Binding Studies and Quantitative Structure–Activity Relationship of 3-Amino-1H-Indazoles as...

Binding Studies and Quantitative Structure–Activity Relationship of 3-Amino-1H-Indazolesas Inhibitors of GSK3b

Julio Caballero1,*, Szymon Zilocchi2,3,William Tiznado3, Simona Collina2 andDaniela Rossi2

1Centro de Bioinform�tica y Simulaci�n Molecular, Facultad deIngenier�a en Bioinform�tica, Universidad de Talca, 2 Norte 685,Casilla 721, Talca, Chile2Department of Pharmaceutical Chemistry, University of Pavia,via Taramelli 12, 27100 Pavia, Italy3Departamento de Ciencias Qu�micas, Facultad de Ecolog�a yRecursos Naturales, Universidad Andres Bello, Av. Repfflblica 275,Santiago, Chile*Corresponding author: Julio Caballero, [email protected]

Docking of 3-amino-1H-indazoles complexed withglycogen synthase kinase 3 beta (GSK3b) was per-formed to gain insight into the structural require-ments and preferred conformations of theseinhibitors. The study was conducted on a selectedset of 57 compounds with variation in structureand activity. We found that the most active com-pounds established three hydrogen bonds with theresidues of the hinge region of GSK3b, but someof the less active compounds have other bindingmodes. In addition, models able to predict GSK3b

inhibitory activities (IC50) of the studied com-pounds were obtained by 3D-QSAR methods CoM-FA and CoMSIA. Ligand-based and receptor-guidedalignment methods were utilized. Adequate R2

and Q2 values were obtained by each method,although some striking differences existedbetween the obtained contour maps. Each of thepredictive models exhibited a similar ability topredict the activity of a test set. The applicationof docking and quantitative structure–activityrelationship together allowed conclusions to bedrawn for the choice of suitable GSK3b inhibitors.

Key words: CoMSIA, glycogen synthase kinase, GSK3b, moleculardocking, quantitative structure–activity relationships

Received 22 March 2011, revised 28 June 2011 and accepted for publi-cation 10 July 2011

Glycogen synthase kinase 3b (GSK3b) is a serine ⁄ threonine proteinkinase that plays a fundamental role in a wide variety of functionsin tumorigenesis, including cell division, proliferation, differentiation,and adhesion (1), and it is also associated with hyperdopamine and

D2 receptor-mediated inhibition of synaptic NMDA receptor function,which has important implications given the important role of NMDAreceptors in the cellular aspects of working memory (2). In addition,evidence indicates that high GSK3b activity ⁄ expression sensitizesneuronal cells to ethanol-induced damages because GSK3b is aconverging signaling point that mediates some of ethanol's neuro-toxic effects (3). Because of all of the above, GSK3b is considereda promising pharmacotherapeutic target for the treatment for sev-eral human diseases that have significant limitations in their currenttreatments, including diabetes (4), neurodegenerative diseases (5),inflammatory disorders (6), and cognitive, behavioral, and neurologi-cal impairments in children with fetal alcohol spectrum disorders(3). The interest in GSK3b is focused on the potential of its inhibi-tors to treat these diseases.

Pharmacological inhibitors of GSK3b have become the focus ofgreat interest in drug discovery (7). Unfortunately, most GSK3binhibitors have similar inhibitory activity on cyclin-dependent kinases(CDKs), making them prone to cause side-effects. Therefore, a keypoint of inhibitor design is to achieve target selectivity. For this,one strategy is the study of the structural features of the inhibitor–GSK3b complexes and the structure–activity relationships of theinhibitors. The fact that crystal structure of GSK3b and some of itscomplexes have been determined offers clues for rational design.

Recently, Lesuisse et al. (8) identified a novel series of 3-amino-1H-indazoles as inhibitors of GSK3b. These authors found severalpotent analogs with IC50 < 10 nM and selectivity for other kinases.In the current work, we analyzed the three-dimensional (3D) posi-tioning of the 3-amino-1H-indazoles inside the active site of GSK3b.Our approach was to obtain bioactive conformations of an inhibitorpanel by molecular docking and to use the known structures to ver-ify the docked position by the 3-amino-1H-indazoles scaffold ofeach inhibitor. This docking analysis can bring to light the mostlikely binding modes for each ligand.

Our second objective was to develop robust and validated quantita-tive structure–activity relationship (QSAR) models. Quantitativestructure–activity relationship is a method used in drug design,which benefits from the latest bioinformatics databases and insilico tools (9). The knowledge accumulated in huge databases isnot enough to simulate the real biological systems. There are still alot of questions about molecular biology that needs answers. Withthree-dimensional QSAR analysis, it is possible to analyze theprobable structural elements affecting the biological activity of

631

Chem Biol Drug Des 2011; 78: 631–641

Research Article

ª 2011 John Wiley & Sons A/S

doi: 10.1111/j.1747-0285.2011.01186.x

compounds, and the model can be used as an artificial tool indesigning new bioactive compounds. Here, we report QSAR predic-tive models by using Comparative Molecular Similarity Indices Anal-ysis (CoMSIA) applied on docked structures.

Materials and Methods

Data setThe primary structures and activities of 57 3-amino-1H-indazoles asGSK3b inhibitors were taken from the literature (8). Inhibitory activi-ties were collected and transformed into log(106 ⁄ IC50) values. IC50

values represent the compound nM concentrations that inhibit theGSK3b activity by 50%. Compounds and inhibitory biological activi-ties used in this study are summarized in Table 1.

DockingIn molecular docking, we attempt to predict the structure (or struc-tures) of the intermolecular complex formed between two or moremolecules. Docking has been widely used to suggest the bindingmodes of kinase inhibitors (10,11). Most docking algorithms areable to generate a large number of possible structures, and so theyalso require a means to score each structure to identify those ofmost interest. In general, the 'docking problem' is thus concernedwith the generation and evaluation of plausible structures of inter-molecular complexes.

Docking was performed with software ICM (12). The characteristicsof ICM docking method have been described in previous reports(11). The protein coordinates extracted from the crystal structure ofhuman GSK3b in a complex with inhibitor I-5 were used as proteinstructure [accession code in Protein Data Bank: 1Q4L (7)] for dockingsimulations. The structures of the inhibitors were sketched by usingMolecular Editora of ICM softwareb. Protein and inhibitor structureswere converted into ICM objects. During protein conversion process,hydrogens were added and they were optimized. Meanwhile, duringligand conversions, 2D representations were converted into 3Dones, partial charges were assigned, and rotatable bonds wereidentified. IcmPocketFinder (13) was used for identifying putativepockets with a tolerance value of 4.6. Initial ligand position and ori-entation, and box position and size, were kept according to the val-ues suggested by the program. The maximum van der Waalsrepulsion was set to 4.0 � in each docking experiment. The twentybetter docking poses for each ligand were analyzed by examiningtheir relative total energy scores. The best docked position wasdetermined by comparing docking poses and considering the totalenergy value. Among several similar docking poses, the more ener-getically favorable conformation was selected.

Quantitative structure–activity relationshipThe idea underlying CoMSIA method is that differences in a targetproperty are often related to differences in the shapes of themolecular fields surrounding the tested molecules. To determine theshape of a molecular field, the magnitudes of its Lennard–Jonesand Coulombic fields are sampled at regular intervals throughout adefined grid. Despite there are many adjustable parameters in

CoMSIA, certainly the most important is the relative alignment ofthe individual molecules when their fields are computed. Properlyaligned molecules have a comparable conformation and a similarorientation in Cartesian space. Two alignment methods were usedfor CoMSIA applications: by considering the positions obtained bydocking, under binding site constraints, and by considering a com-mon structure as a template.

CoMSIA was performed using the SYBYL 7.3 software of Triposc. Thedata set was divided into two subsets. Some compounds were cho-sen randomly as a test set and were used for external validation ofthe 3D-QSAR models; the training sets included all the remainingcompounds. For the QSAR calculations, aligned molecules wereplaced in a rectangular grid, and the interaction energies betweena probe atom and all compounds were computed at the surroundingpoints, using a volume-dependent lattice with 2.0 � grid spacing.Then, standard Sybyl parameters were used for a partial leastsquares (PLS) analysis. The number of components in the PLS mod-els was optimized by using a Q2 value, obtained from the leave-one-out (LOO) cross-validation procedure, with the SAMPLS (14)sampling method. The number of components was increased untiladditional components did not increase Q2 by at least 5% peradded component. In the CoMSIA analyses, similarity is expressedin terms of steric occupancy, electrostatic interactions, local hydro-phobicity, and hydrogen bond (HB) donor and acceptor properties,using a 0.3 attenuation factor. The modeling capability (goodness offit) was judged by the correlation coefficient squared, R2. The pre-diction capability (goodness of prediction) was indicated by thecross-validated R2 (Q2) and by the predictions of the test set.

Results and Discussion

Docking results, inhibitor orientations insideGSK3b

Docking simulations were undertaken using the structure of humanGSK3b in a complex with inhibitor I-5, which has substantial differ-ences in the structure with respect to the studied compounds. Theuse of a reference structure containing a compound of this series ofinhibitors should lead to more reliable docking results, but there areno structures that comply with this criterion. Lesuisse et al. reportedX-ray crystals of compounds 37 and 54 forming complex withCDK2 and improve GSK3b inhibitory activity with the help of crystalstructures of these compounds within CDK2 active site (8). For this,these authors consider that 'inhibitors are oriented similarly inGSK3b and CDK2'. Our docking results support this consideration.

The docking simulations were started using compounds 37 and 54.The orientations obtained by docking experiments carried out insideGSK3b's active site were compared with the X-ray crystallographicstructures of complexes between CDK2 and these compounds(accession codes in PDB: 3LFS and 3LFN) (8). As it can be observedin Figure 1, the docked structures showed the same orientation forinhibitors in GSK3b with respect to the known orientations of theinhibitors in CDK2. The values of the root-mean standard deviations(RMSDs) for the docked structures with respect to the X-ray crystalinhibitor structures (in CDK2) were 1.532 and 0.986 �, respectively.This corroborates that active 3-amino-1H-indazoles have the same

Caballero et al.

632 Chem Biol Drug Des 2011; 78: 631–641

orientation in GSK3b and CDK2, i.e., the aminoindazole moietyinteracts with the hinge area of both kinases, while the butyrylchain points toward the solvent media.

The remaining compounds were docked following the same dockingprotocol. Comparison of the different docking results of all ligandsshows that 41 compounds adopt the same binding mode of com-pounds 37 and 54 inside CDK2, where N1 of the indazole estab-lished an HB interaction with the oxygen from the backbone ofAsp133, N2 of the indazole established an HB interaction with thebackbone NH of Val135, and NH at position 3 of the indazoleestablished an HB interaction with the oxygen from the backboneof Val135 (Figure 2A). This mode of interaction was denoted as MI-A in this paper. Compounds 14, 19, and 26 adopt another modeof interaction, denoted as MI-B in this paper, where N1 of the in-dazole established an HB interaction with the oxygen from thebackbone of Ile62 and NH at position 3 of the indazole establishedan HB interaction with the oxygen from the backbone of Pro136(Figure 2B). Compounds 16, 20, 23–25, 28, 30, 31, and 33–36

adopt another mode of interaction, denoted as MI-C in this paper,where N1 of the indazole established an HB interaction withthe oxygen from the backbone of Val135 and NH at position 3 ofthe indazole established an HB interaction with the oxygen fromthe backbone of Ile62 (Figure 2C). Finally, the inactive bulkier com-pound 53 had only HB interaction between N1 of the indazole andthe oxygen from the backbone of Val135 (Figure 2D); this compoundis too large to enter the active site. Compounds that adopt MI-A

Table 1: Experimental and predicted inhibitory activities[log(106 ⁄ IC50)] of 3-amino-1H-indazoles as inhibitors of GSK3b usingmodels CoMSIA1-EA and CoMSIA2-SDA

NH

N

HN

Cl

OH

O

O

1

NH

N

HNPh

Cl

O

X

NH

N

HNR1

R2

O

32 X = Cl57 X = F

2–31, 33–56

Compound R1 R2

log(106 ⁄ IC50)

Exp.

CoMSIA1-EAPred.

CoMSIA2-SDAPred.

Training set1 2.93 2.87 3.614 H 4-OH–C6H4– 4.36 4.08 3.515 H 4-OH-2-Cl–C6H3– 4.26 4.22 3.896 H 4-NH2–C6H4– 4.12 4.10 5.077 H 3-OH–C6H4– 4.03 4.15 3.729 H Br– 3.70 3.49 3.95

10 H 3-Furyl 3.59 3.55 3.0811 H 4-Pyridinyl 3.18 3.53 2.5312 H 3-Thienyl 3.03 3.13 3.1114 H 4-OBn–C6H4– 2.93 - 2.6115 H C6H5– 2.89 2.93 2.7916 H 2-Cl–C6H4– 2.87 - 1.9617 H (E)-CH3CH=CH– 2.61 2.68 3.0719 H 4-tBu–C6H4– 2.42 - 1.9720 H Bn– 2.38 - 2.3821 H 4-F–C6H4– 2.30 2.72 2.8022 H 3,5-Cl2–C6H3– 2.27 2.24 2.7623 H 4-CF3O–C6H4– 2.26 - 1.8324 H 4-MeS–C6H4– 2.14 - 1.7825 H C6H5(CH2)2– 2.07 - 2.4926 H 4-CF3–C6H4– 1.98 - 1.9727 H 3-Py 1.96 1.66 1.5528 H 4-Me2N–C6H4– 1.91 - 1.9129 H 2-OH-5-Pyridinyl 1.78 1.69 2.4930 H 4-Me–C6H4– 1.77 - 2.3331 H 4-CN–C6H4– 1.69 - 1.7633 H 4-NO2–C6H4– 1.00 - 1.0234 H 4-MeO–C6H4– 1.00 - 1.8235 H 4-Et–C6H4– 1.00 - 1.7536 H 4-Cl–C6H4– 1.00 - 1.9838 4-NH2–C6H4– Cl 5.10 4.68 5.3939 4-OH–C6H4– Cl 5.00 4.96 4.9940 3-Furyl– Cl 4.96 4.90 4.41

Table 1: (Continued)

Compound R1 R2

log(106 ⁄ IC50)

Exp.

CoMSIA1-EAPred.

CoMSIA2-SDAPred.

41 4-Et–C6H4– Cl 4.92 5.16 4.7942 4-Me–C6H4– Cl 4.85 4.75 4.7443 4-NO2–C6H4– Cl 4.74 4.67 4.5944 4-F–C6H4– Cl 4.66 4.70 4.6445 4-BnO–C6H4– Cl 4.64 4.61 4.7346 4-Pyridinyl Cl 4.30 4.24 4.6047 Br– Cl 4.26 4.39 4.0348 2-Furyl– 4-OH–C6H4– 4.85 4.82 4.3549 3-Pyridinyl– 4-OH–C6H4– 3.83 3.89 4.2450 4-Et–C6H4– 4-OH–C6H4– 4.31 4.44 4.5951 3-Furyl– 4-OH–C6H4– 4.01 4.11 4.3453 4-OBn–C6H4– 4-OBn–C6H4– 2.00 - 1.8254 C6H5– 4-OH–C6H4– 4.26 4.42 4.5055 C6H5– C6H5– 2.76 2.82 3.2356 Br– 4-OH–C6H4– 5.30 5.00 3.8957 5.22 5.39 4.62Test set2 H Cl 3.45 4.06 3.663 H 3,4-(OH)2–C6H3– 4.57 3.20 4.768 H CF3– 3.71 3.27 3.51

13 H 2-NH2-5-pyridinyl 2.96 1.94 3.6818 H 3,5-F2–C6H3– 2.54 1.87 2.7732 4.92 4.80 4.6037 C6H5– Cl 5.15 4.30 4.4552 4-OH–6H4– 4-OH–C6H4– 4.25 4.61 4.84

Binding Studies and QSAR of GSK3b Inhibitors

Chem Biol Drug Des 2011; 78: 631–641 633

had interactions in the inner part of GSK3b active site with thehinge region, while compounds with MI-B and MI-C have interac-tions in the outer part of the active site. In general, compoundswith MI-B and MI-C are less active with log(106 ⁄ IC50) < 3; in thissense, the modes of interaction MI-B and MI-C seem to be lessfavorable than MI-A.

Our docking experiments can explain the poor activity of some com-pounds in the current data set. When the inhibitor has an orienta-tion MI-A, the substituent at position 6 of indazole ring is locatedin the pocket between Lys85, Glu97, Met101, Leu132, Cys199, andAsp200. Phenyl, 4-hydrophyphenyl, and 4-aminophenyl are toleratedin this pocket, but the presence of bigger substituents is notallowed. Compounds with 4-OBn-phenyl, 4-tBu-phenyl, and 4-CF3-

phenyl groups having orientation MI-B, and compounds containing4-MeS-phenyl, benzyl, 4-CF3O-phenyl, phenylethyl, 4-Me2N-phenyl,4-Me-phenyl, 4-CN-phenyl, etc. having orientation MI-C, cannotestablish interactions with the hinge region. This is because thesegroups cannot be accommodated within the above-mentionedpocket. As a result, these compounds do not have a good potencyas GSK3b inhibitors.

GSK3b inhibitor selectivity by analyzingdocking simulationsThe issue of selectivity in kinase active molecules is an importanttopic. Our docking simulations provide meaningful data that can beused to orient future design of selective inhibitors. With this inmind, we identified the residues responsible for the differencesbetween active sites of GSK3b and CDK2. We considered the inhib-itors that have the orientation MI-A in our discussion, because themost active inhibitors have this orientation. In general, GSK3b andCDK2 have similar residues in the active site, the main differencesare found in the region at the entrance of the ATP pocket and thehydrophobic pocket (15).

Lesuisse et al. analyzed a crystal structure between compound 1

and human CDK2 (authors did not deposit it in PDB) (8). In thisstructure, the carboxylic acid chain was pointing toward the solventarea at the entrance of the ATP pocket. Authors observed that thiscarboxylic acid did not have HB interactions with residue Lys89 inCDK2 and decided that the carboxylic acid chain contributed littleto the activity in GSK3b because it sits in the solvent area. Theyreplaced this group by butyryl group that orients toward the solventaccording to the crystal structures of compounds 37 and 54 inCDK2 and our docking results in GSK3b. Lys89 forms a salt bridgewith Asp86 in CDK2, and this can be the reason that carboxylicacid chain of the inhibitor does not form HB with Lys89; however,Lys89 ⁄ Asp86 is replaced by Arg141 ⁄ Thr138 in GSK3b. According toour docking results, the guanidine in side chain of Arg141 forms HBwith carboxylic acid of compound 1. The salt bridge observed inCDK2 does not exist in GSK3b because of the presence of threo-nine instead of aspartate; therefore, arginine is available as HBdonor. This characteristic has been exploited in previous works toimprove GSK3b ⁄ CDK2 selectivity (16), and crystal structure ofGSK3b inhibitors forming HB interactions with Arg141 has beenreported (7).

The hydrophobic pocket in CDK2 has a large gatekeeper residue(Phe80); meanwhile, GSK3b has a medium-sized gatekeeper residue(Leu132). When the gatekeeper residue has a bulky side chain (likephenylalanine), the back cavity is small and delimited by b-sheets 4and 6 of the N-terminal lobe and the gatekeeper side chain itself;however, when the gatekeeper is small or medium sized (like leu-cine), its side chain no longer limits the size of the cavity thatexpands toward b-sheet 5 and the a C-helix. C-Helix contains theconserved glutamate (Glu97 in GSK3b), which can establish HBinteractions with kinase inhibitors that have a leucine residue asthe gatekeeper (17). The crystallized structure of the complexbetween compound 54 and CDK2 shows that hydrophobic pocket inCDK2 can accommodate phenyl groups, but 4-OH-phenyl group doesnot interact with the conserved glutamate (Glu51 in CDK2). Our

A

B

Figure 1: Conformational comparison of compounds 37 (A) and54 (B) from the crystal structures in CDK2–inhibitor complexes(CDK2 in gray and inhibitors in magenta) and from docking resultsin GSK3b (GSK3b in violet and inhibitors in yellow). (For interpreta-tion of the references to color in this figure legend, the reader isreferred to the web version of this article.)

Caballero et al.


docking simulations in GSK3b show that phenolic and anilinicgroups at position 6 of the 3-amino-1H-indazole scaffold (com-pounds 3–6, R2 substituent) interact with Glu97, and this canexplain the increased potency of these compounds with respect tocompounds 1 and 9. Lesuisse et al. (8) could not see this interac-tion in the crystal structure between compound 4 and Aurora2kinase (pdb code 3LAU), but in this structure, residues around theDFG motif of Aurora2 were displaced by packing interactions andthe conserved glutamic acid located far from the hydrophobicpocket. Furthermore, these authors reported that compound 4 didnot show any significant inhibition of Aurora2 (IC50 > 30 lM). Thedifference between CDK2 and GSK3b gatekeeper residues alsoexplains the increment of selectivity between compound 37, havinga hydrogen atom at position 7 of the 3-amino-1H-indazole scaffold,and compounds 57 and 32 (having F and Cl at this position,respectively) (8). Docking shows that substituent at position 7 ofthe 3-amino-1H-indazole scaffold is proximal to the side chain ofthe gatekeeper residue; the inclusion of Cl substituent at this posi-tion results in a steric clash with Phe80 in CDK2, but is toleratedwhen the gatekeeper residue is a leucine.

Substituents at position 5 of the 3-amino-1H-indazole scaffold (R1

substituent) lead to highly active compounds against GSK3b, butour docking simulations show that these groups do not interactwith residues that bring selectivity against CDK2. In fact, Les-uisse et al. reported that the highly potent compound 37 (with

a phenyl substituent at position 5) also displayed potent inhibi-tion of CDK2.

3D-Quantitative structure–activity relationshipresults by using binding site constraintsCoMSIA models were developed by using docking aligned confor-mations with MI-A aligned in GSK3b active site. Forty-one com-pounds have the orientation MI-A, and they were divided intotraining (33 compounds) and test sets (eight compounds).

First, we applied comparative molecular field analysis (CoMFA) (18)following the protocol described in previous works (19). The predict-ability of the models was assessed by considering Q2 values. Wefound that CoMFA including steric or electrostatic fields and includ-ing both fields together led to statistically unacceptable modelswith Q2 < 0.5. These models are reported in Table 2. Then, wedeveloped CoMSIA models by including one field, and further, wecombined these fields and analyzed the statistical quality of hybridmodels.

The stepwise development of CoMSIA models using different fieldsis also presented in Table 2. CoMSIA models using steric, electro-static, or hydrophobic fields also had Q2 < 0.5; however, modelsincluding HB donor and acceptor fields had Q2 = 0.522 and 0.520,respectively. We also combined several fields for exploring the

Asp133

Pro136

Ile62

Asp133

Val135

Pro136

Pro136

Ile62

Val135

Asp133

Pro136

Ile62

Val135

Asp133

A B

C D

Figure 2: Predicted binding conformations of all investigated inhibitors 1–58 and their alignment. (A) Compounds with MI-A, (B) com-pounds with MI-B, (C) compounds with MI-C, and (D) compound 53. Non-polar hydrogens are omitted for clarity.



quality of hybrid models. The best CoMSIA model describing GSK3binhibition (CoMSIA1-EA) was obtained by combining electrostaticand HB acceptor fields, with Q2 = 0.639. We evaluated whether theaddition of other fields produces an improvement in the internalvalidation of the model CoMSIA1-EA, but models including morefields had lower Q2 values (Table 2). The model CoMSIA1-EA wasderived by using six components and showed contributions of theelectrostatic field of 70.8% and an HB acceptor contribution of29.2%. In addition, it explains 96.9 of the variance and has a lowstandard deviation (SD = 0.198) and a high Fischer ratio(F = 136.22). The predictions of log(106 ⁄ IC50) values for the 33 3-amino-1H-indazoles from the training set using model CoMSIA1-EAare shown in Table 1. The correlation between the calculated andexperimental values of log(106 ⁄ IC50) (from training and LOO cross-validation) is shown in Figure 3. According to these plots, the modelis able to discriminate between the most active and the less activecompounds.

We also used model CoMSIA1-EA to predict the GSK3b inhibitoryactivities of the test set compounds. The values are given inTable 1, and correlation between the calculated and experimentalvalues is represented in Figure 3. This analysis reveals that the pro-posed model also predicted adequately all the compounds in thetest set.

The contour plots of the CoMSIA electrostatic and HB acceptorfields are presented in Figure 4 for the best model CoMSIA1-EA. Toaid in visualization, the highly active compound 56 is displayed inthe maps. In general, the colored isopleths in the map surround all

Table 2: Results of the 3D-QSAR methods using several different field combinations and binding site constraints alignmenta

Model NC Q2 Scv

Field contributions

Steric Electrostatic HydrophobicH-bonddonor

H-bondacceptor

CoMFA1-S 4 0.395 0.845 1CoMFA1-E 2 0.116 0.986 1CoMFA1-SE 6 0.459 0.828 0.424 0.576CoMSIA1-S 5 0.497 0.784 1CoMSIA1-E 2 0.348 0.847 1CoMSIA1-H 2 0.300 0.878 1CoMSIA1-D 2 0.522 0.725 1CoMSIA1-A 4 0.520 0.751 1CoMSIA1-SE 1 0.362 0.824 0.395 0.605CoMSIA1-SD 4 0.478 0.784 0.364 0.636CoMSIA1-SA 5 0.532 0.756 0.389 0.611CoMSIA1-ED 2 0.505 0.738 0.636 0.364CoMSIA1-EA 6 0.646 0.677 0.708 0.292

CoMSIA1-DA 3 0.579 0.693 0.640 0.360CoMSIA1-SEA 6 0.585 0.726 0.110 0.641 0.249CoMSIA1-SDA 3 0.551 0.715 0.268 0.458 0.274CoMSIA1-EDA 6 0.585 0.726 0.550 0.227 0.223CoMSIA1-SEDA 6 0.538 0.766 0.088 0.505 0.207 0.200CoMSIA1-SHDA 3 0.505 0.751 0.159 0.321 0.335 0.185CoMSIA1-ALL 5 0.470 0.805 0.070 0.370 0.207 0.199 0.154

QSAR, quantitative structure–activity relationship.aNC is the number of components from partial least squares (PLS) analysis, Q2 and SCV are the correlation coefficient and standard deviation of the leave-one-out (LOO) cross-validation, respectively. The best CoMSIA models are indicated in boldface.

1 2 3 4 5 61

2

3

4

5

6

Pre

dict

ed lo

g (1

06/IC

50

Experimental log (106/IC50

)

)

Figure 3: Scatter plot of the experimental activities versus pre-dicted activities for model CoMSIA1-EA [(d) training set predictions(s) leave-one-out (LOO) cross-validated predictions ( ) test set pre-dictions].

Caballero et al.


lattice points where the QSAR strongly associated changes in thecompounds' field values with changes in biological potency. Thecontour plots should not be overinterpreted as receptor maps; how-ever, the use of docking structures as conformations for derivingthe model allows an interpretation of the relevant structural fea-tures inside the receptor's active site (20).

In Figure 4A, blue isopleths indicate regions where positivecharges favor the activity or negative charges disfavor the activity.A blue isopleth covering positions 4–7 of indazole ring suggests

that positively charged groups are tolerated at these positions.Regions of blue contour located between Glu97 and Met101 andbetween Lys85 and Asp200 indicate that positively charged groupsat positions 3 and ⁄ or 5 of 4-hydroxyphenyl substituent at position6 of indazole ring favor the inhibitory activity. Another blue iso-pleth close to Gly65 indicates that positively charged groups atposition 5 of indazole ring favor the inhibitory activity. On theother hand, red isopleths indicate regions where negative chargesfavor the activity or positive charges disfavor the activity (Fig-ure 4A). Red isopleth located at position 4 of 4-hydroxyphenyl sub-stituent at position 6 of indazole ring indicates thatelectronegative substituents such as hydroxyl group favor the inhib-itory activity.

In Figure 4B, magenta isopleths indicate regions where HB accep-tors enhance the activity, and red isopleths indicate regions whereHB acceptors decrease the activity. Big magenta isopleth in front ofbutyryl group indicates that acceptor group is required at this posi-tion. Another two magenta isopleths, one located between thepocket formed by Ala83, Val110, Leu132, and Asp133, and anotherone close to Glu97, suggest that HB acceptor groups at these posi-tions can enhance the inhibitory activity. Finally, a red isoplethbetween Lys85 and Asp200 indicates that HB acceptor groups atthis zone decrease the activity.

3D-Quantitative structure–activity relationshipby using template-based alignmentThe development of an interpretative 3D-QSAR model allows to useit as a platform for retrospective hypotheses, which could guide inrational synthesis of active compounds. Interpretation of significantinteraction regions becomes more meaningful when alignment isconstrained by a binding site (21).

We applied CoMFA and CoMSIA methods on compounds with theorientation MI-A by considering one orientation in a binding siteconstraint. As a comparison, we also constructed the same modelsby using template-based alignment. For this, all the moleculeswere aligned by an atom-by-atom least-square fit. The 3-amino-1H-indazole scaffold of the active compound 56 was used as a tem-plate. All the models offered no predictive statistical values(Q2 < 0.5).

The use of a molecular template is the only choice when there isno target protein to act as a constraint. The structures of kinasesare available by crystallographic data or by homology modeling. Inthe absence of the target model and docking experiments, com-pounds with different binding mode had been aligned with theremaining compounds. The alignment of compounds with a commonscaffold assumes that these compounds have the same bindingmode. Noteworthy, the number of compounds with a common scaf-fold that have a completely different binding mode has increasedwith greater availability of crystal structures of inhibitor–enzymecomplexes (20,22). To investigate the consequences of including allthe compounds in CoMFA and CoMSIA models, template-basedalignment of all the compounds was achieved and 3D-QSAR modelswere constructed. The models were trained by using 49 compoundsbecause compounds 14, 16, 19, 20, 23–26, 28, 30, 31, 33–36,

Ala83

Val110

Ile62 Gly65 Ser66

Val70 Lys85

Glu97

Met101

Leu132

Asp133

Tyr134

Val135

Thr138

Gln185

Asn186

Leu188 Cys199

Asp200

Phe201

Gly202

Ala83

Val110

Ile62 Gly65 Ser66

Val70 Lys85

Glu97

Met101

Leu132

Asp133

Tyr134

Val135

Thr138

Gln185

Asn186

Leu188 Cys199

Asp200

Phe201

Gly202

B

B

B

B

R

MM

M

R

A

B

Figure 4: CoMSIA contour maps for GSK3b inhibitors derivingfrom model CoMSIA1-EA. The amino acid residues located close tothe binding pocket of GSK3b are represented for comparing theirposition with the position of isopleths derived from the model.Compound 56 is shown inside the fields. (A) Electrostatic field: blueisopleths indicate regions where positive charges favor the activityor negative charges disfavor the activity, and red isopleths indicateregions where negative charges favor the activity or positivecharges disfavor the activity. (B) Hydrogen bond (HB) acceptor field:magenta isopleths indicate regions where HB acceptors enhancethe activity, and red isopleths indicate regions where HB acceptorsdecrease the activity.



and 53 were incorporated into the training set. The results are pro-vided in Table 3. The majority of models were predictive accordingto internal validation statistics. The best model describing GSK3binhibition CoMSIA2-SDA has Q2 = 0.672 and included steric, HBdonor, and HB acceptor fields with contributions of 41.6, 32.3, and26.1%, respectively. In addition, it explains 84.9 of the variance,has a standard deviation SD = 0.532, and has a Fischer ratio ofF = 60.22. The predictions of log(106 ⁄ IC50) values all the 49 com-pounds from the training set using model CoMSIA2-SDA are shownin Table 1. The correlation between the calculated and experimentalvalues of log(106 ⁄ IC50) (from training and LOO cross-validation) isshown in Figure 5. According to these plots, the model is able todiscriminate between the most active and the less active com-pounds. We also used model CoMSIA2-SDA to predict the GSK3binhibitory activities of the test set compounds. The values are givenin Table 1, and correlation between the calculated and experimentalvalues is represented in Figure 5. This analysis reveals that the pro-posed model also predicted adequately all the compounds in thetest set.

It is interesting, but not surprising, that the application of 3D-QSARmethodologies by using binding site constraint and template-basedalignments leads to different models containing different fields. Thisparadigm has been previously identified in 3D-QSAR. In a criticalreview of 3D-QSAR applications, Doweyko (23) observed that tem-plate-based models led to distorted conclusions when comparedwith those developed using a binding site constraint. This authortested a fictional set and constructed CoMFA models by using tem-plate and binding site alignments. Both models pointed to signifi-cantly different features, but the one based on binding site

Table 3: Results of the 3D-QSAR using several different field combinations and template-based alignmenta

Model NC Q2 Scv

Field contributions

Steric Electrostatic Hydrophobic H-bond donor H-bond acceptor

CoMFA2-S 3 0.644 0.805 1CoMFA2-E 5 0.518 0.959 1CoMFA2-SE 2 0.624 0.819 0.576 0.424CoMSIA2-S 1 0.537 0.899 1CoMSIA2-E 3 0.548 0.908 1CoMSIA2-H 3 0.319 1.114 1CoMSIA2-D 3 0.503 0.952 1CoMSIA2-A 3 0.559 0.896 1CoMSIA2-SE 2 0.602 0.843 0.352 0.648CoMSIA2-SD 4 0.635 0.826 0.550 0.450CoMSIA2-SA 2 0.589 0.856 0.481 0.519CoMSIA2-ED 3 0.592 0.862 0.748 0.252CoMSIA2-EA 4 0.567 0.899 0.777 0.223CoMSIA2-DA 4 0.588 0.877 0.481 0.519CoMSIA2-SEA 2 0.594 0.851 0.305 0.484 0.211CoMSIA2-SDA 4 0.672 0.782 0.416 0.323 0.261

CoMSIA2-EDA 4 0.645 0.814 0.627 0.197 0.176CoMSIA2-SEDA 4 0.659 0.797 0.246 0.409 0.209 0.136CoMSIA2-SHDA 2 0.548 0.897 0.256 0.276 0.234 0.235CoMSIA2-ALL 2 0.562 0.884 0.197 0.310 0.218 0.136 0.139

QSAR, quantitative structure–activity relationship.aNC is the number of components from partial least squares (PLS) analysis, Q2 and SCV are the correlation coefficient and standard deviation of the leave-one-out (LOO) cross-validation, respectively. The best CoMSIA models are indicated in boldface.

1 2 3 4 5 6

1

2

3

4

5

6

Pre

dict

ed lo

g (1

06/IC

50

Experimental log (106/IC50)

)

Figure 5: Scatter plot of the experimental activities versus pre-dicted activities for model CoMSIA2-SDA [(d) training set predic-tions (s)leave-one-out (LOO) cross-validated predictions ( ) test setpredictions].

Caballero et al.


constraints yield results consistent with the known structure. Withthis in mind, and aware of the existence of compounds with a com-mon scaffold that have a completely different binding mode (20,22),we delimited the interpretive scope of each model. The model con-structed by using binding site alignment (CoMSIA1-EA) shows therelevant pharmacophoric features required to best match in thebinding site, and consequently, the desired features that character-ize the most potent compounds are obtained. On the other hand,the model constructed by using template-based alignment (CoM-SIA2-SDA) shows the requirements of the substituents on a com-mon scaffold to improve the biological activity. These interpretivetasks sound similar, but are not the same.

Once stated the interpretive task of the model CoMSIA2-SDA, thecontour plots derived from this model were interpreted. CoMSIAsteric, HB donor, and HB acceptor fields are presented in Figure 6.To aid in visualization, the highly active compound 56 is displayedin the maps. Figure 6A shows the aligned molecules used for3D-QSAR analysis. In Figure 6B, green and yellow isopleths indicateregions where bulky groups favored and disfavored the activity,respectively. A large green contour at position 6 of the 3-amino-1H-indazole indicates that bulkier substituents are desired at thisposition. In fact, the presence of phenyl substituents seems to beadequate for having a good inhibitor [compounds 3–7 havelog(106 ⁄ IC50) values above 4]; however, analogous compounds withsmall substituents are less active [for instance, compounds 2 and 9

have log(106 ⁄ IC50) values below 4]. There is a yellow contour at

position 5 of the 3-amino-1H-indazole, which indicates that smallergroups are desired at this position. In fact, the presence of Cl sub-stituent at this position in compounds 37, 40, 41, and 45

[log(106 ⁄ IC50) values above 4.5] is preferable instead of phenylgroup in analogous compounds 54, 51, 50, and 53 [log(106 ⁄ IC50)values below 4.5]. Finally, there is a green contour in the regionlocated in the outer part from position 5 of 3-amino-1H-indazole,which suggests the inclusion of a bulky group in this zone toimprove the inhibitory activity. In fact, compound 14

[log(106 ⁄ IC50) = 2.93] has a benzyl group in this region and has bet-ter activity than compounds with smaller substituents such as com-pounds 18, 19, 21–24, 26, 28, 30, 31, and 33–36

[log(106 ⁄ IC50) < 2.6].

In Figure 6C, cyan and purple isopleths indicate regions where HBdonors favored and disfavored the activity, respectively. Two bigcyan isopleths indicate that HB donor groups are desired in theouter part from positions 5 and 6 of the 3-amino-1H-indazole andclose to amino group of the 3-amino-1H-indazole. A purple isoplethclose to carbonyl of the butyryl group in compound 56 suggeststhat HB donor groups are not favorable in this region.

In Figure 6D, magenta and red isopleths indicate regions whereHB acceptors favored and disfavored the activity, respectively. Redisopleth close to the carbonyl oxygen of the butanamido group incompound 56 suggests that HB acceptor group is essential at thisposition; meanwhile a magenta isopleth close to amino group of

G

G

Y

C

C

P M

R

R

A

C D

B

Figure 6: CoMSIA contour maps for GSK3b inhibitors deriving from model CoMSIA2-SDA. Compound 56 is shown inside the fields.(A) Steric field: green and yellow isopleths indicate regions where bulky groups favored and disfavored the activity, respectively. (B) Hydrogenbond (HB) donor field: cyan and purple isopleths indicate regions where HB donors favored and disfavored the activity, respectively.(C) HB acceptor field: magenta isopleths indicate regions where HB acceptors enhance the activity, and red isopleths indicate regions whereHB acceptors decrease the activity.



the butanamido suggests that HB acceptor groups are not favor-able in this region. The red isopleth close to the position 3 of thephenyl group at position 6 of the 3-amino-1H-indazole indicatesthat HB acceptor groups disfavor the GSK3b inhibitory activity. Infact, the presence of a pyridinic nitrogen at this position in com-pounds 27 and 29 deteriorates the activity [log(106 ⁄ IC50) valuesbelow 2.0].

Conclusions

In this paper, we report the structural features and structure–activ-ity relationship of GSK3b inhibitors. We selected a data set of 573-amino-1H-indazoles and studied the 3D positioning of them insidethe active site of GSK3b by docking experiments. We obtained thatthe most active compounds have interactions with the hinge regionresidues of GSK3b active site, but some of the less active com-pounds have different orientations and cannot adequately penetratethe active site.

In addition, 3D-QSAR approaches were applied to derivequantitative relationships between the structure of the 3-amino-1H-indazoles compounds and their GSK3b inhibitory activity. For this,we used binding site constraint and template-based alignments. Inthe first approach, we used docked structures with same orientationfor deriving the models. In the latter approach, we used all thecompounds aligned in the 3-amino-1H-indazole scaffold as a tem-plate. We found different CoMSIA models by using both alignmentsto explain the inhibitory activity. The relevant fields in bothapproaches describe the inhibitory activity of the studied com-pounds and allow to draw conclusions concerning the most appro-priate molecular features of 3-amino-1H-indazoles as GSK3binhibitors, which could be used in the design of new inhibitors.

Acknowledgements

J.C. thanks 'Becas Universidad de Talca' for financial supportthrough doctoral fellowship. Part of this work has been supportedby Fondecyt, Grant 11090431, Proyecto 374 interno DI-13-10 ⁄ R,Universidad Andr�s Bello.

References

1. Pap M., Cooper G.M. (1998) Role of glycogen synthase kinase-3in the phosphatidylinositol 3-kinase ⁄ Akt cell survival pathway. JBiol Chem;273:19929–19932.

2. Li Y.C., Xi D., Roman J., Huang Y.Q., Gao W.J. (2009) Activation ofglycogen synthase kinase-3 beta is required for hyperdopamineand D2 receptor-mediated inhibition of synaptic NMDA receptorfunction in the rat prefrontal cortex. J Neurosci;29:15551–15563.

3. Luo J. (2009) GSK3b in ethanol neurotoxicity. Mol Neurobi-ol;40:108–121.

4. Wagman A.S., Johnson K.W., Bussiere D.E. (2004) Discovery anddevelopment of GSK3 inhibitors for the treatment of type 2 dia-betes. Curr Pharm Des;10:1105–1137.

5. Neary J.T., Kang Y. (2006) P2 purinergic receptors signal to glyco-gen synthase kinase-3b in astrocytes. J Neurosci Res;84:515–524.

6. Klamer G., Song E., Ko K.H., O'Brien T.A., Dolnikov A. (2010)Using small molecule GSK3 inhibitors to treat inflammation. CurrMed Chem;17:2873–2881.

7. Bertrand J.A., Thieffine S., Vulpetti A., Cristiani C., Valsasina B.,Knapp S., Kalisz H.M., Flocco M. (2003) Structural characteriza-tion of the GSK-3b active site using selective and non-selectiveATP-mimetic inhibitors. J Mol Biol;333:393–407.

8. Lesuisse D., Dutruc-Rosset G., Tiraboschi G., Dreyer M.K., Mai-gnan S., Chevalier A., Halley F., Bertrand P., Burgevin M.C., Quar-teronet D., Rooney T. (2010) Rational design of potent GSK3binhibitors with selectivity for Cdk1 and Cdk2. Bioorg Med ChemLett;20:1985–1989.

9. Kubinyi H. (1997) QSAR and 3D QSAR in drug design. Part 1.Methodology. Drug Discov Today;2:457–467.

10. Alzate-Morales J.H., Caballero J., Vergara-Jaque A., Gonz�lez-Nilo F.D. (2009) Insights into the structural basis of N2 and O6substituted guanine derivatives as cyclin-dependent kinase 2(CDK2) inhibitors: prediction of the binding modes and potencyof the inhibitors by docking and ONIOM calculations. J Chem InfModel;49:886–899.

11. Alzate-Morales J.H., Vergara-Jaque A., Caballero J. (2010) Com-putational study on the interaction of N1 substituted pyrazolederivatives with B-Raf Kinase: an unusual water wire hydrogen-bond network and novel interactions at the entrance of theactive site. J Chem Inf Model;50:1101–1112.

12. Abagyan R., Totrov M., Kuznetsov D. (1994) ICM - A newmethod for protein modeling and design: applications to dockingand structure prediction from the distorted native conformation.J Comput Chem;15:488–506.

13. An J., Totrov M., Abagyan R. (2005) Pocketome via comprehen-sive identification and classification of ligand binding envelopes.Mol Cell Proteomics;4:752–761.

14. Bush B.L., Nachbar R.B. (1993) Sample-distance partial leastsquares: PLS optimized for many variables, with application toCoMFA. J Comput Aided Mol Des;7:587–619.

15. Vulpetti A., Crivori P., Cameron A., Bertrand J., Brasca M.G.,D'Alessio R., Pevarello P. (2005) Structure-based approaches toimprove selectivity: CDK2)GSK3b binding site analysis. J ChemInf Model;45:1282–1290.

16. Witherington J., Bordas V., Haigh D., Hickey D.M.B., Ife R.J.,Rawlings A.D., Slingsby B.P., Smith D.G., Ward R.W. (2003) 5-Aryl-pyrazolo[3,4-b]pyridazines: potent inhibitors of glycogen syn-thase kinase-3 (GSK-3). Bioorg Med Chem Lett;13:1581–1584.

17. Tong Y., Claiborne A., Stewart K.D., Park C., Kovar P., Chen Z.,Credo R.B., Gu W.Z., Gwaltney S.L. II, Judge R.A., Zhang H.,Rosenberg S.H., Sham H.L., Sowin T.J., Lin N.H. (2007) Discoveryof 1,4-dihydroindeno[1,2-c]pyrazoles as a novel class of potentand selective checkpoint kinase 1 inhibitors. Bioorg MedChem;15:2759–2767.

18. Cramer R.D., Patterson D.E., Bunce J.D. (1988) Comparativemolecular field analysis (CoMFA). 1. Effect of shape on bindingof steroids to carrier proteins. J Am Chem Soc;110:5959–5967.

19. Alzate-Morales J., Caballero J. (2010) Computational study ofthe interactions between guanine derivatives and cyclin-depen-dent kinase 2 (CDK2) by CoMFA and QM ⁄ MM. J Chem InfModel;50:110–122.

Caballero et al.


20. Oza V., Ashwell S., Brassil P., Breed J., Deng C., Ezhuthachan J.,Haye H. et al. (2010) Discovery of a novel class of triazolonesas Checkpoint Kinase inhibitors–hit to lead exploration. BioorgMed Chem Lett;20:5133–5138.

21. Garriga M., Caballero J. (2011) Insights into the structure ofurea-like compounds as inhibitors of the juvenile hormone epox-ide hydrolase (JHEH) of the tobacco hornworm Manduca sexta:analysis of the binding modes and structure-activity relationshipsof the inhibitors by docking and CoMFA calculations. Chemo-sphere;82:1604–1613.

22. Fischmann T.O., Hruza A., Duca J.S., Ramanathan L., MayhoodT., Windsor W.T., Le H.V., Guzi T.J., Dwyer M.P., Paruch K., Doll

R.J., Lees E., Parry D., Seghezzi W., Madison V. (2008) Struc-ture-guided discovery of cyclin-dependent kinase inhibitors. Bio-polymers;89:372–379.

23. Doweyko A.M. (2004) 3D-QSAR illusions. J Comput Aided MolDes;18:587–596.

Notes

aMolecular Editor, version 2.5, La Jolla, CA: Molsoft LLC, 2006.b

ICM, version 3.4-8, La Jolla, CA: Molsoft LLC, 2006.cSYBYL, version 7.3, 1699 South Hanley Rd., St. Louis, MO 63144,USA: Tripos Inc., 2006.



Binding Studies and Quantitative Structure–Activity Relationship of 3-Amino-1H-Indazoles as...

Documents

Transcript of Binding Studies and Quantitative Structure–Activity Relationship of 3-Amino-1H-Indazoles as...