Superfamily-wide portrait of serine hydrolaseinhibition achieved by library-versus-library screeningDaniel A. Bachovchina,1, Tianyang Jia,1, Weiwei Lia,1, Gabriel M. Simona, Jacqueline L. Blankmana, Alexander Adibekiana,Heather Hooverb, Sherry Niessenb, and Benjamin F. Cravatta,2

aThe Skaggs Institute for Chemical Biology, bCenter for Physiological Proteomics, and Department of Chemical Physiology, The Scripps Research Institute,10550 North Torrey Pines Road, La Jolla, CA 92037

Edited by Kevan M. Shokat, University of California, San Francisco, CA, and approved October 4, 2010 (received for review August 5, 2010)

Serine hydrolases (SHs) are one of the largest and most diverse en-zyme classes in mammals. They play fundamental roles in virtuallyall physiological processes and are targeted by drugs to treat dis-eases such as diabetes, obesity, and neurodegenerative disorders.Despite this, we lack biological understanding for most of the 110þpredicted mammalian metabolic SHs, in large part because of adearth of assays to assess their biochemical activities and a lackof selective inhibitors to probe their function in living systems.We show here that the vast majority (>80%) of mammalian meta-bolic SHs can be labeled in proteomes by a single, active site-direc-ted fluorophosphonate probe. We exploit this universal activity-based assay in a library-versus-library format to screen 70þ SHsagainst 140þ structurally diverse carbamates. Lead inhibitors werediscovered for ∼40% of the screened enzymes, including manypoorly characterized SHs. Global profiles identified carbamate in-hibitors that discriminate among highly sequence-related SHsand, conversely, enzymes that share inhibitor sensitivity profilesdespite lacking sequence homology. These findings indicate thatsequence relatedness is not a strong predictor of shared pharma-cology within the SH superfamily. Finally, we show that lead car-bamate inhibitors can be optimized into pharmacological probesthat inactivate individual SHs with high specificity in vivo.

enzymology ∣ mass spectrometry ∣ profiling ∣ proteomics

Amajor challenge facing biological researchers in the 21st cen-tury is the functional characterization of the large number of

unannotated gene products identified by genome sequencingefforts (1). Many proteins partly or completely uncharacterizedwith respect to their biochemical activities belong to expansive,sequence-related families (2). Although such membership can in-form on the general mechanistic class to which a protein belongs(e.g., enzyme, receptor, or channel), it is insufficient to predictspecific biochemical and physiological functions, which requireknowledge of substrates, ligands, and interacting biomolecules.On the contrary, membership within a large protein family caneven present a barrier to achieving these goals by frustratingthe implementation of standard genetic and pharmacologicalmethods to probe protein function. For example, targeted genedisruption of one member of a protein superfamily may result incellular compensation from other family members.

Problems are also encountered when attempting to developspecific inhibitors and/or ligands for uncharacterized membersof large protein families, where at least two major experimentalissues must be addressed. First, there is an intrinsic difficultyfacing ligand discovery for uncharacterized proteins, which oftenlack the functional information required to develop high-qualityassays for compound screening. Creative solutions to this pro-blem have emerged for specific protein classes, such as G-proteincoupled receptors (GPCRs) (3) and kinases (4, 5), where genericassays have been developed that exploit conserved functionaland/or structural features displayed by members of each proteinfamily (e.g., G-protein coupling for GPCRs; ATP-binding sitesfor kinases). Whether such “universal” assay parameters existfor other protein superfamilies is unclear. Second, even with a

general screening assay in hand, achieving ligand selectivity forone member of a large protein family presents a major challenge.Methods are particularly needed to assess selectivity in native bio-logical systems, where proteins are regulated by posttranslationalmechanisms that may alter their activity and ligand affinity (6, 7).

Activity-based protein profiling (ABPP) (8) is a chemicalproteomic technology that addresses some of the aforementionedchallenges. ABPP employs active site-directed chemical probesthat covalently label large numbers of mechanistically related en-zymes in native biological systems. ABPP probes have so farbeen developed for more than a dozen enzyme families, includinghydrolases (9–14), kinases (15), histone deacetylases (16), andoxidoreductases (17, 18). When performed in a competitiveformat, where compounds are assayed for their ability to blockprobe labeling (10, 12, 19), ABPP offers a powerful means todiscover small-molecule inhibitors of enzymes that is indepen-dent of their degree of functional annotation. A key advantageof competitive ABPP is that it permits simultaneous optimizationof the potency and selectivity of inhibitors against numerousrelated enzymes directly in their native cellular environment. Thisstrategy has led to the identification of selective inhibitors formany enzymes (12, 19–25), including several uncharacterizedproteins (22–24).

Despite these advantages, competitive ABPP has not yet beendemonstrated to be feasible for screening the large majority ofenzymes from an expansive family against a small-moleculelibrary. Here, we address this problem by evaluating the perfor-mance of ABPP against the mammalian serine hydrolase (SH)superfamily. We show that >80% of the 110þ predicted metabolicSHs in mammals are targeted by a single fluorophosphonate (FP)activity-based probe. We employ this FP probe to perform alibrary-versus-library competitive ABPP screen, wherein 72 SHsare assayed against a collection of ∼140 carbamate small mole-cules. From this screen, lead inhibitors were discovered for morethan 30 SHs, including several uncharacterized enzymes. Impor-tantly, we show that competitive ABPP can be used to identifyinhibitors that discriminate among highly sequence-relatedhydrolases and direct the rapid optimization of these inhibitorsinto pharmacological probes that selectively inactivate individualSHs in living animals.

ResultsGlobal Cell and Tissue Profilingwith a FPActivity-Based Probe.HumanSHs can be divided into two near-equal-sized subfamilies—the

Author contributions: D.A.B., W.L., G.M.S., and B.F.C. designed research; D.A.B., T.J., W.L.,J.L.B., and H.H. performed research; D.A.B., W.L., and A.A. contributed new reagents/analytic tools; D.A.B., W.L., S.N., and B.F.C. analyzed data; and D.A.B. and B.F.C. wrotethe paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.1D.A.B., T.J., and W.L. contributed equally to this work.2To whom correspondence should be addressed. E-mail: cravatt@scripps.edu.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1011663107/-/DCSupplemental.

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trypsin/chymotrypsin class of serine proteases (∼125 human mem-bers) and the metabolic SHs (∼115 human members) (26).The latter set of enzymes includes a wide range of structurally di-verse peptidases, lipases, esterases, thioesterases, and amidases.Although FP probes (SI Appendix, Fig. S1) have been shown tolabel both serine proteases and metabolic SHs (10, 27, 28), weelected to focus our analysis on metabolic SHs, because serine pro-teases are often produced as inactive precursors (zymogens) andtherefore are difficult to assay in heterologous expression systems.

As has been recently reviewed (26), metabolic SHs play keyroles in diverse (patho)physiological processes, and several ofthese enzymes are targeted by clinically approved drugs, includ-ing acetylcholinesterase (AChE) for Alzheimer’s disease (29),pancreatic lipases for obesity (30), and dipeptidylpeptidase IV fordiabetes (31). Despite the importance of metabolic SHs inmammalian physiology, nearly half of these enzymes remain with-out any assigned biochemical activity or substrate (26). Selectiveinhibitors are also lacking for the vast majority (>80%) of mam-malian metabolic SHs, further complicating their functionalcharacterization. Previous studies have showcased the value of FPprobes for ABPP of SHs in proteomes (27, 28, 32) and for thediscovery of inhibitors for members of this enzyme family (19,22, 23, 25). Whether competitive ABPP can serve as a universalassay for SH inhibitor discovery depends, however, on the frac-tion of mammalian SHs that can be profiled by FP probes.

We set out to determine the full complement of mammalianmetabolic SHs targeted by FP probes by using a panel of mouse

cell and tissue proteomes that showed diverse labeling patternswith a fluorescent FP (FP-rhodamine) as judged by 1D-SDS-PAGE (Fig. 1A and SI Appendix, Fig. S2). We identified the SHtargets of FP probes in these tissues by using the MS platformABPP-MudPIT (28). Briefly, proteomes were incubated with abiotinylated FP probe [FP-biotin (9)], and FP-labeled enzymeswere enriched with avidin beads, digested with trypsin, and ana-lyzed by multidimensional liquid chromatography (LC)-MS/MS.Probe-labeled SHs were defined as those that showed (i) an aver-age of ≥5 spectral counts in FP-treated proteomes and (ii) >5-fold more spectral counts in probe-treated versus “no probe” con-trol samples. In total, 101 FP-labeled SHs were identified inmouse cells and tissues (SI Appendix, Table S1), and this numberwas further increased to 105 by evaluating FP labeling for recom-binantly expressed versions of SHs that show low signals in tissues(1 < average spectral counts < 5). Several SHs showed tissue-restricted patterns of probe labeling consistent with their knownexpression profiles and functions [e.g., pancreatic lipases (PNLIP,PNLIPRP1, and PNLIPRP2) were found exclusively in the pan-creas; hepatic lipase (LIPC) was most strongly labeled in theliver] (Fig. 1B). The 105 FP-labeled SHs number accounts for82% of the 128 predicted metabolic SHs in mice (Fig. 1C). Weconclude from these data that a FP activity-based probe can serveas a near-universal assay to characterize mammalian SHs in pro-teomes. We next sought to adapt this assay for screening a libraryof small-molecule inhibitors against the SH superfamily.

Fig. 1. Determining the full complement of mammalian metabolic SHs targeted by FP activity-based probes. (A) A panel of mouse tissue proteomes (1 mg ofprotein per mL) was labeled with FP-rhodamine (2 μM, 45 min) and proteomes analyzed by 1D-SDS-PAGE and in-gel fluorescence scanning. Representativefluorescent gel of FP-rhodamine-labeling events shown in gray scale. (B) Hierarchical cluster analysis of SH activity signals identified in mouse tissues byABPP-MudPIT. Data are presented as the average spectral counts from three independent experiments normalized for each SH to the tissue containingthe most spectral counts for that enzyme. (C) A dendrogram showing all 128 members of the mouse metabolic SH family with branch length depicting se-quence relatedness. This analysis includes two additional human SHs, FAAH2 and PNPLA4, that lack mouse orthologues. SHs that were labeled by FP activity-based probes are shown in red (105 enzymes or 82% of the metabolic SH family). cm, conditioned media; LPS, cells treated with 10 μg∕mL lipopolysaccharidefor 24 h; RAW, RAW264.7 mouse macrophage cell line.

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A Library-Versus-Library Format for Competitive ABPP. A typical for-mat for competitive ABPP involves incubation of a proteomewith a small molecule, followed by labeling of the sample witha fluorescent activity-based probe, separating proteins by SDS-PAGE, and quantifying the fluorescence intensity of proteinband(s) on the gel relative to a control (DMSO-treated) proteome(19, 23). Although this type of competitive ABPP experiment canbe performed in native cell and tissue proteomes, the large differ-ences in endogenous expression levels of SHs, combined withtheir tendency to cluster in certain mass ranges (25–35 and55–65 kDa), means that only a fraction of SHs can be resolvedby 1D-SDS-PAGE in native proteomes. Complementary MS-based platforms, such as ABPP-MudPIT, have been introducedto address these problems (23, 25); however, such LC-MS meth-ods are too time consuming to permit screening of a library ofcompounds. We therefore adopted a different strategy whereineachmammalian SH is recombinantly expressed (e.g., by transienttransfection in eukaryotic cells) and then mass-resolvable subsetsof these enzymes are combined into groups of up to eight enzymesto create a multiplexed SH library for inhibitor screening by 1D-SDS-PAGE. Importantly, this library-versus-inhibitor library for-mat for competitive ABPP enabled, with only a handful of excep-tions, screening of enzymes directly in crude cell proteomes (i.e.,without requiring any enzyme purification).

We selected a total of 72 SHs for analysis, 66 of which wereassayed as recombinant proteins and six of which were more con-veniently obtained from endogenous sources (e.g., secreted SHsthat could be accessed from the conditioned media of cell lines)(SI Appendix, Table S2). These enzymes were selected to covermost branches of the metabolic SH family (SI Appendix, Fig. S3).Only six enzymes required an additional, single purification stepin order to visualize a band by FP-rhodamine labeling. Labelingwith FP-rhodamine confirmed the activity of recombinant SHsand maintenance of their gel-resolved signals in multiplexedgroups (Fig. 2A). We estimated that this assay format would per-mit the screening of more than 100 compounds against the entire72-enzyme panel within the time frame of 4–6 wk. Given this levelof throughput, we elected to screen a targeted library of candi-date inhibitors on the basis of the carbamate group, which cova-lently reacts with the conserved serine nucleophile of SHs to formhydrolytically stable enzyme adducts (Fig. 2B). Carbamates havebeen developed that show excellent selectivity for individual SHsand have proven valuable as research tools (22, 23, 25, 33) andtherapeutic drugs (29).

We synthesized a structurally diverse set of ∼140 carbamates(see SI Appendix for details) and screened these compounds at

50 μM against the 72-member SH panel. A compound was scoredas active against a given SH if it blocked >75% of FP-rhodaminelabeling. A representative profile for the SH FAAH2 is shown inFig. 2C. Primary competitive ABPP data for the other 71 SHs areshown in SI Appendix, Fig. S4, and a summary of the full library-versus-library dataset can be accessed at the Web site: http://www.scripps.edu/cgi-bin/cravatt/BachovchinJiLi2010. Carbamatehits were identified for 33 SHs (SI Appendix, Table S3), corre-sponding to ∼46% of the screened enzymes. Certain SHs, suchas the carboxylesterases (CESs), showed broad reactivity withthe carbamate library (SI Appendix, Fig. S4). This finding is con-sistent with previous studies designating CESs as common targetsfor a wide range of SH-directed inhibitors (19, 34, 35), whichlikely reflects the role that these enzymes play in xenobiotic me-tabolism (see SI Appendix for details). We also identified carba-mate inhibitors for a substantial fraction (36%) of non-CES SHs(Fig. 3A and SI Appendix, Table S3). Notably, several of thesecarbamates were found to selectively inactivate a single (non-CES) SH (Fig. 3A and Table 1). We used competitive ABPPto calculate IC50 values for a representative set of these inhibi-tors, which ranged from 0.008 to 5.3 μM (Table 1, and SIAppendix, Fig. S5). Other carbamates showed slightly broader re-activity with the SH family, inhibiting a handful of enzymes (threeto six) by >75% at 50 μM (Fig. 3A). These results indicate thatreasonably potent (nanomolar to low micromolar) and selectivelead inhibitors can be obtained for many SHs from amodest-sizedcollection of carbamate small molecules.

Several sequence-related clades of enzymes exist within the SHfamily (Fig. 1C), and we wondered whether these enzymes wouldshow equivalent inhibitor sensitivity profiles or, alternatively, ifcarbamates could discriminate among homologous enzymes.Our data strongly support the latter conclusion, because severalcarbamates were identified that selectively inactivate one mem-ber of a pair of nearest-sequence neighbor enzymes, includingFAAH-1/FAAH-2, AADACL1/AADAC, and PLA2G7/PAFAH2(Fig. 3 B–D). Interestingly, we also found cases where the mostpotent “off target” activity was not a homologous, but rather a verydistantly related SH. For instance, WWL38 inhibited AADACL1andACHEwith IC50 values of 4.8 and 13.3 μM, respectively, whileshowing no activity (IC50 > 100 μM) against AADAC (the near-est-sequence neighbor to AADACL1) (SI Appendix, Fig. S5).Such data indicate that sequence homology is not a particularlystrong predictor of shared inhibitor sensitivity profiles amongSHs, thus underscoring the importance of proteomic methods likeABPP that can uncover unanticipated pharmacological cross-points within enzyme superfamilies.

Fig. 2. A library-versus-library format for competi-tive ABPP. (A) SHs were expressed individually andassayed for activity in crude cell lysates by treatmentwith FP-Rh and analysis by gel-based ABPP. Gel-resol-vable SHs were combined and screened for inhibitionby the carbamate library; a representative example isshown for the carbamate URB597, which is a knowninhibitor of FAAH (33). (B) General structure of acarbamate library and mechanism of SH inactivationby carbamates. (C) Representative example of theprimary competitive ABPP screening data for the en-zyme FAAH2 expressed by transient transfection in293T cells. A mock-transfected proteome and FP-rhodamine signals from an endogenously expressedSH are shown for comparison. Unless otherwise indi-cated, each number on the horizontal axis refers to acarbamate (lacking the WWL prefix to conservespace). From this analysis, several hits were identified,including WWL44, which selectively inhibited FAAH2relative to other SHs with an IC50 value of 1.7 μM(Table 1). See SI Appendix, Fig. S4 for a completeset primary competitive ABPP data from our library-versus-library screen.

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Optimization of Carbamate Hits to Create in Vivo PharmacologicalProbes. Although some highly potent inhibitors were identifieddirectly from the carbamate library (e.g., WWL65, which was asub-10-nM inhibitor of FAAH), most of the carbamate hits exhib-ited inhibitory activities in the range of high-nanomolar to low-micromolar. We therefore asked whether these moderate-potency leads could be efficiently optimized into selective andefficacious inhibitors for in vivo pharmacological studies. To ad-dress this question, we selected WWL151 as a case study, whichselectively inhibited the uncharacterized SH ABHD11 with anIC50 value of 5.3 μM (Table 1). The most unusual feature ofWWL151 compared to other members of the carbamate librarywas the seven-membered azepane ring, which appeared to grantthis compound high selectivity for ABHD11 [compared, for ex-

ample, to the six-membered piperidine ring analogue WWL215(Table 2), which inhibited several additional SHs, includingFAAH-1, FAAH-2, and PLA2G7 (Fig. 4A and SI Appendix,Fig. S4)]. Because only a limited number of azepane analoguesare commercially available, we postulated that exchanging thisring system with a monosubstituted piperidine ring might offera more facile medicinal-chemistry strategy for improving potency,while, at the same time, preserving selectivity for ABHD11. Fromthis effort, we identified the 2-methyl substituted piperidyl carba-mate WWL219 (Table 2), which showed markedly improvedactivity against ABHD11 (IC50 ¼ 160 nM) but still inhibitedthe SH FAAH (Fig. 4 A and B). Extension of this 2-substitutionto an ethyl group to give WWL222 (Table 2) maintained potencyagainst ABHD11 (IC50 ¼ 170 nM) (Fig. 4C) while eliminatingactivity against other SHs (Fig. 4 A and B).

An attractive feature of carbamates is that these agents typicallyshow excellent pharmacological activity in vivo, including theability to penetrate the nervous system (25, 33, 36), where theirSH targets may play important roles in regulating neurochemicalsignaling pathways (37, 38). Consistent with this precedent,we found that administration of WWL222 to mice (10 mg∕kg,i.p., 4 h) completely inactivated brain ABHD11 as judged bycompetitive ABPP-MudPIT (Fig. 4D). Remarkably, none of theother ∼50 brain SHs detected in this proteomic analysis wereinhibited by WWL222. These data demonstrate that WWL222acts as a selective and efficacious inhibitor of ABHD11 in vivo.To confirm that other carbamate hits also showed in vivo activity,we treated mice with the ABHD6 inhibitor WWL123 (Table 1;5–20 mg∕kg, i.p., 4 h). Competitive ABPP profiles of brain tissuefrom WWL123-treated animals revealed selective inactivation ofABHD6 (SI Appendix, Fig. S6).

DiscussionComplete genome sequences promise to radically change thefield of molecular pharmacology. Systematic efforts to subcloneand express the full complement of protein-coding sequencesfrom mouse and human genomes are underway and should pro-vide straightforward access to “expression-ready” constructs forthese proteins (39, 40). The principal remaining challenge is thento develop general functional assays for mammalian proteins, aproblem that is compounded by the fact that many of these pro-teins are uncharacterized with respect to biochemical and cellularactivity. Here, we have shown that competitive ABPP offers anear-universal assay platform for the SH superfamily, which re-presents one of the largest and most diverse enzyme classes in

Fig. 3. Identification and characterization of lead inhibitors for SHs (A) Hierarchical cluster analysis of carbamate inhibition profiles for a representative subsetof SHs. From this analysis, compounds that inactivate several SHs (e.g., WWL98 andWWL202) can be readily discriminated from those that show high selectivityfor individual SHs (listed in Table 1 and B–D). (B–D) Concentration-dependent inhibition profiles for carbamates that show high selectivity for one member of apair of sequence-related enzymes. (B) FAAH-1 versus FAAH-2, (C) AADAC versus AADACL1, and (D) PLA2G7 versus PAFAH2. See SI Appendix, Fig. S5 for moreexpanded concentration-dependent inhibition curves used to generate the reported IC50 values.

Table 1. Selective lead inhibitors (and their SH targets) identified bylibrary-versus-library competitive ABPP (see SI Appendix, Fig. S5 forconcentration-dependent inhibition curves used to generatereported IC50 values)

Hydrolase Compound Structure IC50, μM

AADACL1 WWL57 0.36

ACHE WWL52 3.5

ABHD6 WWL123 0.43

ABHD11 WWL151 5.3

CEL WWL92 4.1

FAAH WWL65 0.008

FAAH2 WWL44 1.7

PLA2G7 WWL153 0.29

PNPLA8 WWL210 2.9

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mammals. We determined that more than 80% of the predictedmetabolic SHs in mice can be profiled in cell and tissue pro-teomes with a single FP activity-based probe. SHs that werenot detected by ABPP in this study could represent enzymes thatshow highly restricted tissue distributions [e.g., searches of theBioGPS database (http://biogps.gnf.org/) suggest that AADACL2and AADACL4 are exclusively expressed in mouse skin and re-tina, respectively, two tissues that were not profiled in this study]or an inability to recognize and react with the FP probe. We alsocannot exclude the possibility that some of the predicted SHgenes are in fact pseudogenes that do not produce a functionalprotein product. These factors, taken together, suggest that moreextensive tissue profiling, perhaps with structural analoguesof the prototype FP probe (10), should further enhance ABPPcoverage of the metabolic SH superfamily.

The library-versus-library format for competitive ABPP pre-sented herein offers a technically straightforward and reasonably

rapid way to screen the SH superfamily against hundreds of in-hibitors. Although this platform is not compatible with screeningof thousands of compounds, we should emphasize that the outputis comparable to such higher-throughput screens in terms ofaggregate data points (>11;000 for our library-versus-libraryanalysis) and information content. Because competitive ABPPtests each compound against numerous (70þ) SHs in parallel,a family-wide portrait of pharmacological activity is generatedfor each inhibitor that immediately informs on its potency andselectivity. We used this combined information to identify usefullead carbamate inhibitors for several SHs, including enzymes,such as ABHD11, CEL, and PNPLA8, for which no other selec-tive inhibitors have yet been described. In the case of ABHD11,we furthermore showed how structure-activity relationship datafrom the initial competitive ABPP screen could guide the rapidoptimization of a low-micromolar hit into a selective and effica-cious inhibitor, WWL222, that is suitable for in vivo pharmaco-logical studies. Like many other SH inhibitors, our carbamate hitsshowed some cross-reactivity with members of the CES subfam-ily. This cross-reactivity should not, however, hinder the useof carbamates to characterize SHs in a wide range of biologicalsystems, because CESs are mostly restricted in their expression tothe liver (see Fig. 1B and discussion in the SI Appendix).

ABHD11 is a poorly characterized SH that exhibits a broadtissue distribution, including high expression in the brain andheart (Fig. 1B). Proteomic studies have identified ABHD11 as amitochondrial protein (41), although its actual biochemical func-tion, including its endogenous substrates and products, remainsunknown. The ABHD11 gene is located in a region of chromo-some 7 (7q11.23) that is hemizygously deleted in Williams–Beu-ren syndrome, a rare genetic disease with symptoms that includevascular stenosis, mental retardation, and excessive sociability(42). Whether ABHD11 plays a role in Williams–Beuren syn-drome remains unclear. The inhibitor WWL222 should assistfuture investigations of ABHD11’s relevance to symptoms ob-served in Williams–Beuren syndrome, as well as to elucidatethe enzyme’s endogenous biochemical and cellular functions.

Projecting forward, it is worthwhile to consider the grandquestion—how long might it take to generate selective and invivo-active inhibitors for every member of the SH family by usinga near-universal, proteomic assay like competitive ABPP?Although our discovery of lead inhibitors for ∼46% of thescreened SHs (∼36% of the non-CES enzymes) is encouraging,we also note that several of these leads are not yet selective en-ough for use as pharmacological probes. It is possible that suchmultitarget carbamates can serve as medicinal-chemistry startingpoints for generating selective inhibitors of individual SHs [as hasbeen accomplished for multitarget kinase inhibitors (7) and as wehave previously shown forWWL98, which led to the developmentof the selective monoacylglycerol lipase (MGLL) inhibitorJZL184 (25)]. We also anticipate that some multitarget carba-mates may show greater selectivity for individual SHs when tested

Table 2. Structures of a carbamate sublibrary targeting ABHD11,including the optimized inhibitor WWL222

Compound n R1 R2

WWL151 3 H 4-NO2-PhWWL209 3 H PhWWL210 3 H 2-Cl-PhWWL211 3 H 2-OMe, 4-NO2-Ph

WWL214 3 H

WWL215 2 H 4-NO2-PhWWL216 1 H 4-NO2-PhWWL219 2 2-Me 4-NO2-PhWWL220 2 4-Me 4-NO2-PhWWL222 2 2-Et 4-NO2-PhWWL223 2 2;6-ðMeÞ2 4-NO2-Ph

WWL225 2 4-NO2-Ph

WWL226 2 3-Me 4-NO2-PhWWL227 2 2-CH2OH 4-NO2-Ph

WWL228 2 4-NO2-Ph

WWL229 2 4-NO2-Ph

WWL230 2 4-NO2-Ph

WWL231 2 4-NO2-Ph

WWL232 2 4-NO2-Ph

Fig. 4. Development of a selective and in vivo-active inhi-bitor of ABHD11. (A) Competitive ABPP signals forWWL151 and structural analogues (5 μM) against ABHD11and the common off-targets for this compound scaffold—FAAH, MGLL, ABHD6, and PNPLA8. (B) Cluster analysis ofthe competitive ABPP data shown in A, designatingWWL222 as a potent and selective ABHD11 inhibitor. (C)Concentration-dependent inhibition curve for WWL222against ABHD11. From this curve, an IC50 value of170 nM was calculated. Data are presented as means�standard error of the mean (SEM); n ¼ 3∕group. (D)ABPP-MudPIT analysis of SHs from the brain proteomesof mice treated with vehicle or WWL222 (10 mgkg−1,i.p., 4 h); Among the ∼50 SHs detected in this analysis, onlyABHD11 was inhibited by WWL222 (*p < 0.02). Data arepresented as means� SEM; n ¼ 3∕group.

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at lower concentrations. As an initial assessment of this postulate,we measured IC50 values of 0.05, 1.57, and 2.75 μM for WWL110versus BCHE, ABHD2, and CEL, respectively (SI Appendix,Fig. S5), indicating that this agent is a relatively selective inhibitorof BCHE but also potent enough to act as a lead for inhibitors ofABHD2 and CEL. Beyond this line of future research, achievingcomplete pharmacological coverage of the SH family will likelyrequire screening either a much-expanded carbamate library oradditional structural classes of compounds. Carbamates are quitestraightforward from a synthetic perspective, and it is certainlypossible to consider producing a larger (1;000þ-member) libraryof these agents. One could also create additional small-moleculelibraries on the basis of other chemotypes that mechanisticallyinhibit SHs, such as phosphonates (43), electrophilic ketones(19), and lactones (30) or lactams (44). As these compoundlibraries grow in size, they will eventually exceed the capacityof gel-based competitive ABPP. We have recently introducedone potential solution to this problem, a fluorescence polarizationplatform for ABPP that is compatible with ultrahigh-throughputscreening (24).

Finally, we anticipate that the knowledge gained from ourcompetitive ABPP studies with SHs can apply to genome-widepharmacological studies of other enzyme families for which activ-ity-based probes have been developed, including kinases (15),oxidoreductases (17, 18), and additional classes of hydrolases(12–14). In this way, future medicinal-chemistry pursuits may takeon a decidedly proteomic flavor, such that projects focused on asingle enzyme give way tomore target-agnostic, family-wide inves-

tigations of small molecule–protein interactions. The resultingpharmacopeia should greatly enhance our understanding of pro-tein function in mammalian physiology and disease.

Materials and MethodsGlobal Mouse Cell and Tissue Profiling with FP Probes. See the SI Appendix fordetails.

Expression of SH Library. See the SI Appendix for details.

Synthesis of Carbamate Library. See the SI Appendix for details.

Primary Screening of Carbamate Library by Gel-Based ABPP. Typically, ∼3–6 gel-resolvable SHs were combined into a single sample (25 μL) and incubatedwith DMSO or a carbamate (50 μM) for 45 min at 25 °C. FP-rhodamine(2 μM) was then added for an additional 45 min at 25 °C. The reactions werequenched, separated by SDS-PAGE, and visualized by in-gel fluorescencescanning. IC50 values for select compounds were determined as describedin the SI Appendix.

ABPP-MudPIT Analysis of SHs Inhibited by Carbamates in Vivo. See theSI Appendix for details.

ACKNOWLEDGMENTS. We thank David Milliken, Brent Martin, Sarah Tully,and Andrea Zuhl for technical assistance. This work was supported by theNational Institutes of Health (DA025285, GM090294, DA026161), theDeutscher Akademischer Austausch Dienst (Postdoctoral Fellowship toA.A.), the National Science Foundation (Predoctoral Fellowship to D.A.B.),Activx Biosciences, and The Skaggs Institute for Chemical Biology

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