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Fusion of Structure and Ligand based methods for identification of novel CDK2 Inhibitors Priya Mahajan, Gousia Chashoo, Monika Gupta, Amit Kumar, Parvinder Pal Singh, and Amit Nargotra J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.7b00293 • Publication Date (Web): 19 Jul 2017 Downloaded from http://pubs.acs.org on July 21, 2017

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Journal of Chemical Information and Modeling

Fusion of Structure and Ligand based Methods for Identification of Novel CDK2 Inhibitors Priya Mahajan,†,∥ Gousia Chashoo,‡ Monika Gupta,† Amit Kumar,† Parvinder Pal Singh,§,∥ Amit Nargotra*,†,∥

†

Discovery Informatics, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu

180001, India ‡

Cancer Pharmacology, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu 180001, India

§

Medicinal Chemistry, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu 180001, India

Academy of Scientific and Innovative Research, CSIR-Indian Institute of Integrative Medicine, Canal Road, Jammu 180001, India

∥

GRAPHICAL ABSTRACT

1 ACS Paragon Plus Environment


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ABSTRACT Cyclin dependent kinases play a central role in cell cycle regulation which makes them a promising target with multifarious therapeutic potential. CDK2 regulates various events of the eukaryotic cell division cycle, and the pharmacological evidences indicates that over expression of CDK2 causes abnormal cell-cycle regulation, which is directly associated with hyper proliferation of cancer cells. Therefore, CDK2 is regarded as a potential target molecule for anti-cancer medication. Thus to decline CDK2 activity by potential lead compounds has proved to be an effective treatment for cancer. The availability of a large number of X-ray crystal structures and known inhibitors of CDK2 provides a gateway to perform efficient computational studies on this target. With the aim to identify new chemical entities from commercial libraries, with increased inhibitory potency for CDK2, ligand and structure based computational drug designing approaches were applied. A drug-like library of 50,000 compounds from ChemDiv and ChemBridge database was screened against CDK2 and 110 compounds were identified using the parallel application of these models. On in vitro evaluation of 40 compounds, 7 compounds were found to have more than 50% inhibition at 10µM. MD studies of the hits revealed the stability of these inhibitors and pivotal role of Glu81 and Leu83 for binding with CDK2. The overall study resulted in the identification of 4 new chemical entities possessing CDK2 inhibitory activity. 1. INTRODUCTION


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Cyclin-dependent kinase (CDKs) belongs to a class of serine/threonine protein kinases that act as a key regulatory element in cell cycle progression and transcription. CDKs exist in various isoforms and deregulation of CDKs has been known for their large-spectrum therapeutic potential.1 CDK2 is an extensively studied target for its antitumor activity due to its significant contributions in signal transduction pathways involved in cell cycle regulation. It plays an important role in cell division cycle by regulating multiple events i.e. centrosome duplication, DNA synthesis, G1-S transition, and modulation of G2 progression. Anomalous expression of CDK2 at the restriction point may leads to abnormal cell-cycle regulation, which has been identified in many human cancers.2 Development of efficient drugs for CDK2 to combat the cancer is the need of hour. CDK2 have been intensively investigated as a therapeutic target for cancer from many decades, and many inhibitors have surfaced out, which belong to the diverse scaffolds viz. indazoles, thiazoles, isothiazoles, acylaminopyrazoles, cabolines, thiazole, pyrimidines etc.3 To date, many CDK2 inhibitors i.e. SNS-032 (BMS-387032), Flavopiridol (Alvocidib) HCl, Milciclib (PHA-848125), PHA848125, BAY 10-00394, AT7519, SCH 727965 and Roscovitine (Seliciclib, CYC274) are in clinical trials for anti-cancer studies.3 Although several CDK2 inhibitors have been investigated clinically for their potential as anti-cancer agents, but none approved for clinical use due to isoform selectivity, solubility, toxicity etc. issues. Still, many pharmaceutical companies such as Novartis, AstraZeneca etc. are putting lots of efforts in designing new and potential inhibitors for CDK2. Day by day the number of crystal structures of CDK2 and its inhibitors are increasing. The availability of such data makes CDK2 a very interesting target for computational studies. In the present study, diverse CDK2 inhibitors and X-ray crystal structure of protein in complex with inhibitors was explored for designing structure and ligand based in silico models. Ligand based substructure similarity search and Pharmacophore based 3D QSAR models were built from known CDK2 inhibitors which identifies compounds that share similar structural properties to known actives. Structure based e-pharmacophore models and docking studies were performed guided by the crystal structure information of CDK2 in complex with bound inhibitors, which identify compounds that complement with the active site of the target protein. The developed in silico models were applied in parallel to screen a drug-like library of 50,000 compounds procured from ChemDiv and ChemBridge library. Thereafter, based on the interaction of the in silico hits with the key residues, energy parameter calculation and structural diversity, few compounds were selected and subjected 3 ACS Paragon Plus Environment


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for biological activity. The results emphasised on the successful application of virtual screening to identify new CDK2 inhibitor scaffolds that can be taken forward for carrying out further medicinal chemistry and optimisation studies for designing selective CDK2 inhibitors. 2. MATERIAL AND METHODS 2.1 Dataset. There are more than 300 X-ray crystal structures of CDK2 reported in PDB4and out of these, nearly 200 are bound with diverse inhibitors. These co-crystallized inhibitors were categorized into 27 diverse scaffolds, and the respective PDB entry formed the dataset for structure based computational studies (structure of these scaffolds with their respective PDB Ids are given in table S1). 26 other CDK2 inhibitors in clinical and pre-clinical studies were collected from Drug Bank5 and Scifinder database6. In addition, CDK2 inhibitors with IC50 less than 100µM were collected from ChEMBL database7, out of which 51 most potent and diverse inhibitors were identified (table S2). This dataset of 77 known CDK2 inhibitors (26 from Drug Bank and Scifinder database, 51 from ChEMBL database)8-39 along with 27 diverse scaffolds identified from co-crystallized inhibitors were considered for ligand based computational studies. In addition, four congeneric series. i.e. Indenopyrazole derivative (Set-A series), pyrazolo[4,3-h]qinazoline-3-carboxamides (Set-B series), pyrazoles with imidazole pyrimidine amides derivatives (Set-C series) and 3-aminopyrazoles (Set-D series) reported as CDK2 inhibitors35-49 were considered to build pharmacophore based 3D-QSAR models (table S3-S6). A library of 50,000 drug-like compounds procured from ChemDiv and ChemBridge database -, and housed in the Institutional compound library of IIIM was used for screening of potent CDK2 inhibitors. This compound library is referred as the Institutional compound library in rest of the article. 2.2 Ligand guided screening strategies 2.2.1 Substructure and Similarity search. Substructure and similarity search identify compounds that contains the substructure of active scaffold/query compound and the compounds which share a similar structure to the query compound respectively. Based on the widely accepted assumption that compounds which share similar structure have similar chemical properties and biological activity, substructure and similarity search of the reported CDK2 inhibitors was carried out. A dataset of 77 CDK2 inhibitors consisting of clinical, preclinical, biological testing compounds were collected from literature (table S2). These 77 inhibitors, along with the 27 scaffolds identified from co-crystallized inhibitors, were


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considered for sub-structure and similarity search using ChemAxon software

with a

similarity threshold of 0.5 i.e. identified compounds must have 50% similarity to the query compounds.50 2.2.2 Pharmacophore based 3D-QSAR modeling. A Pharmacophore model identifies the minimum necessary structure features of inhibitors, that a compound must possess for inhibition of an up regulated enzyme involved in a disease. These structural features are explored to identify potential candidates from large compound databases. Herein, four congeneric series of CDK2 inhibitors were considered to build pharmacophore models for the identification of potent inhibitors from the Institutional compound repository. The reason for building 4 predictive models, considering SetA-D series, was that the compounds attained via screening must have maximum number of common pharmacophore features with respect to each series. The number of compounds considered for building pharmacophore models for each series is given in the table1. Before building the pharmacophore model, inhibitory activity IC50 values of inhibitors were converted into their logarithmic value (pIC50) so as to get the linear co-relation while generating QSAR models . The structure of inhibitors were sketched and minimized at OPLS_2005 force field. Prepared ligands were then used for generating common pharmacophore hypothesis using the Phase module of Schrodinger software. To build pharmacophore models the dataset of compounds from each series was divided into active, moderate active and less active compounds on the bases of activity (table S3-S6). The chemical structure of each compound convey a particular set of pharmacophoric features i.e. H-bond acceptor (A), H-bond donor (D), hydrophobic group (H), negatively charged group (N), positively charged group (P) and aromatic ring (R) calculated using Phase.51 Based on these pharmacophoric features, the pharmacophore models were developed. Each model/hypothesis conveys a particular 3D conformation for a set of active ligands in which compound was going to bind with the receptor. In order to validate the developed pharmacophore hypothesis for the selected series, 3D-QSAR studies were carried out. To build QSAR models, the initial dataset was divided into training and test sets (table1, table S3-S6). The dataset of training set compounds was considered for building 3D-QSAR models and validated through test set compounds. The best hypothesis was selected based on the values of correlation coefficient of the training set (R2) and the internal predictivity coefficient of the test set (Q2) compounds. Table 1. Dataset of compounds taken for building Pharmacophore based 3D-QSAR 5 ACS Paragon Plus Environment


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Sets Set-A Set-B Set-C Set-D

Series Indenopyrazole derivatives pyrazolo[4,3h]qinazoline-3carboxamides Pyrazole- Pyrimidine derivatives 3-aminopyrazole series

No. of Active: Moderate compounds active: Less active in dataset compounds

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Active dataset for pharmacophore modeling

Training: Test set compounds

117

89 : 18 : 10

78

88 : 29

65

46 : 13 : 6

37

50 :15

155

101 : 38 : 16

82

117 : 38

45

33 : 8 : 4

28

34 11

2.3 Structure guided screening strategy 2.3.1 Energy-optimized pharmacophore mapping (e-Pharmacophore). e-Pharmacophore, an amalgamation of molecular docking and pharmacophore modeling is mainly used to screen large compound libraries. Here the 27 crystal structures of CDK2 were considered for building e-Pharmacophore models. As the bioactive conformations of CDK2 co-crystallized ligands were considered for this study, it increases the chance to identify potent CDK2 inhibitors from screening the Institutional compound library having the similar pharmacophore features and interactions within the protein-ligand complexes. Initially these 27 crystal structure, bound with diverse inhibitors, were prepared using the protein preparation wizard of Schrodinger. Further, on these 27 prepared crystal structures their respective co-crystallized inhibitors were docked at their reference position without disturbing their co-ordinates, using XP scoring function of Glide.52 The e-Pharmacophore model generates a receptor-based excluded volume with the energy contribution of each atom present in the ligand calculated through Glide scoring function and pharmacophoric feature of Phase module was utilized to screen the database. The e-Pharmacophore models build on 27 X-ray crystals with their respective PDB Ids are given in figure S1 generated using ePharmacophore mapping of Schrodinger.53 The identified compounds from this study will contribute to important structural features with descriptor which must be important for the affinity as well efficacy of the compound for the therapeutic target. 2.3.2 Molecular Docking. In PDB there are nearly 200 X-ray crystal structures of CDK2 reported with co-crystallized inhibitors. In order to select the most appropriate structure for carrying out the molecular docking studies, Root Mean Square Deviation (RMSD) calculation was performed on 27 representative crystal structures containing different core 6 ACS Paragon Plus Environment

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moieties (table S1). The bound inhibitors were prepared and docked at the active site of CDK2 using XP, SP and HTVS scoring function of Glide.52 RMSD was calculated between bound and docked conformation of the co-crystallized inhibitor. Lesser the RMSD difference between bound and docked conformation, greater would be the chance to attain bioactive conformation of the compounds identified from in silico docking studies. The calculated RMSD of all the 27 co-crystallized inhibitors at ligprep and confgen using XP, SP and HTVS scoring function is given in the supporting information with their respective PDB Ids (table S7). 11 PDB Ids, which showed RMSD of less than 1.5Å, were selected for Enrichment factor (EF) calculation. For EF calculation, a decoy library of 1000 compounds (provided by Schrodinger) and 77 CDK2 inhibitors (from literature) were prepared and docked at the respective grids of the selected PDB IDs using various scoring function. PDB Id which identifies maximum number of actives in top 1% via screening this decoys + actives library was considered for molecular docking studies.54,55 This approach would increase the chances of attaining bioactive conformations and identification of maximum number of actives from screening the large compound libraries. 2.4 In silico screening protocol. All the three in silico screening filters i.e. e-Pharmacophore mapping, Pharmacophore based 3D QSAR screening and substructure and similarity search were applied in parallel to screen the Institutional compound library to identify potent compounds for CDK2 inhibition. Common compounds were identified among these three in

silico screening filters. The compounds attained via applying these filters were subsequently docked on CDK2 in order to analyze their interaction with the critical amino acid residue i.e. Leu83 residue reported for CDK2 inhibition.56 A dock score with a cut off value -8.0 was kept as a benchmark for the selection of compounds. Considering docking score threshold, interaction with the critical residue and diversity of identified compounds, certain compounds were selected for their bio evaluation. 2.5 In vitro Kinase Assay. ADP-Glo Kinase Assay is a lumniscent kinase assay that measures ADP formed from a kinase reaction; ADP is converted into ATP, which is converted into light by Ultra-Glo Luciferase. The Lumniscent signal positively co-relates with ADP amount and kinase activity. The assay is well suited for measuring the effects chemical compounds have on the activity of a broad range of purified kinases making it well ideal for both primary screening as well as kinase selectivity profiling.



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Briefly, the assay is performed in a white 96 well plates taking both reaction mixture (kinase reaction in the presence of substrate) and blank control (kinase reaction in the absence of substrate) into consideration. The respective CDK/Cyclin reaction is initiated by the addition of 5 µl of 250 µM ATP Assay Solution (1ml of ATP Assay Solution is prepared by adding 25 µl ATP Solution (10mM) to 500 µl of 2X buffer and 475 µl of dH20) bringing the final volume up to 25 µl and the reaction mixture is incubated at 30oC for 15 min. After the incubation period, the reaction termination and the remaining ATP depletion is done by adding 25 µl of ADP-Glo Reagent to each well and the reaction mixture is further incubated at ambient temperature for another 40 min. After this, a 50 µl of Kinase Detection Reagent (Prepared by mixing Kinase Detection Buffer with Lyophillized Kinase Detection Substrate) is added to each well and the plate is incubated again for 30 min. Finally, the 96-well reaction plate is read on a Luminescence plate reader and the ADP produced (nmol) in the presence and absence of substrate is determined. Percent Kinase Inhibition is calculated as: % Kinase Activity = Lumniscence of Test- Lumniscence of Blank X 100 Lumniscence of Control-Lumniscence of Blank % Kinase Inhibition= 100- % Kinase Activity 2.6 Free binding energy calculation. The compounds which showed higher binding affinity were further considered for binding free energy calculation using Prime MMGBSA (Molecular Mechanics Generalized Born Surface Area) DG bind, which is calculated as under: ∆G(bind) = E_complex(minimized) - ( E_ligand(minimized) + E_receptor(minimized) ) Where,

E_complex(minimized)

is

the absolute free energy

of

the

complex,

E_receptor(minimized) is the absolute free energy of the protein, and E_ligand(minimized) is the absolute free energy of the ligand. 2.7 Molecular dynamics. The binding pocket of CDK2 comprises of following hydrophobic residues i.e. Ile 10, Val18, Ala31, Val64, Phe80, Phe82, Leu83, Leu134, Ala144; Polar residues Thr14, His84, Gln85, Gln131; Negatively charged Glu51, Glu81, Asp86, Asp145; Positively charged Lys33, Lys89; Gly11 and Gly13 which resides within 4Å from the centroid of the active site. In order to identify the amino acid residues responsible for providing stability to the compounds found active in in vitro studies for CDK2 inhibition, the docked pose of these actives were considered for MD simulation studies. Initially the protein8 ACS Paragon Plus Environment

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ligand complex was merged in the SCP solvent system with cubic boundary conditions. Each system was neutralized by adding Na+ and Cl- counter ion for simulation studies. All the complexes were minimized at the default parameters. The simulation was performed in NPT ensemble for 20ns. Maestro-Desmond interoperability tool (version 4.1, Schrodinger, LLC, 2015) was used for performing all the MD studies.57 3. RESULT AND DISCUSSION 3.1. Ligand guided screening strategies 3.1.1. Sub-structure and similarity search of structurally diverse known CDK2 inhibitor. The 27 diverse scaffolds and 77 CDK2 inhibitors resulted in the identification of 8,422 and 897 compounds respectively from sub-structure and similarity search against Institutional compound library. 3.1.2. Pharmacophore based 3D-QSAR modeling. Four predictive pharmacophore based 3D-QSAR models with R2 close to 1, Q2 more than 0.5 and stability higher than root mean square error (RMSE) were selected to screen the Institutional compound library. The values of statistical parameters identified for the selected predictive pharmacophore models for Set A-D series are given in table 2. 6-point pharmacophoric features were obtained from Set A-C series, which resulted in the identification of 444 compounds whereas 5 point pharmacophore hypothesis of Set D series could identify 289 compounds upon screening the Institutional compound library (table 2). All the models showed good prediction of the test set compounds as indicated by the Q2 values (table 2) which describes the robustness of the QSAR model. The pharmacophoric features of these Set-A to Set-D series with the best hypothesis with the best fit compound is shown in figure 1. The inter-site distances between the pharmacophoric features of the 4 models were also calculated (table S8-11). The plots of actual v/s predicted activities of training and test set compounds of 4 series are depicted in figure S2-S5. The pharmacophore screening of the 4 congeneric series resulted in the identification of the total 733 compounds. Table 2. Statistical parameters of the 4 congeneric PLS$

Hypothesis

R2*

Q2#

Set-A

6

AAADDR

0.932

0.841

Set-B

6

AAADDH

0.828

0.702

Series

RMSE@

No of Hits

0.744

0.355

166

0.492

0.41

122

Stability



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Set-C

6

AAAHHR

0.804

0.735

0.511

0.22

156

Set-D

5

ADDHR

0.946

0.505

0.345

0.287

289

$ PLS: Partial least square * R2: co-relation coefficient of predictive model # Q2: cross-validated coefficient of the predictive model @ RMSE : Root mean square error in the test set prediction

Figure 1. Best pharmacophore hypothesis from each series aligned on reference ligand here light red sphere represent hydrogen bond acceptors (A), orange torus ring as aromatic (R), green sphere are Hydrophobic (H) and light blue sphere are hydrogen bond donor (D) which defines the geometric location of pharmacophoric site A) Best pharmacophore hypothesis AAADDR (Indenopyrazole derivative) aligned on best-fit compound 81 B) Best pharmacophore hypothesis AAADDH (pyrazolo[4,3-h]qinazoline-3-carboxamides) aligned on best-fit compound 24 C) Best pharmacophore hypothesis AAAHHR (imidazole pyrimidine amides analogs) aligned on best-fit compound 65 and D) Best pharmacophore hypothesis ADDHR (3-aminopyrazoles) aligned on best-fit compound 17. 3.2. Structure guided screening strategy 3.2.1. e-Pharmacophore screening. The bound co-ordinates of diverse CDK2 inhibitors were considered to generate 27 e-Pharmacophore models. Screening of the Institutional compound library through these e-pharmacophore models resulted in the identification of 7,859 compounds which possess several important chemical structural features of the already known CDK2 inhibitors.


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3.2.2. Molecular Docking studies. The 3S1H crystal structure of CDK2 resulted in the identification of the maximum number of actives within 1% of the screened dataset (77 actives and 1000 decoys) at XP scoring function via EF calculation (table S12). The ROC plot of 3S1H PDB Id illustrating the number of CDK2 known inhibitors identified from the screened dataset using HTVS, SP and XP scoring function of Glide docking is shown in figure 2. Further the AUC, ROC, RIE, and BEDROC (α= 20) values of this PDB also showed better values at XP than HTVS and SP scoring function (table 3). Hence the selection of XP scoring function for performing molecular docking studies was appropriate. Thus all the molecular modeling studies were performed at 3S1H crystal structure of CDK2 using XP scoring function. Table 3. AUC, ROC, RIE and BEDROC values calculated at HTVS, SP and XP docking results for 3S1H CDK2 crystal structure AUC@

ROC#

RIE*

BEDROC (α= 20) $

HTVS

0.84

0.87

4.09

0.382

SP

0.91

0.94

5.26

0.491

XP

0.94

0.97

6.27

0.585

Scoring function

@

AUAC: area under the curve is the probability that a randomly chosen known active will rank higher than a randomly chosen decoy (the value is bounded between 1 and 0, with 1 being ideal screen performance) *ROC: Receiver Operator Characteristic area under the curve (value is bounded between 1 and 0, with 1 being ideal screen performance and 0.5 reflecting random behaviour) #

RIE: Robust Initial Enhancement. Active ranks are weighted with an continuously decreasing exponential term ( Large positive RIE values indicate better screen performance) $

BEDROC: Boltzmann-enhanced Discrimination Receiver Operator Characteristic area under the curve. The

value is bounded between 1 and 0, with 1 being ideal screen performance



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Figure 2. ROC plots of screening a dataset of CDK2 inhibitors and decoys at 3S1H using Glide docking A) HTVS scoring function B) SP scoring function anf C) XP scoring function. 3.3. In silico screening studies. In the present study our aim was to identify new chemical entities from Institutional compound library for CDK2 inhibition. This goal was achieved by applying ligand and structure based in silico filters in parallel i.e. substructure plus similarity search, e-Pharmacophore mapping and pharmacophore based 3DQSAR modeling which resulted in the identification of 9253, 11736 and 733 compounds respectively from screening the Institutional compound library. A large number of compounds were identified from each ligand-based screening filter. Thus, in order to reduce the number of false positives, the parallel selection approach was applied. These identified compounds were further subjected to molecular docking studies on the crystal structure of CDK2 (3S1H) using XP scoring function. The compounds having dock score of < -8 were retained from each filter and hence, a total of 262 common compounds were identified. On further analyzing the interaction of these compounds with the key residue Lue83, 110 compounds were selected. The identified 110 compounds were further clustered into 17 structural moieties on the bases of their common moieties that exist in the identified compounds. The overall screening protocol adopted to screen the library is shown in figure 3.


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Figure 3. In silico screening strategy applied for screening the ChemDiv and ChemBridge library Based on the structural diversity and binding affinity minimum one compound was selected from each moiety and in total 40 compounds were given for biological screening (table S13). 3.4 In vitro screening and IC50 determination. 40 compounds identified from in silico studies were screened for their in vitro CDK2 inhibitory potential at a preliminary concentration of 10 µM. Out of these, seven compounds exhibited more than 50% kinase inhibitory (Ki) potential at this concentration. These seven compounds were further evaluated at 5 different concentrations for determining their IC50 values. The graph plot of IC50 calculation for all these seven compounds (hits) are shown in figure 4.



IC50 5.4 µM

IC50 10.3 µM

80

% CDK2 Inhibition

% CDK2 Inhibition

60 40 20 0 0.0 -20

0.5

1.0

60 40 20 0 0.0

1.5

IC50 4.8 µM 100

% CDK2 Inhibition

% CDK2 Inhibition

40 20

0.0

0.5

1.0

80 60 40 20 0 -0.5

1.5

0.0

1.0

1.5

IC50 4.8 µM

IC50 10 µM 100

% CDK2 Inhibition

60

% CDK2 Inhibition

0.5

Log Conc. (Z632-6425) µM

Log Conc. (D361-2645) µM

40 20 0 0.0

0.5

1.0

80 60 40 20

1.5 0 -0.5

Log Conc. (5318158) µM

0.0

0.5

1.0


IC50 3.9 µM 100 80 60 40 20 0 -0.5

1.5


60

-20

1.0


80

0 -0.5

0.5

-20

IC50 6.6 µM

% CDK2 Inhibition

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0.0

0.5

1.0

1.5


Figure 4. Graph plot of IC50 calculation. 14 ACS Paragon Plus Environment

1.5

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3.5. Novelty search of the identified hits. The 7 hits which showed an IC50 of less than about 10 µM were taken further for literature search. The compounds comprised of diverse structural moieties and the core moiety of each compound was considered for literature review using Scifinder database. Structural analysis of the seven hits helped in segregating these compounds into six different core moieties (figure 5) viz.,

pyrazolo-dihydro-

pyridinone moiety (M1; in D361-0100), pyrazolo-4-pheny-thiazepinone moiety (M2; in D361-2634), tetrahydro-2H-indazole-3-carboxamide moiety (M3; in Z632-6425), 2indolinone (M4; in 5666717), pyrazole-carbohydrazide moiety (M5; in 5318158 and 5784234) and pyrazolo-4-thiophen-thiazepinone moiety (M6; in D361-2645). All these structural moieties were considered for the novelty search and it was identified that the moieties M4 and M5 were reported for anti-cancer, anti-kinase as well as CDK2 inhibition, while M1 to M3 moieties were reported for anti-cancer and anti-kinase activity, but not CDK2. The moiety M6 (pyrazolo-4-thiophen-thiazepinone) was found to be novel i.e. still not reported for any anti-cancer or kinase activity. This study resulted in the identification of a novel M6 moiety i.e. pyrazolo-4-thiophen-thiazepinone which could be explored further for lead optimization studies and synthesis to design better and selective analogs for CDK2 inhibition.

Figure 5. Structure of 7 identified hits. The core moieties are shown in bold. 3.6. Molecular dynamic analysis of the active compounds. The calculated binding free energy of the 7 active compounds falls within the range of -76.669 to -49.594 kcal/mol. 15 ACS Paragon Plus Environment


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Molecular dynamics studies of all these compounds were carried out and upon analysing the protein-ligand interaction diagram during the 20ns MD run it was identified that Leu83 which is a critical residue formed H-bonds with all the active compounds. Glu81 residue formed Hbonds with five compounds viz. 5666717, 5784234, D361-0100, D361-2634 and D361-2645. Ile10 formed H-bonding with D361-2634, and Phe80 was involved in π-π interaction with D361-2645. Gln131 residue interacts with Z632-6425 via water linking, while Thr14, Lys89 and Lys129 residues form ionic interactions with compound 5318158 whereas Asp86 and Gln131 residue provides an additional stability to this compound by interacting via Na+ ion (table 4). Table 4. Structure of actives with core moieties, protein ligand interaction studies before and after MD simulation Compound ID

Interaction plot of contacts that occur between protein and ligand during the simulation run of 20ns with compound

Detailed protein-ligand interaction diagram after MD simulation

D361-0100

D361-2634


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Z632-6425

5666717

5318158

5784234

D361-2645



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It was observed from the MD simulation analysis of the novel compound D361-2645, that it forms a stable complex with the protein. Similar interactions were seen in the initial frame and during the simulation run of 20ns. The π-π interaction of Phe80 with thiophene moiety persist for 57%, H-bonding interaction of Glu81 with NH group of pyrazolo moiety persist for 99% whereas H-bonding of Leu83 with NH group and N atom of pyrazolo-thiazepinone moiety persist for 86% and 97% respectively. From RMSD plot it was found that the protein attained stability after 2ns of simulation run and RMSF of side chains of protein indicates N terminal and middle residues shows more fluctuation than C terminal residues (figure 6). This study confers that Phe80, Glu81 and Leu83 are important residues for the stability of D3612645-CDK2 complex. Similarly MD studies were performed on the other active compounds in complex with CDK2 and the important residues involves in providing stability to complex are referred in table 4.


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Figure 6. Analysis of MD simulation of CDK2 protein in complex with D361-2645 (novel compound) A) 2D-Structure of D361-2645 molecule Id B) Initial frame of CDK2 protein in complex with D361-2645 C) RMSD of protein and ligand D) RMSF of side chains of protein, E) A schematic of detailed ligand atom interactions with the protein residues. Interactions that occur more than 30.0% of the simulation time in the selected trajectory, F) ProteinLigand contacts during the simulation.



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For analysing the scope of modification for the design of more potent and selective inhibitors of CDK2, the interaction of D361-2645 (novel moiety) with CDK2 was studies in detail. It was observed that the pyrazolo-thiazepinone moiety of this compound is involved in interaction with critical residues Glu81 and Leu83, whereas thiophene forms π–π interactions with Phe80. Considering the protein-ligand interactions and binding pocket analysis it was identified 2nd position of thiazepinone ring A can be replaced with HB-donor/acceptor group and methyl group at 7th postion of pyrazole ring B can be substituted with HB-donor whereas thiophene ring C can be replaced with other aromatic ring or can be substituted with HBdonor/acceptor groups (Figure 7). With these inputs, and suggestions from the medicinal chemists at our Institute, some new compounds, proposed to have better CDK inhibitory activity are designed and proposed (Figure 8), along with their ADME properties calculated using Qikprop58 (table S14).

Figure 7. Scope of modification around the novel identified hit D361-2645 for CDK2 A) 2Dstructure of active compound B) Interaction diagram of active compoud C) Binding pocket analysis of compound with CDK2 active site D) Scope of modification.


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Figure 8. Proposed potent derivatives of pyrazolo-thiazepinone. 3.7. Conclusion Seven structurally diverse CDK2 inhibitors have been identified, using a combination of structure-based and ligand-based computational studies, from the Institutional compound library comprising of 50,000 drug-like compounds. The computational methods were applied in a way so as to fetch a maximum number from each independent methodology. Eventually, these methods were applied in parallel in order to attain maximum sensitivity (true positive rate) of the model. After analysing the molecular interactions of the identified compounds with the protein and considering maximum structural diversity, a total of 40 compounds were screened in vitro. This resulted in the identification of seven structurally diverse compounds having IC50 in the range of 3.9 µM to 10.3 µM. Molecular dynamics studies of these hits suggested that Glu81 and Leu83 are the pivotal residues which stabilize the ligands within the binding pocket. Similar observation has been reported earlier also in several studies.48,56,59 Comparison of the interaction of 27 representative PDB Ids of CDK2 bound with different inhibitors from PDBsum60, revealed similar H-bonding interactions majorly with Glu81 and Leu83, which further verified the finding. The identification of potent inhibitors of CDK2 through this methodology validates the robustness of the models and their usage in combination. This also helped in identification of a novel scaffold (pyrazolo-4-thiophenthiazepinone) which has not been reported earlier for CDK2 inhibitory activity as well as for any anti-kinase or anti-cancer activity. Few structures based on this moiety have been designed and proposed as potent CDK2 inhibitors. This structural moiety can be explored



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further with medicinal chemistry support for designing effective and selective CDK2 inhibitors.

ASSOCIATED CONTENT

Supporting Information Data of 27 bioactive-scaffolds identified from co-crystallized CDK2 inhibitors reported in PDB (Table S1); structure of 77 CDK2 inhibitors used for substructure and similarity search (Table S2); information regarding the built pharmacophore models (Tables S3−S6, S8-S11, Figure S2-S5); RMSD calucation (Table S7); EF calucation (Table S12); structures considered for bioevalution (Table S13); proposed structures and their predicted molecular properties (Table S14); e-Pharmacophore models build on the co-crystallized ligands (Figure S1).

AUTHOR INFORMATION

Corresponding Author *E-mail: [email protected]; Phone: 0191-2585028; EPAB Ext.: 269. Notes The authors declare no competing ﬁnancial interest.

ACKNOWLEDGMENTS

DBT project GAP-0141: Establishment of Sub-DIC under BTIS Net program. ICMR project P-90807: Identification and optimization of CDK (Cyclin Dependent Kinases) based anticancer leads using cheminformatics tools. AN acknowledges DBT, New Delhi, for their financial support through Project GAP-0141. AN, GC, MG, AK and PS thank CSIR, New Delhi, for financial support. PM thanks ICMR, New Delhi, for her research fellowship (Grant P-90807).

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