www.elsevier.com/locate/ymeth

Methods 42 (2007) 250–260

Strategies for developing protein tyrosine phosphatase inhibitors

Lutz Tautz *, Tomas Mustelin

Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA

Accepted 15 February 2007

Abstract

Protein tyrosine phosphatases (PTPs) play vital roles in numerous cellular processes and are implicated in a growing number ofhuman diseases, ranging from cancer to cardiovascular, immunological, infectious, neurological, and metabolic diseases. Here we presentmethods for developing small molecule inhibitors for these enzymes, starting with how to set up a high throughput chemical libraryscreening for PTP inhibitors, how to confirm and prioritize hits, and how to circumnavigate possible pitfalls. Next, we present the rel-atively new hit generating method of in silico or virtual screening. We give an overview of existing software tools, describe how to chooseand generate protein target structures and illustrate the procedure with examples. We then discuss how three-dimensional PTP structurescan be analyzed in terms of their potential to bind small molecule inhibitors selectively over homologous proteins and how computertools can be applied for lead optimization efforts. We finish with a perspective of how well these PTP inhibitors might perform as futuredrugs to treat human disease.� 2007 Elsevier Inc. All rights reserved.

Keywords: Protein tyrosine phosphatases; PTPs; Small molecule PTP inhibitors; Inhibitor development; HTS; Virtual screening; Docking; Homologymodeling; SAR; Lead optimization

1. Introduction

At least one third of all cellular proteins contain cova-lently bound phosphate. Though rare by comparison toserine and threonine, tyrosine phosphorylation [1] playsan extremely important role in many processes that arecharacteristic of higher eukaryotes, such as cell-to-cell com-munication and functions that coordinate the behavior ofcell populations within these multicellular organisms [2].Tyrosine phosphorylation is a rapidly reversible post-trans-lational modification catalyzed by protein tyrosine kinases(PTKs) and reversed by protein tyrosine phosphatases(PTPs). PTPs often play very specific, non-redundant,and highly regulated roles and often participate in cellularprocesses as ‘positive’ components. The human genomecontains at least 107 genes for four evolutionary distinctfamilies of PTPs: the class I, II, and III Cys-based PTPs,

1046-2023/$ - see front matter � 2007 Elsevier Inc. All rights reserved.

doi:10.1016/j.ymeth.2007.02.014

* Corresponding author. Fax: +1 858 713 9925.E-mail address: tautz@burnham.org (L. Tautz).

and the Asp-based tyrosine phosphatases. All Cys-basedPTPs share a common catalytic mechanism based on anucleophilic cysteine with a low pKa that forms a thio-phosphate intermediate during catalysis (Fig. 1). Hydroly-sis is assisted by an invariant aspartic acid, which is part ofa loop that closes towards the catalytic pocket upon sub-strate binding.

In support of the notion that PTPs have important andnon-redundant functions in cell physiology, nearly half ofall PTP genes in the human genome have already beenimplicated in human disease [3]. Disease-related perturba-tions range from loss of expression to point-mutations, sin-gle-amino acid substitutions to overexpression and ectopicexpression of PTPs, e.g., in cancer. The rapidly increasingnumber of human diseases associated with PTP abnormal-ities has begun to elicit a growing interest in PTPs as drugtargets [3]. The spark that truly ignited the quest for PTPinhibitors with great market potential was the paperreporting the PTP1B knockout mouse, which indicatedthat PTP1B acts as a negative regulator of insulin signaling[4]. Inhibition of PTP1B would alleviate insulin resistance

O

Substrate

PO

O-

O-

Cys

S- N NH

N

Arg

O O

Asp

H

H

H

H

P-Loop

WPD-Loop

H

OH

OH

Substrate

Cys

S N NH

N

Arg

-O O

Asp

H

H

H

P-Loop

WPD-Loop

H

OH

PO

O-

O-

Phosphate

Fig. 1. Common catalytic mechanism of class I–III cysteine-based protein tyrosine phosphatases. The catalytic cysteine is part of the PTP signature motifC(X)5R located in the phosphate-binding loop (P-loop). An invariant apartic acid, located in the so-called WPD-loop, functions as the general acid/baseduring hydrolysis.

L. Tautz, T. Mustelin / Methods 42 (2007) 250–260 251

in type II diabetes and would improve the effects of insulinon both glucose balance and fatty acid metabolism.Another PTP with major impact on overall metabolism isthe phosphatase SHP2, the brain-specific deletion of whichleads to a striking weight gain due to decreased catabolismand body temperature [5]. Other potential drug targetsinclude MKP1, CDC25, HePTP, SHP1, PRL3, and PTPafor cancer, Lyp and CD45 for autoimmune diseases,LMPTP and MKP-1 for cardiovascular and neurologicaldiseases, laforin, myotubularin, and myotubularin-relatedproteins for monogenetic inherited syndromes, as well asthe Yersinia phosphatase YopH to combat the lethalityof the pathogen that causes plague [3 and referencestherein].

Inhibitors of PTKs (e.g., Gleevec, Iressa, and Tarceva)are already on the market. Many more are in clinical trials.A priori, PTPs should have the same potential of beingdrug targets. We think that the reasons that there are noPTP inhibitors on the market yet are largely historical:the first PTP was identified molecularly [6] 10 years afterthe first PTK. In addition, there was limited interest inPTPs in the late 1980s and early 1990s, since it was gener-ally thought (for lack of evidence to the contrary at thetime) that there probably are only a few non-specific house-keeping PTPs. Finally, ‘conventional wisdom’ has longheld that the structure of the active site of PTPs did notallow for the generation of selective inhibitors. Accumulat-ing evidence has now made it clear that the two latterassumptions are wrong: PTPs indeed have unique non-redundant and important functions and a great deal ofspecificity in vivo (although much remains to be exploredin this area). There are also many recent examples in the lit-erature of highly selective small-molecule inhibitors. Col-lectively, the over 50 solved three-dimensional structuresof PTPs have revealed that the surface topology surround-ing the catalytic pocket of each PTP has numerous uniquefeatures, which allow for inhibitor specificity and can be

utilized for rational structure-based design of highly selec-tive compounds.

2. High throughput screening for PTP inhibitors

High throughput screening (HTS) of chemical librariesis a commonly used starting point for drug discoverytoday. HTS has been widely used by pharmaceutical com-panies since the beginning of the nineties when roboticautomation of liquid handling, incubation, and detectionsteps became available. PTPs are particularly suitable forHTS because relatively simple in vitro enzymatic assayscan be used.

2.1. Choosing the right substrate

A widely used general substrate for PTPs is p-nitro-phenyl phosphate (pNPP, Fig. 2a), the hydrolysis of whichgenerates p-nitrophenol and free inorganic phosphate. Todetermine the rate of the enzymatic reaction, the concen-tration of product can be measured either using BiomolGreen (Biomol, Inc.), a malachite green reagent whichforms a green colored complex with free phosphate thatcan be quantified by measuring its absorbance at 620 nm,or by measuring the absorbance of the deprotonatedp-nitrophenol (pNP) at 405 nm. Both assays have someadvantages over the other. While using the Biomol Greenreagent is about five times more sensitive than readingabsorbance of pNP at 405 nm and therefore requiring lessenzyme, it has a relative narrow linear range of detection,ranging from about 1 to 50 lM free phosphate. In contrast,the absorbance of pNP is linear and gives usable absor-bance values between 5 and 500 lM. However, librarycompounds with a yellow color can interfere with the read-ing at 405 nm, resulting in potential false negatives, whilethe dark green color of the Biomol complex is less proneto be falsely simulated by compounds in the library. Buffer

O

O

OO

P O

O

OH

HOP OHO

OH

O

O

OHO

P O

O

OH

HO

O

O

F

OF

P OHO

OH

O

P OHO

OH

NO2

a b c d

Fig. 2. General PTP substrates: p-nitrophenol phosphate (pNPP, a), 6,8-difluoro-4-methylumbelliferyl phosphate (DiFMUP, b), fluorescein diphosphate(FDP, c), and O-methyl-fluorescein phosphate (OMFP, d).

252 L. Tautz, T. Mustelin / Methods 42 (2007) 250–260

compatibility is another aspect to consider when choosingbetween the two detection methods. While absorbance ofpNP can be read in virtually every buffer system, problemswith forming the Biomol complex occur when used withbuffers containing carboxylic acid groups like sodium cit-rate or N-(2-acetamido)iminoacetic acid (ADA).

More recently, fluorescence based substrates have beenused in PTP assays, such as 6,8-difluoro-4-methylumbellife-ryl phosphate (DiFMUP, Fig. 2b), fluorescein diphosphate(FDP, Fig. 2c) and O-methyl-fluorescein phosphate(OMFP, Fig. 2d). They have low fluorescence in the phos-phorylated state but are strong fluorophores when dephos-phorylated, giving them high reading to background ratios.

The concentration of substrate used for HTS usuallyequals the Km value. Classical PTPs typically have Km val-ues for pNPP in the low millimolar to high micromolarrange and Km values for fluorescent substrates in the midto low micromolar range. Conversion rates should be inthe linear range with less than 15% of the initial substrateused up.

2.2. Setting up the assay

For a 96-well plate format the total assay volume typi-cally ranges from 60 to 100 ll. Lanes 1 and 12 can be usedfor controls, allowing the screening of 80 compounds perplate. Controls include a positive control, a negative con-trol without substrate added, a background control with-out enzyme added, and a control containing 1 mM of thegeneral PTP inhibitor sodium orthovanadate. Compoundand enzyme solutions are mixed first and preincubatedfor 10–15 min at room temperature. Adding substrate solu-tion starts the reaction. Depending on the particularenzyme and robotic setup, reaction times can be as littleas 10 min and as long as several hours, respectively. Opti-mization is required for each different PTP, includingenzyme stability and performance tests over time. For405 nm reading, 60–100 ll 1 M sodium hydroxide solutionis added, which both stops the reaction and ensures thenecessary high pH for pNP reading. When using BiomolGreen reagent, twice the reaction volume is added andincubated for 20–30 min to allow for complex/color forma-tion. Absorbance is read with a plate reader and back-

ground without enzyme added is subtracted. Activity canbe expressed as percentage of the positive control or by cal-culating z-scores. The z-score is a dimensionless quantityand excludes control measurements under the assumptionthat most compounds are inactive and can serve as positivecontrol. It can be calculated by subtracting the populationmean from an individual (raw) score and then dividing thedifference by the population standard deviation.

A measure for quality and power of an HTS assay is theso-called Z-factor [7], which is unrelated to the z-score. TheZ-factor is a screening window coefficient, which is reflec-tive of both the assay signal dynamic range and the datavariation associated with the signal measurements.

The Z-factor can be calculated using Eq. (1), where (l) isthe mean and (r) the standard deviation of both the posi-tive (p) and negative (n) controls.

Z-factor ¼ 1� 3ðrp þ rnÞ=ðjlp � lnjÞ ð1Þ

Good to excellent assay performance can be expected withZ-factors between 0.5 and 1.0. Assays with Z-factors be-tween 0 and 0.5 need further optimization. If Z-factorsare less than 0, signals from the positive and negative con-trols overlap, meaning the assay is essentially useless forscreening purpose.

2.3. Assay material and instrumentation

A general PTP assay buffer contains 100 mM BisTris pH6.0 and 1 mM dithiothreitol (DTT) but should be opti-mized for each PTP. Working solutions of pNPP (Sigma–Aldrich, S0942) and enzyme were prepared freshly in assaybuffer. Sodium orthovanadate (Na3VO4, Sigma–AldrichS6508) working solution was prepared in MQ water con-taining 10% DMSO. Compound working solutions contain10% DMSO and were store in 96 well plate format. Assayplates were from Corning (costar 9017). The HTS was runon a Sagian core robotic system composed of 6 major com-ponents: The Biomek FX liquid handling system (BeckmanCoulter), the Molecular Devices Analyst HT for readingfluorescence, luminescence, or absorbance, the Kendro/Heraus Cytomat, an automated plate hotel that can hold189 plates, the ORCA (Optimized Robot for ChemicalAnalysis) Arm that links all of the components together,

L. Tautz, T. Mustelin / Methods 42 (2007) 250–260 253

and the automatic Sagian Tip-Lift, which presents the tipboxes for robotic access.

2.4. Confirmation and prioritization of hits

We recommend defining ‘hit’ as a threshold value thatgives a manageable number of such hits, e.g., >50% inhibi-tion or higher depending on the assay outcome. Regardless,we pick all hits for a secondary assay in order to confirmtheir inhibitory activity. Ideally, the secondary assay usesa different substrate or readout than the screening assayto reduce the number of false positives. For further investi-gations, these confirmed hits will be either repurchased orresynthesized. If not provided by the supplier, we suggestto confirm their purity by NMR spectroscopy and high-res-olution mass spectrometry, as library compounds are notalways pure or correct. We then characterize these hit com-pounds by performing dose-dependence measurements todetermine their IC50 and Ki values. For IC50 values, the ini-tial rate at fixed substrate concentration (equal to the corre-sponding Km values for each PTP) will be measured atvarious concentrations of compound. IC50 values can bedetermined by plotting the relative activity versus com-pound concentration and fitting to Eq. (2) using appropri-ate programs such as GraphPad Prism (GraphpadSoftware, Inc.) or any other curve fitting software.

V i=V 0 ¼ IC50=ðIC50 þ ½I�Þ ð2ÞIn this case, Vi is the reaction velocity when the inhibitorconcentration is [I], V0 is the reaction velocity with noinhibitor.

The inhibition constant Ki is useful for further prioritiza-tion of hit compounds. Not only is the value a measure ofpotency, but its determination also reveals whether a com-pound acts as competitive, uncompetitive or mixed modeinhibitor. The initial rate should be measured at variousfixed concentrations of inhibitor with 6–8 different concen-trations of substrate, similar to the determination of Km. Ki

values can be calculated and the inhibition pattern can beevaluated by fitting the data to the Michaelis–Menten equa-tions for either competitive Eq. (3), uncompetitive Eq. (4),or mixed Eq. (5) inhibition, using nonlinear regressionand the program GraphPad Prism or similar.

m0 ¼ V max½S�=ðKmapp þ ½S�Þwith Kmapp ¼ Kmð1þ ½I�=K iÞ ð3Þ

m0 ¼ V max½S�=ðKm þ ð1þ ½I�=K iÞ½S�Þwith Kmapp ¼ Km=ð1þ ½I�=K iÞ ð4Þ

m0 ¼ V max½S�=ðð1þ ½I�=K icÞKm þ ð1þ ½I�=K iuÞ½S�Þwith Kmapp ¼ Kmð1þ ½I�=K icÞ=ð1þ ½I�=K iuÞ ð5Þ

In the case of the mixed inhibition model Kic is the inhibi-tion constant for the competitive participation and Kiu theinhibition constant for the uncompetitive participation.The best fitting equation decides which of the three inhibi-tion patterns is the most likely one for each compound. For

a comparison of the fitting results the second-orderAkaike’s Information Criterion (AICc) [8], which is widelyused as a criterion of model selection, can be calculatedwith Eq. (6), where N is the number of data points, SSthe absolute sum of squares and K the number of parame-ters fit by nonlinear regression plus 1.

AICc ¼ N lnðSS=NÞ þ 2K þ ð2KðK þ 1ÞÞ=ðN � K � 1Þ ð6Þ

The probability to have chosen the right model can be com-puted with Eq. (7), where D is the difference between AICcscores.

probability ¼ expð�0:5DÞ=ð1þ expð�0:5DÞÞ ð7ÞAlthough a competitive inhibition pattern is desirable,mixed inhibition is very common among HTS screeninghits. In general, compounds with competitive Ki valueslower than 10 lM and IC50 values lower than 20 lM willbe taken into a counter screen against other PTPs and fur-ther into structure–activity-relationship (SAR) studies forpharmacophore and lead generation.

2.5. Pitfalls

Although HTS is a very powerful approach, it is alsoprone to several different types of errors. Indeed, HTSresults should not be trusted without careful validationand follow-up. Errors include: methodical errors withinthe robotic systems, false negatives, and false positives,including promiscuous inhibitors and compounds ableto form large colloid-like aggregates that sequester andthereby inhibit enzymes [9]. For PTPs, specific pitfallsto watch out for include oxidizing compounds or nucleo-philic acceptors that irreversibly bind to the thiolate ofthe catalytic cysteine, even with reducing agents like dithi-othreitol (DDT) present. For example, quinones arenotorious as both oxidizing and covalent binders, thelatter because of their Michael-acceptor properties. In arecent study [10] ortho-quinones were shown to inhibitPTPa by producing high levels of hydrogen peroxide,which subsequently inactivated the phosphatase byoxidation. Ironically, the presence of DTT enhanced theproduction of peroxide. For these reasons, any suspiciouslooking hits can be tested by varying the preincubationtime of PTP and compound to determine if there is atime-dependent component of inhibition. As an exampleof this, we show the time-dependent inhibition of theYopH PTP by 2-(4-methoxyphenyl)cyclohexa-2,5-diene-1,4-dione (Fig. 3), a hit from our HTS against thisphosphatase [11]. There is a linear increase in the ratioof kcat (control) over kcat (20 lM compound) over 10–60 min of preincubation.

3. Virtual screening for PTP inhibitors

Based on the increasing number of available three-dimensional structures of PTPs from all major subfamiliesof enzymes during the last 10 years (http://www.pdb.org),

O

O

O

0 25 50 753

4

5

6

7

8

preincubation time enzyme/compound [min]

ratio

kca

t (co

ntro

l) / k

cat (

20 μ

M c

ompo

und)

Fig. 3. Time-dependent inhibition of the Yersinia phosphatase YopH bythe para-quinone 2-(4-methoxyphenyl)cyclohexa-2,5-diene-1,4-dione.

254 L. Tautz, T. Mustelin / Methods 42 (2007) 250–260

in silico methods have become more and more popular aslead discovery tools. The basic technique used for virtualscreening of compound databases is called ‘docking’, acomputational method that samples conformations ofsmall molecules in protein binding sites and applies scoringfunctions to rank molecules by a free energy-like term. In arecent published example, inhibitors of PTP1B weresearched for using both virtual screening and HTS of a400,000-compound chemical library [12]. Three hundredand sixty-five high-scoring docked compounds were testedexperimentally, improving the hit rate 1,700-fold comparedto the unbiased HTS screening. Interestingly, there was nooverlap among the high-throughput and virtual screeninghit lists, suggesting that the two techniques are complemen-tary. In our own experience, there is little overlap betweena de novo virtual screen and HTS of the same library set ofcompounds. However, in combination they are very pow-erful and yield much more insights into how a particularinhibitor binds. This information is vital for further devel-opment of lead compounds and the synthesis of inhibitorswith improved potency.

3.1. Docking programs

Many commercial software packages with differentdocking algorithms are available. Most successfully appliedalgorithms are DOCK [13], FlexX [14], GOLD [15], Auto-Dock [16], GLIDE [17], QXP [18], and ICM [19]. BioSol-veIT’s FlexX is an extremely fast, robust, and highlyconfigurable flexible ligand docking algorithm, optimizedfor virtual screening and grid computing. It is one of themost used incremental fragment-based docking programs.Since FlexX is no longer included in Tripos’s SYBYLpackage, convenience and usefulness of its recently intro-duced graphical user interface prototype remains to beseen. GOLD, a collaboration between the University ofSheffield, GlaxoSmithKline plc, and The Cambridge Crys-

tallographic Data Centre (CCDC), though not as fast asFlexX, is highly regarded for its accuracy and reliability.GOLD features a genetic algorithm (GA), full ligand flex-ibility, user defined partial protein flexibility for up to 10residues, and a choice of scoring functions: GoldScore,ChemScore, and User defined score. GOLD is optimizedfor virtual screening applications as well as for parallel exe-cution on processor networks. Schrodinger’s GLIDEapproximates a complete systematic search of the confor-mational, orientational, and positional space of the dockedligand, using a Monte-Carlo simulated annealing tech-nique. GLIDE offers very good results for both virtualscreening and binding mode prediction. It features theGLIDE_Score function, which is a more advanced versionof ChemScore with force-field-based components and addi-tional terms accounting for solvation and repulsive interac-tions. Though significantly slower than FlexX, GLIDE hasbeen praised for its accuracy since its introduction in 2003.Molsoft’s bio/cheminformatics, modeling, and dockingsoftware ICM features one of the most comprehensivepackages with a very user-friendly interface and runs onall major platforms. Its virtual ligand screening (VLS)engine is based on a biased probability Monte-Carlomethod called Internal Coordinate Mechanics (ICM). Ina recent study at AstraZeneca R&D, ICM-VLS has beenshown to perform in average at the same level as GLIDE,docking 164 known ligands to 12 different targets. Interest-ingly, using the tyrosine phosphatase PTP1B as target,ICM’s accuracy scored best among FlexX, GOLD, andGLIDE [20].

3.2. Choosing the right target structure

A successful in silico screening for PTP active siteinhibitors starts with choosing the right three-dimensionalstructure. General things to consider when choosing astructure from the PDB database are: high resolution(at least 2.5 A, better <2 A), low b-factors (especiallywithin the binding site), and no missing residues or sidechain atoms within the docking region. Besides these gen-eral quality factors, PTP-specific considerations deal withthe conformation of the active site. Since binding of sub-strate or any other ligand into the phosphate bindingpocket (catalytic pocket) causes a conformational changeof the WPD loop, two types of 3 D structures can be typ-ically found. The ‘open’ conformation with no substrate/ligand bound refers to the WPD-loop in distant position.Upon substrate/ligand binding, the loop shifts about 8 Atowards the catalytic pocket, forming the ‘closed’ confor-mation. Therefore, only structures in the closed confor-mation are suitable to screen for potential ligandsthought to target the catalytic pocket. To further compli-cate matters, there are different versions of the closed con-formation for several PTPs, which arise from the kind ofligand used for co-crystallization. Structures with onlysmall phosphate or phosphate-like ions bound to theP-loop deep at the bottom of the catalytic pocket can

L. Tautz, T. Mustelin / Methods 42 (2007) 250–260 255

have WPD-loops in an almost lid-like position, makingthe pocket entrance very narrow. We find that dockingphosphotyrosine into the pocket gives a good measureto validate the usefulness of a particular 3D structure:the docked phosphotyrosine should have its phosphategroup in the same position as the phosphate (or tungstate,vanadate, or sulfate) in the co-crystal structure.

If a three-dimensional structure of the PTP of interest isnot available, or only an open conformation has been solved,protein homology modeling offers a good solution. We usethe SWISS-MODEL automated comparative protein mod-eling server (Swiss Institute of Bioinformatics and Biozen-trum Basel, http://swissmodel.expasy.org/) [21], whichprovides a good first approach. Via a web-based interface,an amino acid sequence can be uploaded, which will be firstsubjected to a Blast to search for proteins with high sequencesimilarity and existing 3D structure as suitable templates. Itis also possible to submit up to 5 templates or multiplesequence alignments. The server then uses the software toolsProMod and ProModII to do the comperative modeling, fol-lowed by an energy minimization step using the Gromos96force field algorithm. If the templates were chosen by the ser-ver, one has to make sure that the template structure is in theclosed conformation. MODELLER [22], another popularsoftware for protein homology modeling, sometimes yieldsbetter results than the SWISS-MODEL server. Thoughfreely available for academic use, a graphical user interfacefor MODELLER is only provided commercially fromAccelrys (Accelrys Software Inc.).

3.3. Receptor and ligand preparation

Though some docking algorithms, e.g., FlexX, aredesigned to handle raw PDB files, preparation of the proteinstructure for docking is advised. Most software have thetools to remove ligands, solvent molecules, and ions, addinghydrogens and optimizing their position, checking forclashes, adding correct charges to the molecule, correctingthe orientation of side chain amides, hydroxyl groups, anddisulfide bonds, as well as the tautomeric state of histidineresidues and the protonation states of basic and acidic resi-dues. In cases where additional domains are present thatare not involved in ligand binding, these should be alsoremoved. When using the SYBYL software package (Tripos,Inc.), the final receptor can be saved as a MOL2 file, a formatwidely supported by other programs. For docking withSchrodinger’s GLIDE or Molsoft’s ICM, we suggest to usethe companies own receptor preparation tools.

Besides setting up the receptor, preparing the com-pounds in the database is as important. For docking, chem-ical structures need to have 3D coordinates assigned, whichnormally can be accomplished with tools provided in thesoftware package. The same is true for assigning chargesto carboxylic, sulfonic, phosphonic, etc. acids, amino andguanidinium groups. If the docking program does not pro-vide the necessary tools for preparing the ligands, OpenEye’s (OpenEye Scientific Software) applications for struc-

ture generation, such as OMEGA or SZYBKI, presentgood alternatives. Its FILTER software is a very usefultool to filter databases based on physical properties orfunctional groups.

3.4. Docking of a small benzenesulfonic acid library to a

modeled receptor

In a proof-of-concept study in our laboratory, we usedin silico methods to find high-affinity inhibitors for thePTP LYP [23,24], the three-dimensional structure of whichhas not yet been determined. Starting with the amino acidsequence of the catalytic domain of LYP (1–309), a homol-ogy model was generated using SWISS-MODEL and ahigh-resolution structure of the very close homolog PTP1B(PDB code: 1PTY) as template. The resulting model struc-ture of Lyp comprised amino acids 39–293 and could bestructurally aligned to 1PTY.pdb with an RMSD of0.36 A. When only the P-loops of LYP (His226–Thr234)and PTP1B (His214–Ser222) were aligned the RMSD was0.06 A, indicating virtually identical backbone conforma-tions of the two phosphotyrosine binding sites. However,the surface topology of each PTP was unique. The modeledcatalytic domain of LYP was then taken into SYBYL (ver-sion 7.2, Tripos, Inc.) for further preparation for dockingstudies. AMBER charges were assigned, orientations ofside chain amides were corrected, and hydrogens wereadded and their position optimized by energy minimizationusing AMBER7 FF99 force field. A compound librarydatabase was generated out of 367 commercially availablebenzenesulfonic acid derivatives from Sigma–Aldrich withmolecular weights less than 600. Benzenesulfonic acid(BSA, Table 1) is a nonhydrolyzable phosphotyrosinemimetic and inhibits PTPs competitively with IC50 valuesin the high micromolar to low millimolar range. Dockingof compounds that contain benzenesulfonic acid as sub-structure was meant to find molecules, which would haveadditional structural features that could improve bindingto LYP’s active site by, hopefully, several orders of magni-tude. Structures were exported from the Sigma–Aldrichwebsite as SD files and subsequently examined in Chem-Finder (CambridgeSoft, Inc.) to remove counter ions andadding charges as necessary. 3D coordinates of the com-pounds were generated using CONCORD as implementedin SYBYL and saved as MOL2 files. Flexible docking wasaccomplished using the FlexX module within SYBYL. Thereceptor site was defined as an 8 A radius within the cata-lytic cysteine 227. The 30 lowest energy poses for each com-pound and their corresponding Total-Scores (FlexXscores) and CScores, including G-Scores, PMF-Scores,D-Scores, and ChemScores were calculated. Total-Scoresranged from �44.6 for the best scoring compoundBSA311 (Table 1) to �7.1 for the worst scoring molecule.Benzenesulfonic acid scored with �23.4 in the middle ofthe range. The 5 best ranked compounds according to theirTotal-Scores and ChemScores at highest CScore were thenpurchased for evaluation. All of them showed good to

Table 1Docking study of benzenesulfonic acid and 367 derivatives with a homology model of lymphoid tyrosine phosphatase Lyp

Structure Total-Score (FlexX) ChemScore @ highest CScore IC50 (lM)

BSA

S

O

O

OH

�23.4 �30.2 >200

BSA281

N N

NH

O

HO

HO S

O

O

OH

�35.5 �50.9 0.401

BSA68

HN

NH

O

HN

S OH

O

O�39.2 �39.4 6.43

BSA357

O

O

OOH

SO

NH

OH

N+

H

�39.9 �54.4 0.456

BSA61

O

S

O

NH2

OH

NHHO

�41.0 �48.1 0.062

BSA311

S

O

O

NN

N

OH

N+O-

ONO

N+

-O O

N+

-O

O

�44.6 �48.9 0.348

Docking scores as well as IC50 values against Lyp for benzenesulfonic acid and the 5 best ranking derivatives are given.

256 L. Tautz, T. Mustelin / Methods 42 (2007) 250–260

L. Tautz, T. Mustelin / Methods 42 (2007) 250–260 257

excellent inhibiton of LYP in vitro with compound BSA61being the best inhibitor, having an IC50 value of 62 nM(Table 1). Clearly, in this case, in silico docking was ableto discriminate compounds according to their bindingaffinity for LYP’s active site. Physically evaluation of lessthan 2% of the total number of compounds resulted inthe finding of a 62 nM hit, which corresponds to about fourorders of magnitude higher potency than the substructurebenzenesulfonic acid alone. This example also demon-strates how much interactions outside the catalytic pocketcan contribute to an inhibitor’s efficacy.

4. Structure-aided lead optimization

Rational drug design and structure-aided lead optimiza-tion is not new: Capoten (captopril) from Bristol-MyersSquibb, the first ACE inhibitor on the market, was devel-oped in 1981 based on the structure of carboxypeptidaseA, a protein with similar catalytic capabilities. However,structure-aided methods have not been as widely appliedsince the late nineties, when it became clear that HTS alonewas not going to be the magic bullet in drug discovery, andsecond, automated crystallographic techniques made itpossible to solve protein structures in a high throughputfashion. Since the 3D structure of PTP1B was reported in1994 [25] as the first of a protein tyrosine phosphatase,many more have followed. Recent efforts of several struc-tural genomics consortia are aiming on crystallization ofall human PTP catalytic domains.

4.1. Examining protein surfaces

Small molecules bind to globular proteins by interactionwith amino acid residues that are exposed at the surface ofthe protein but are preferably located within pockets orgrooves to maximize interactions. Software tools likeGRASP [26] or MOLCAD [27] can be applied to examinethe binding landscape within an active site. GRASP is oneof the most widely used programs for computing and dis-playing molecular surfaces of proteins. Rendered surfacescan be color-coded by electrostatic potential (Poisson–Boltzmann) as well as by any property of the underlyingatoms (e.g., hydrophobicity) or by their own intrinsic prop-erties, such as local curvature. While GRASP was devel-oped for the use with large polyhedral surfaces, such asproteins, MOLCAD’s rendering techniques allow display-ing of property-coded surfaces of both small moleculesand macromolecules. MOLCAD also offers an even moreextensive set of 3D-representations and is unique in its abil-ity to display the surface separating two molecules as wellas the surface of intra-molecular channels. MOLCAD isdistributed by Tripos as a module of its SYBYL package.

4.2. Diversity of PTP active sites

Although all Cys-based PTPs share a similar phosphate-binding loop (the P-loop) and all class I Cys-based PTPs

have the same overall fold, the many solved PTP structuresmake it abundantly clear that each PTP has numerousunique features on the surface surrounding the catalyticpocket. A systematic comparison of the surfaces surround-ing the active sites in all PTPs by visualizing topology, elec-trostatic potential, and lipophilic potential reveals aremarkable diversity within PTP families. These features,including cavity depth of pockets, also provide a good mea-sure of the likelihood of finding small-molecule inhibitorsthat are potent and selective. Fig. 4 illustrates MOLCADsurface representations of six different PTPs, color-codedby their normalized electrostatic potential. Beyond a fewsimilarities, the surfaces display striking differences in topol-ogy and charge distribution profile. For example, thissurface is mainly positively charged in PTP1B (Fig. 4c),PRL-1 (Fig. 4d), and CD45 (Fig. 4e), whereas in LMPTP(Fig. 4b) and HePTP (Fig. 4f) negatively charged residuesare dominant. In VHR (Fig. 4a), the charge distribution issharply divided into a negative and a positive region. A sim-ilar examination of the lipophilic potential of these surfaces,or other properties, can be very informative for the design ofinhibitors and the choice of controls for specificity.

4.3. Selective small molecule inhibitors for the tyrosine

phosphatase YopH

The following example illustrates the use of PTP surfacecomparisons for inhibitor development. The Yersinia phos-phatase YopH is a critical virulence factor for the plaguebacterium Yersinia pestis. This pathogen deploys a typeIII secretion system to inject a set of paralyzing proteinsdirectly into the cytoplasm of macrophages and lympho-cytes of infected individuals [28,29]. As a result, the tar-geted cells become unable to respond and the bacteriacan multiply unopposed by the normal mechanisms of hostdefense. In T lymphocytes, YopH shuts down T cell anti-gen receptor signaling by dephosphorylating key signalingmolecules [30]. YopH is therefore a drug target for thetreatment of plague.

To develop small-molecule inhibitors for YopH, we per-formed a HTS from which hits were taken into structure–activity-relationship studies and a structure-aided leadoptimization process, resulting in the identification of a ser-ies of novel YopH inhibitors with nanomolar Ki values, aswell as the structural basis for inhibition [11]. Two similarHTS hits, (Z)-5-(5-((2,4-dioxothiazolidin-5-ylidene)methyl)furan-2-yl)-2-hydroxybenzoic acid (Fig. 5a) and (E)-4-(meth-ylthio)-2-(5-((5-(4-nitrophenyl)furan-2-yl)methylene)-4-oxo-2-thioxothiazolidin-3-yl)butanoic acid (Fig. 5b) weredocked into YopH’s active site, which showed that boththe 2-hydroxybenzoic acid (salicylic acid) moiety in the firsthit and the nitrophenyl ring in the second hit can occupythe catalytic pocket. Both the salicylic acid and the nitrogroup were found to bind to the P-loop through hydrogenbond interactions. In addition, the oxygen of the furanylring interacted with side chains of Gln357 and Arg404 atthe rim of the pocket, both of which are unique to YopH

Fig. 4. Comparison of PTP active site MOLCAD surface representations of VHR (1J4X.pdb, a), LMPTP (1PHR.pdb, b), PTP1B (1PTY.pdb, c), PRL-1(1XM2.pdb, d), CD45 (1YGR.pdb, e), and HePTP (1ZC0.pdb, f). P-loops were aligned to ensure the representations show structures in an equal position.All structures are in ‘closed’ conformation. The color code of the MOLCAD surfaces represents the normalized electrostatic potential (red: most positive,purple: most negative).

258 L. Tautz, T. Mustelin / Methods 42 (2007) 250–260

compared to other PTPs. From further SAR and dockingstudies, furanyl salicylic acid evolved into our preferredpharmacophore for YopH. A comparison of surface fea-tures in YopH with other PTPs also suggested that a rigidhydrophobic moiety at the other end of the inhibitorswould potentially interact with a large hydrophobic patchjust outside the catalytic pocket unique to YopH. Thecorresponding region in PTP1B is blocked by a largeprotrusion (Fig. 6b). Indeed, 5-{5-[(6,7-dimethyl-3-oxo[1,3]thiazolo[3,2-a]benzimidazol-2(3H)- ylidene) methyl]-2-

furyl}-2-hydroxybenzoic acid (Fig. 5c), a compound thatmet these requirements, was found to be the most potentinhibitor of YopH with a Ki value of 143 nM and alsodocked well into the predicted position (Fig. 6a).Moreover, the inhibitor showed a 39-fold selectivity forYopH over PTP1B and three orders of magnitudegreater selectivity over many other PTPs tested. Inaddition, the compound was shown to reverse the effectsof YopH in human T cells at concentrations as low as6 lM [11].

O

OHOH

O

HN

SO

O

O

N+

NS

O

S

O

OH

S

O-O

O

OH OH

O

NN

SO

a b c

Fig. 5. Selected HTS hits against the Yersinia phosphatase YopH (a andb) and a follow up lead structure with improved potency and selectivity(c).

L. Tautz, T. Mustelin / Methods 42 (2007) 250–260 259

5. Concluding remarks

Even after some 10 years of work, including majorefforts with PTP1B in both academic laboratories and thebiotech and pharmaceutical industries, the developmentof PTP inhibitors can still be regarded to be in its infancy.Since the phase II clinical trial of PTP1B inhibitor ertipro-tafib (Wyeth Research) was terminated because of unsatis-factory efficacy, dose-limiting side effects, and data fromthe in vivo treatments that were inconsistent with PTP1Binhibition as the sole mechanism for improved insulin sen-sitivity, the interest in PTPs as drug targets has cooled offamong pharmaceutical companies. However, we predictthat this may be temporary as the literature on PTPs andtheir roles in human disease grows.

Fig. 6. Active site MOLCAD surface representations of Yersinia phosphatase Ywhite error indicates a large protrusion, which prevents binding of the YopMOLCAD surfaces represents lipophilic potential (blue: most hydrophilic, br

A major challenge in PTP inhibitor development isposed by the hydrophilic and multiply charged nature ofthe natural phosphotyrosine-containing substrates forPTPs. Substrate-like inhibitors, i.e., phosphotyrosinemimetics, are usually not able to penetrate the plasmamembrane. Therefore, many current efforts now focus oncell-permeable lead compounds, typically discoveredthrough HTS, which can serve as starting points for thedevelopment of efficient compounds with optimal pharma-cological properties. HTS efforts within the NIH RoadmapInitiative for Medical Research include several promisingPTP drug targets and are on their way to hopefully yieldnew chemical entities for lead structure generation. BesidesHTS projects, which are still the major source of activehits, in silico methods are becoming more and more accu-rate and reliable. Virtual screening is a great tool forscreening through large libraries (even millions of com-pounds) in a very cost-effective manner. Virtual screeningcan be viewed as a filter that brings large compoundlibraries down to a manageable size for direct in vitro test-ing. This may save both time and cost, allowing academiclaboratories to partake efficiently in hit discovery. It shouldbe pointed out that for lead identification it is not necessaryto find all active compounds, finding one or two from eachactive compound class can be considered a success in vir-tual screening. With continuously improving docking algo-rithms and affordable computing power of processor grids/networks, in silico methods are most likely to play an evenmore important role in the future.

It is now clear that although all Cys-based PTPs havevery similar catalytic cores, they differ dramatically in sur-face topology and charge distribution in the terrain thatsurrounds the catalytic pocket. Therefore it is indeed possi-ble to develop small molecules that can bind with both highaffinity and high selectivity. However, it may not be

opH with docked lead structure (a) and its close homolog PTP1B (b). TheH lead structure to PTP1B in a similar position. The color code of theown: most hydrophobic).

260 L. Tautz, T. Mustelin / Methods 42 (2007) 250–260

necessary to develop strictly monospecific PTP inhibitors:groups of closely related PTPs appear to often have at leastpartly overlapping functions. Thus, spectrum-selective PTPinhibitors that act against such a group of PTPs mightshow much greater biological activity.

A bottleneck in PTP inhibitor development is the lack ofwell-established cell-based assays to evaluate hits in intactcells. Ideally, such assays should reveal both specific targetinhibition as well as off-target effects. Ideally, one shouldsee hyperphosphorylation of the substrate(s) of the targetPTP and/or enhanced downstream pathways, but thereshould not be any effects on overall tyrosine phosphoryla-tion, other pathways, or cell viability (unless the PTP isspecifically involved in survival). Cell-based assays wouldallow better prioritization of cell-permeable, non-toxic, effi-cient and selective enough compounds, the dose–responsecurves of which would give important information for test-ing them in animal models. Finally, we predict that the firstPTP inhibitor on the market will greatly increase the enthu-siasm for PTPs and will inspire more research and drugdevelopment in this area.

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