, 27, 45) cell lysates or tissue extracts. This validates the use of our novel FP-alkyne probe for the in situ detection of serine hydrolase activities in living cells. The identification of the same enzymes across various human cell lines (breast (7, 27), prostate (45), and colon (our data)), suggests also that those enzymes might be ubiquitously expressed and should therefore be regarded as potentially critical off-targets when assessing the specificity of inhibitors.


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  		TABLE I Selection of in-cell labeled CaCo2 enzymes identified by 2D gel and MALDI-TOF MS analysis

The identified proteins are sorted as serine hydrolases and other enzymes with FP reactivity formerly reported with their corresponding Swiss-Prot accession numbers and MEROPS identifiers (49). The number of peptides assigned by MS/MS analysis in each independently picked gel spot is indicated in the MSMS column (e.g. six and four peptides of PEP (isoforms) were identified, respectively, from two discrete spots among the train of PEP spots seen in Figs. 4 and 5). Provided are also the published references when the activity of the protein (or homolog ("h.")) has already been detected in previous labeling studies using fluorophosphonate probes, the calculated (Calc) and observed (Obs) molecular mass and pI of the picked enzymes. The complete list of the proteins, identified from the spots picked from the 2D gels, with the detailed sequences, masses, and scores of their unique tryptic peptides identified by MS/MS is provided in supplemental Table T1. The detailed data for the protein identifications based on single peptides are listed in supplemental Table T2; the corresponding MS/MS spectra can be found in the Single peptide-based identifications-MS/MS spectra section in the supplemental data. For the identification based on PMF only, the corresponding annotated MS spectrum together with other relevant data is provided in the PMF-based identification-MS spectrum section in the supplemental data.

 
Although the number of identifications, 14 validated formerly cross-referenced enzymes with FP reactivity (Table I) and more than 30 potentially new labeled proteins (highlighted in red in supplemental Table T1), lies in the range of what had been obtained so far (between seven identifications by gel (13) and 57 identifications by MS-based multidimensional protein identification technology (27)), only three validated serine proteases were identified in this study. Several factors could account for this low identification rate. First, the total number of serine hydrolases encoded by the human genome may greatly exceed the number of serine proteases (46). Second, the potentially deleterious digesting properties of proteases might imply both an overall lower expression of those enzymes compared with hydrolases as well as a tighter regulation of their activities. Third, a large number of serine proteases are secreted or membrane-associated. Because these subcellular fractions were not analyzed in this study, this could explain the biased identification rate toward the serine hydrolases versus proteases. This latter shortcoming is in part due to inherent limitations of 2D gel electrophoresis, which remains unpractical for the analysis of large hydrophobic proteins (membrane proteins typically). Also the recourse to 2D gel may have restricted our protease identification capacities in terms of sensitivity. Indeed and despite the higher sensitivity achievable by fluorescence labeling of the enzymes compared with total protein staining methods (Coomassie Blue or silver staining), the limited dynamic range of the fluorescence scanner seems to have restricted our detection to the labeled enzymes of high abundance only (about 50 spots were clearly and reproducibly identifiable on the 2D gels).

Although switching to an enrichment process and/or to a gel-free approach would probably increase the number of identified labeled enzymes, 2D gel electrophoresis remains a powerful separation technique to monitor the extent of labeling of the detected enzymatic activities even in these highly complex proteome samples. In particular, in-gel fluorescence readout provides a direct, rapid, and accurate quantification of the labeling of enzymes, as will be shown hereafter, especially when those enzymes are expressed in multiple isoforms, which might be problematic to resolve individually by an MS-based method.

In-cell and In-lysate Selectivity Profiling of Inhibitors of Serine Proteases (e.g. PEP)—
Among the identified proteins, the prolyl endopeptidase serine protease (Swiss-Prot accession number P48147) was selected for a proof of concept experiment of in-cell selectivity profiling by activity-based proteomics. Although its exact physiological role remains unclear, its ability to process bioactive neuropeptides in vitro suggests that PEP may be involved in cognitive functions and neurological disorders (for reviews, see Refs. 47 and 48). First, the presence of PEP in the CaCo2 cells was confirmed by Western blotting the 2D gels (Fig. 5A and B, insets). Second, an in situ competition of labeling was attempted by preincubating the CaCo2 cells with a nanomolar in-house reversible inhibitor of PEP (Inhibitor A; in vitro IC50 = 3 nM) prior to in-cell labeling with the FP-alkyne probe (Fig. 5B). The experiments resulted in complete disappearance of the fluorescent signals of the labeled PEP enzyme (Fig. 5, A and B, compare the circled spots of PEP), demonstrating (i) that labeling of PEP with the FP-alkyne probe could be efficiently competed by the inhibitor even in the presence of a saturating excess of probe compared with the inhibitor (e.g. 10 µM FP-alkyne versus 50 nM Inhibitor A; Fig. 6A) and (ii) that the inhibitor could thus penetrate the cell membrane to achieve potent in situ inhibition of PEP activity.

To validate the use of activity-based proteomics for in situ quantification of inhibitor potency (IC50), a similar competition of labeling was achieved with seven different concentrations of the inhibitor either (i) on lysates by preincubating CaCo2 cell lysate fractions with the inhibitor prior to the labeling or (ii) in situ upon preincubation of the CaCo2 cells with the inhibitor before performing in-cell labeling and lysis. Each tested condition required separate migration on as many 2D gels and resulted in competition of the in-gel fluorescence of the inhibited enzyme (e.g. Fig. 6, circled spots of PEP). In-gel fluorescence quantification of those labeled spots led to accurate determination of percentage of inhibition, which was plotted as classical dose-response curves and resulted in the following IC50 values (Fig. 6): 4 and 30 nM for in-cell and in-lysate IC50, respectively (average of two independent IC50 determinations). It should be stressed that the IC50 determined by competition of the in-cell labeling of enzymes integrates parameters such as cell permeability of the inhibitor, compartmental distribution, local endogenous target/off-target enzyme concentration, and cellular metabolic regulation mechanisms (detoxification/efflux). These factors are totally overlooked by in-lysate or in vitro IC50 determination, although they critically affect the local effective concentration of the inhibitor in vivo and may result in shifts in potencies if the inhibitor is co-localized with its primary target or with off-targets.

Here the 2D gel uncovers its potential by the possibility to pinpoint the multiple isoforms of the labeled enzymes. One could imagine the case of an inhibitor that would compete for the labeling of a specific isoform of an enzyme and not the other ones. Such an isoform-specific inhibition would be very difficult if not impossible to study by regular MS-based analysis if that specific peptide carrying the modification (e.g. phosphorylation or glycosylation) could not be identified by MS.

Our data also indicate that IC50 values of reversible inhibitors can be determined even under saturating labeling conditions. Indeed competition experiments performed at various time points prior to and after labeling completion did not result in a significant variation of the IC50 estimation (supplemental Fig. S5). Although this result might sound counterintuitive considering the potential displacement of the reversible inhibitor-enzyme equilibrium expected upon continuous covalent trapping of the free enzyme fraction by the probe as noted elsewhere (13, 16), several explanations could account for this. First, the fluorophosphonate probe labels kinetically the enzymes in a "reactivity-driven" manner (Fig. 1B), but it does not possess any significant intrinsic binding capabilities, and thus it cannot displace bound inhibitors from the enzyme. Second, it seems that in the case of potent (e.g. nanomolar) inhibitors, presenting a high binding affinity and a low dissociation constant (Ki), the interval under which the inhibitor could quit the inhibitor-enzyme complex and leave de novo free active enzyme in solution would not allow enough time for the probe to access the enzyme and perform active site labeling. Considering that different enzymes are labeled with different kinetics (16) and to optimize the signal-to-noise ratio for the IC50 estimation, we suggest to perform the selectivity profiling experiments under complete labeling conditions when the Ki of the inhibitor is the only variable that would then directly correlate to the observed competition of labeling with the probe. In all cases, even taking into account a potential inhibitor-enzyme equilibrium displacement, the determined IC50 value would be rather overestimated (i.e. appearing worse than it is in reality) but would still be of significance to compare relative in situ inhibition on the same target.

To confirm that our in-cell selectivity profiling methodology is indeed of general use, the same experiments were repeated with less potent (micromolar) inhibitors of PEP (Inhibitor B (in vitro IC50 = 1.9 µM) and Inhibitor C (in vitro IC50 = 1.2 µM)). Although the competition of labeling with these two inhibitors did not reach absolute completion at the inhibitor concentrations tested (Fig. 5, C and D, circled spots for PEP), the decrease of PEP labeling was reproducible and could be quantified to yield accurate IC50 determinations (1.4 and 7 µM for Inhibitor B and Inhibitor C, respectively; average of two independent IC50 determinations; supplemental Fig. S6).

Interestingly although we were specifically looking for the disappearance of spots to assess the specificity of those inhibitors, to our surprise, a newly labeled train of spots suddenly appeared upon incubation of 100 µM Inhibitor C (Fig. 5D, spots indicated with an arrow). This de novo appearance of enzymatic activity was completely unexpected because inhibitors are supposed to compete for the labeling of off-target enzymes. This effect was exclusively observed upon competition of the in-cell labeling and could not be reproduced upon in-lysate competition of labeling (data not shown), implying that the "inhibitor" induced its peculiar activation effect through a pathway that is functional under in vivo conditions only. The detailed study of the mode of action of Inhibitor C falls beyond the scope of this study, but it will be of particular interest to determine whether this substance exhibits its effect by inhibiting an extra factor, which is involved otherwise in the usual down-regulation of the enzyme, or by inducing the expression (or activation) of that newly appearing active enzyme through a completely unanticipated pathway. Nevertheless this striking result highlights again the value of the FP-alkyne activity-based probe to assess in-cell selectivity profiling of inhibitors and paves new ways for understanding "side effects" of drugs that would have been otherwise completely overlooked by standard inhibition measurement by in vitro fluorescent assays.

In summary, we developed a novel activity-based probe that achieves labeling of serine hydrolases within complex proteomes and performs efficient in situ labeling of enzymatic activities within living cells. Because of the accuracy of 2D gel fluorescence quantification, this probe can be used to monitor in situ the relative activity levels of enzymes, and as a proof of principle, the potencies of several PEP inhibitors were compared in living CaCo2 cells and lysates. The possibility to monitor simultaneously individual activities of several members of an enzyme family in living cells opens new perspectives in the drug discovery process and offers the opportunity to assess the potency and selectivity of inhibitors toward entire families of related enzymes simultaneously in one single experiment under highly physiological conditions (on endogenous active enzymes at their relevant localization and concentration within their natural environment in intact cells). Finally our study uncovers unforeseen aspects of in-cell safety profiling by activity-based proteomics where an inhibitor will not only be profiled for off-target inhibition potency but will also be assessed for potential off-target activation, an effect that would be impossible to discover with other in vitro profiling methods.


   ACKNOWLEDGMENTS
 
We are grateful to François Gessier and Gary Ksander (Global Discovery Chemistry, Novartis) for the gift of the PEP inhibitors. We also thank Julian Woelcke, Jörg Eder, Aengus Mac Sweeney, Richard Sedrani, and Lorenz Mayr (Expertise Platform Proteases, Novartis) for critical reading of the manuscript as well as the other members of the Platform for the stimulating discussions about the project. Finally we acknowledge Prof. Christopher Overall for useful advice and comments throughout the course of this study.


  FOOTNOTES
 
Received, October 17, 2007, and in revised form, February 29, 2008.

Published, MCP Papers in Press, March 24, 2008, DOI 10.1074/mcp.M700505-MCP200

1 The abbreviations used are: ABP, activity-based probe; FP, fluorophosphonate; KLK7, kallikrein 7; PEP, prolyl endopeptidase; Z, benzyloxycarbonyl; AMC, 7-amino-4-methylcoumarin; DPBS, Dulbecco's PBS; TCEP, tris(carboxyethyl)phosphine; 1D, one-dimensional; 2D, two-dimensional; S/N, signal-to-noise ratio; ACTH, adrenocorticotropic hormone; C.I., confidence interval; PMF, peptide mass fingerprint; DPP, dipeptidyl-peptidase. Back

* This work was supported by the Novartis Institutes for BioMedical Research, Education Office, and Expertise Platform Proteases. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Back

S The on-line version of this article (available at http://www.mcponline.org) contains supplemental material. Back

|| To whom correspondence should be addressed: Center for Proteomic Chemistry/Expertise Platform Proteases, Novartis Insts. for BioMedical Research, Fabrikstrasse 16-2.72.2, CH-4002 Basel, Switzerland. Tel.: 41-61-6962485; Fax: 41-61-6968132; E-mail: shirley.gil_parrado{at}novartis.com


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