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Computational Biology and Chemistry

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Natural compounds as potential Hsp90 inhibitors for breast cancer-Pharmacophore guided molecular modelling studies

Shailima Rampogua,1, Shraddha Paratea,1, Saravanan Parameswarana,1, Chanin Parka,Ayoung Baeka, Minky Sona, Yohan Parkb, Seok Ju Parkc,⁎, Keun Woo Leea,⁎

a Division of Life Sciences, Division of Applied Life Science (BK21 Plus), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute ofNatural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju, 52828, Republic of Koreab College of Pharmacy, Inje University, 197 Inje-ro, Gimhae, Gyeongnam, 50834, Republic of Koreac Department of Internal Medicine, College of Medicine, Busan Paik Hospital, Inje University, Gyeongnam, Republic of Korea

A R T I C L E I N F O

Keywords:Hsp90 inhibitorsBreast cancerNatural compoundsStructure-based pharmacophore modellingMolecular dynamics simulations

A B S T R A C T

Breast cancer is one of the major impediments affecting women globally. The ATP-dependant heat shock protein90 (Hsp90) forms the central component of molecular chaperone machinery that predominantly governs thefolding of newly synthesized peptides and their conformational maturation. It regulates the stability and func-tion of numerous client proteins that are frequently upregulated and/or mutated in cancer cells, therefore,making Hsp90 inhibition a promising therapeutic strategy for the development of new efficacious drugs to treatbreast cancer. In the present in silico investigation, a structure-based pharmacophore model was generated withhydrogen bond donor, hydrogen bond acceptor and hydrophobic features complementary to crucial residuesAla55, Lys58, Asp93, Ile96, Met98 and Thr184 directed at inhibiting the ATP-binding activity of Hsp90.Subsequently, the phytochemical dataset of 3210 natural compounds was screened to retrieve the prospectiveinhibitors after rigorous validation of the model pharmacophore. The retrieved 135 phytocompounds werefurther filtered by drug-likeness parameters including Lipinski’s rule of five and ADMET properties, then in-vestigated via molecular docking-based scoring. Molecular interactions were assessed using GeneticOptimisation for Ligand Docking program for 95 drug-like natural compounds against Hsp90 along with twoclinical drugs as reference compounds – Geldanamycin and Radicicol. Docking studies revealed three phyto-chemicals are better than the investigated clinical drugs. The reference and hit compounds with dock scores of48.27 (Geldanamycin), 40.90 (Radicicol), 73.04 (Hit1), 72.92 (Hit2) and 68.12 (Hit3) were further validated fortheir binding stability through molecular dynamics simulations. We propose that the non-macrocyclic scaffoldsof three identified phytochemicals might aid in the development of novel therapeutic candidates against Hsp90-driven cancers.

1. Introduction

Breast cancer is one of the leading causes of cancer related mor-talities noticed in women globally (Ghoncheh et al., 2016) with 39,620recorded deaths in USA (Zagouri et al., 2013). Predominantly, breastcancer demonstrates metastasis to bone, lung, liver and brain (López deVictoria and Koculi, 2015). Though, Hsp90 is in increasing levels inmany of the human cancers, its overexpression in breast cancer isaligned with poor progression (Bagatell and Whitesell, 2004). Thischaperone communicates with the proteins promoting breast cancersuch as estrogen receptor (ER), antiapoptotic kinase Akt, tumor sup-pressor p53 protein, Raf-1 MAP kinase, angiogenesis transcription

factor HIF-1alpha, and receptor tyrosine kinases from erbB family(Miyata et al., 2013; Zagouri et al., 2013, 2012). Therefore, targetingHsp90 would undermine the breast cancer cases altogether.

In recent years, molecular chaperones emerged as attractive drugtargets for therapeutic research due to their distinct cellular distribution(Ahmad and Muzaffar, 2016). They participate in ensuring the properfolding of proteins (Hoter et al., 2018), thereby maintaining intricatebalance between protein synthesis and degradation (Franke et al.,2013). The misfolded proteins may alter normal proliferation andphysiological functioning of cells leading to the six hallmarks of cancer(Amolins and Blagg, 2009; Blagg and Kerr, 2006; Hanahan andWeinberg, 2011). Heat shock proteins comprise a group of molecular

https://doi.org/10.1016/j.compbiolchem.2019.107113Received 8 April 2019; Received in revised form 14 August 2019; Accepted 18 August 2019

⁎ Corresponding authors.E-mail addresses: psjgenesis@hanmail.net (S.J. Park), kwlee@gnu.ac.kr (K.W. Lee).

1 Equal contribution.

Computational Biology and Chemistry 83 (2019) 107113

Available online 05 September 20191476-9271/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

T

chaperones, of which, the highly conserved 90 kDa Hsp90 is the corecomponent of oligomeric chaperone machine that collaborates with acluster of co-chaperones to attain the folding, activation and stabiliza-tion of numerous client proteins, thus maintaining cellular homeostasis(Hoter et al., 2018; Verma et al., 2016). These client proteins involvedin multiple signalling pathways are frequently upregulated in cancercells, thus making inhibition of Hsp90 as a suitable strategy to develop

new effective anti-cancer drugs (Ahmad and Muzaffar, 2016; Butleret al., 2016; Franke et al., 2013).

Hsp90 is a member of the Gyrase-Hsp90-histidine Kinase-MutL(GHKL) superfamily of homodimeric ATPases (Butler et al., 2016) withBergerat fold geometry (Sidera and Patsavoudi, 2014). It prevails infour isoforms: cytosolic Hsp90α and Hsp90β, mitochondrial tumornecrosis factor (TNF) receptor-associated protein-1 (Trap-1) and

Fig. 1. Generation of receptor-based pharmacophore model. A) Pharmacophore model mapped against the co-crystallized ligand, 2-(1H-pyrrol-1-ylcarbonyl) ben-zene-1, 3, 5-triol (PYU) within the ATP-binding pocket of NTD of Hsp90. B) Each key residue has demonstrated with the required pharmacophore features C)Interfeature distances within the pharmacophore features.

Table 1Different values resulted in the decoy set validation of known drugs from an in-house and DUD database.

S. No. Parameters In-house database DUD database

1 Total number of molecules in database (D) 160 832 Total number of actives in database (A) 22 223 Total number of hits retrieved from the database (Ht) 25 184 Total active molecules in the hit list (Ha) 17 175 % Yield of active [(Ha/Ht) × 100] 68 94.446 % Ratio of actives [(Ha/A) × 100] 77.27 77.277 Enrichment Factor (EF) 4.9 3.58 False negatives (A-Ha) 5 59 False positives (Ht-Ha) 8 110 Goodness of fit score (GF) 0.668 0.88

Fig. 2. Retrieval of potential hit compounds from the natural compound phytochemical dataset using the pharmacophore model.

S. Rampogu, et al. Computational Biology and Chemistry 83 (2019) 107113

2

glucose regulated protein 94 (Grp94) (Hoter et al., 2018; Verma et al.,2016). Each monomeric form of Hsp90 protein comprises of three di-verse conserved domains. A ˜25 kDa N-terminal ATP-binding domainthat incorporates the ATP binding cleft required for conformationalchanges of ATPase Hsp90 cycle, ˜35 kDa middle domain that acts as therecognition site for substrate clients and ˜10 kDa C-terminal domaincontaining an alternative ATP-binding site and the dimerization motifMEEVD for the binding of co-chaperones. (Amolins and Blagg, 2009;Butler et al., 2016; Franke et al., 2013; Hoter et al., 2018; Sidera andPatsavoudi, 2014; Verma et al., 2016).

The therapeutic potential of Hsp90 was established by naturalproduct inhibitors targeting the ATP binding pocket of NTD:Geldanamycin (GA), derived from Streptomyces hygroscopicus andRadicicol (RD) isolated from Monosporium bonorden (Butler et al., 2016;Franke et al., 2013; Hoter et al., 2018; Sidera and Patsavoudi, 2014;Verma et al., 2016), leading to the competitive inhibition of ATPbinding and hydrolysis (Sidera and Patsavoudi, 2014). Notably, some ofthe N-terminal ATP-binding site drugs are ansamycins constituting ansarings joined by chief functional moieties like benzoquinone and car-bamate moiety in GA (Trendowski, 2015), while resorcinol and epoxidemoiety in RD (Hadden and Blagg, 2009). Although GA and RD de-monstrated structural instability, limited in-vivo stability and hepato-toxicity in clinical practice (Amolins and Blagg, 2009; Butler et al.,2016; Hoter et al., 2018; Sidera and Patsavoudi, 2014; Verma et al.,2016), they provided a structural basis for the identification of naturalproducts as novel Hsp90 inhibitors (Blagg and Kerr, 2006; Davenportet al., 2014).

GA and RD having comparable structures to ATP, thereby occupythe ATP binding pocket of NTD, enabling further interrogation of si-milar Hsp90 natural product inhibitors (Davenport et al., 2014). Thesepotent natural inhibitors represent metabolites exhibiting prominentsuppression of tumor proliferation, displaying an ability to disruptprotein kinase signaling pathways (Chini et al., 2016). In addition,these macrocyclic scaffolds gave an opportunity to explore non-mac-rocyclic drug-like compounds that do not exhibit the impairments as-sociated with GA and RD. Most potent dietary natural compounds wereexplored that include isothiocyanates, polyphenols, ellagitannins, in-doles, retinoids, and flavonoids (Amolins and Blagg, 2009; Chini et al.,2016; Ismail et al., 2016; Vassallo et al., 2013; Verma et al., 2016), ofwhich, polyphenols have attracted renewed attention as potential an-tiproliferative therapeutics for their ability to improve metabolism,activity and health (Davenport et al., 2014; Garg et al., 2016; Hosokawaet al., 1990; Nishiumi et al., 2011; Verma et al., 2016). Recent studieshave also reported that the antioxidant activity of dietary flavonoids inthe suppression of carcinogenesis evaluating their inhibitory effects ondistinct types of cancers (Meiyanto et al., 2012; Nishiumi et al., 2011).

The interest in designing new Hsp90 inhibitors with non-macro-cyclic scaffolds devoid of detriments linked with clinical drugs, GA andRD prompted us to identify phytochemicals as potential inhibitorsagainst Hsp90 via a structure-based pharmacophore approach.

2. Materials and methods

2.1. Generation of the structure-based pharmacophore model

Receptor-based pharmacophore model utilizes the known active siteof a protein to identify effective competitive inhibitors and hence thestructure of Hsp90 substrate binding domain with its bound inhibitorwas retrieved from RCSB (PDB ID: 3EKO, chain A) (Kung et al., 2008).Binding site was defined within 10 Å around the bound co-crystallizedligand elucidating the key complementary features as evaluated by theInteraction Generation module available with Discovery Studio (DS)v18.1.0. Subsequently, the Receptor-Ligand Pharmacophore Generationmodule within DS was employed for the generation of pharmacophoremodels.Ta

ble2

Theinterm

olecular

interactions

ofreferenc

ean

dhitco

mpo

unds

withHsp90

(PDBID

:3EK

O)alon

gwiththedo

ckingfitnessscore.

Com

poun

dNam

eGOLD

Fitness

Score

Hyd

roge

nbo

ndinteractions

(distanc

ein

Å)

Residue

sinvo

lved

inva

nde

rWaa

lsinteractions

Residue

sinvo

lved

inπ-

π/π-alky

linteractions

Referen

ce1(G

elda

namycin)

48.27

Asn51

:ND2-O4(3.2)Ly

s58:NZ-O3(3.0)Asp93

:OD1-H67

(2.1)Ile9

6:N-O

9(3.1)Gly97

:N-O

9(2.5)Asn10

6:ND2-O8(2.7)Gly13

5:O-H

56(2.7)

Leu4

8,Se

r52,

Gly95

,Asp10

2,Val13

6,Gly13

7,Th

r152

,Gly18

3,Th

r184

,Val18

6Asn51

,Ala55

,Met98

,Leu

107,

Phe1

38

Referen

ce2(R

adicicol)

40.90

Lys58:NZ-O2(3.0)Asp93

:OD2-H41

(2.6)

Asn51

,Ser52

,Asp54

,Ile91

,Ile96

,Asp10

2,Asn10

6,Le

u107

,Thr18

4Le

u48,

Ala55

,Met98

,Phe

138,

Val18

6Hit1

73.04

Lys58:NZ-O44

(2.7)Asp93

:OD1-H80

(2.1)Asn10

6:O-H

74(2.1)

Thr184

:OG1-O42

(2.1)

Ile2

6,Le

u48,

Ser52,

Asp54

,Gly95

,Ile96

,Gly97

,Asp10

2,Le

u103

,Asn10

5,Le

u107

,Ile1

10,A

la11

1,Val13

6,Gly13

7,Ph

e138

,Tyr13

9,Gly18

3

Ala55

,Met98

,Ly

s112

,Val18

6

Hit2

72.92

Asn51

:O-H

59(2.7)Ile9

1:O-H

74(2.4)Ph

e138

:N-O

5(2.7)

Ile2

6,Asp54

,Ala55

,Val92

,Asp93

,Ile96

,Gly97

,Asn10

6,Le

u107

,Ile1

10,A

la11

1,Gly13

2,Gly13

5,Gly13

7,Ty

r139

,His15

4,Th

r184

,Ly

s185

Leu4

8,Ly

s58,

Met98

,Asp10

2,Ly

s112

,Val13

6,Val18

6

Hit3

68.12

Thr184

:OG1-O7(2.5)

Ile2

6,Glu47

,Lys58

,Val92

,Asp93

,Ile96

,Asn10

6,Gly10

8,Ile1

10,

Ala11

1,Th

r115

,Gly13

2,Gly13

5,Gly13

7,Ph

e138

,Tyr13

9,Ly

s185

Leu4

8,Asn51

,Ala55

,Ile91

,Met98

,Le

u107

,Val13

6,Val18

6

S. Rampogu, et al. Computational Biology and Chemistry 83 (2019) 107113

3

2.2. Validation of the pharmacophore models

The validation of selected pharmacophore is an important criterionto ensure that the model retrieves active compounds from a given da-taset. Accordingly, the chosen model was escalated to map two dif-ferent datasets such as the decoy set (an in-house dataset), also called as

Güner-Henry scoring method (Jones and Willet, 2000) and the direc-tory of useful decoys (DUD). Accordingly, the model was computed onthe basis of goodness of fit (GF) score and enrichment factor (EF)(Sakkiah et al., 2010) for evaluating the robustness of pharmacophore,where the GF score functions as a pivotal factor determining the qualityof pharmacophore ranging between 0 (null model) and 1(ideal model)

Fig. 3. Molecular dynamics simulation A) RMSD profiles for the five systems. B) Binding mode of reference and hit compounds within the ATP-binding pocket of NTDof Hsp90. The hits have accommodated at the binding pocket as noticed with the reference compounds. C) Scrutiny of H-bond interactions for the five systems. Thehit compounds depicted higher number of hydrogen bonds than that of reference compounds Geldanamycin (Reference 1) and Radicicol (Reference 2).

Fig. 4. Intermolecular interactions of the reference and hit compounds with key residues of Hsp90. The hydrogen bond interactions of A) Geldanamycin (Reference1)B) Radicicol (Reference2) C) Hit1 D) Hit2 and E) Hit3. The hydrogen bond interactions were depicted as green dashed lines while the residues and compounds in stickrepresentation.

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Table 3Comparison of pivotal amino acids required for Hsp90 inhibition analysed from 27 co-crystallized ligands of X-ray structures with better resolution than PDB ID:3EKO.

PDB ID Resolution (Å) 2D-structure of co-crystallized ligand Asn51 Ala55 Lys58 Asp93 Met98 Gly97 Phe138 Thr184

1BYQ 1.50 ✔ ✔ ✔ ✔ ✔

2YEF 1.55 ✔ ✔ ✔ ✔ ✔

2YI7 1.40 ✔ ✔ ✔ ✔ ✔ ✔ ✔

2YK9 1.32 ✔

2YKE 1.43 ✔ ✔ ✔ ✔

2YKJ 1.46 ✔ ✔ ✔ ✔ ✔ ✔

3B27 1.50 ✔ ✔ ✔ ✔

3B28 1.35 ✔ ✔

3EKO 1.55 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

3O0I 1.47 ✔ ✔ ✔ ✔

3T1K 1.50 ✔ ✔ ✔ ✔ ✔

3T2S 1.50 ✔ ✔ ✔ ✔ ✔

3T10 1.24 ✔ ✔ ✔ ✔ ✔ ✔

3VHA 1.39 ✔ ✔

3VHC 1.41 ✔ ✔ ✔ ✔

3VHD 1.52 ✔ ✔ ✔ ✔ ✔ ✔

3WHA 1.30 ✔ ✔ ✔ ✔

4LWE 1.50 ✔ ✔ ✔ ✔ ✔ ✔

(continued on next page)

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(Rampogu et al., 2018; Shahlaei and Doosti, 2016).

= ××

EF Ha DHt A

= ⎛⎝

⎞⎠

+ × − −−{ }GF Ha

4HtA(3A Ht) 1 Ht Ha

D A

where D signifies the total molecules and A represents the total activemolecules in the data set, Ht indicates the total retrieved hits from thedatabase, while Ha refers to actives present within the retrieved hits.

2.3. Virtual screening of the natural compounds dataset

The generated pharmacophore model was utilized to screen thenatural compounds dataset of 3210 compounds employing the LigandPharmacophore Mapping module with rigid fitting method to search forthe novel scaffolds with an ability to inhibit Hsp90. The compoundswere chosen from datasets with known anticancer properties, such asNPACT (Mangal et al., 2013) and unknown anticancer properties. TheADMET and Ro5 was executed for the compounds with unknown an-ticancer properties. Natural compounds are exceptional to rule of 5(Ming-Qiang Zhang and Barrie Wilkinson, 2007). The compounds

mapped were monitored for their drug-likeness and pharmacokineticsby Lipinski’s rule of 5 (Ro5) (Lipinski, 2004) and absorption, distribu-tion, metabolism, excretion and toxicity (ADMET) properties (TareqHassan Khan, 2010). Accordingly, the ADMET Descriptors modulewithin the DS was employed for evaluation of ADMET properties of themapped compounds. The resultant compounds were further scrutinizedby Ro5 which determines molecular weight (< 500 Da), hydrogen bonddonors (< 5), hydrogen bond acceptors (< 10), rotatable bonds (< 10)and lipophilicity (logP< 5). The obtained drug-like compounds weresubjected to molecular docking along with 2 reference compounds – GA(Reference 1) and RD (Reference 2).

2.4. Molecular docking studies

The compounds that conformed the above-mentioned criteria werefurther evaluated by molecular docking with Genetic Optimisation forLigand Docking (GOLD) program v5.2.2 (Verdonk et al., 2003). Mole-cular docking guides in screening the compounds that accommodatewell within the protein active site elucidating the ideal binding mode ofsmall molecules for which GOLD uses genetic algorithm revealingpartial flexibility of protein accompanied by ligand flexibility exploring

Table 3 (continued)

PDB ID Resolution (Å) 2D-structure of co-crystallized ligand Asn51 Ala55 Lys58 Asp93 Met98 Gly97 Phe138 Thr184

4U93 1.55 ✔ ✔

5J2X 1.22 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

5J64 1.38 ✔ ✔ ✔ ✔ ✔

5LNZ 1.54 ✔ ✔ ✔ ✔ ✔ ✔

5LO5 1.44 ✔ ✔ ✔ ✔

5LR1 1.44 ✔ ✔ ✔

5LRL 1.33 ✔ ✔ ✔

5NYI 1.44 ✔ ✔ ✔ ✔ ✔ ✔ ✔

5XR9 1.50 ✔ ✔ ✔ ✔ ✔ ✔ ✔

5XRE 1.50 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

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the conformational space within the active site (Kitchen et al., 2004).The inhibitor binding positions and accuracy of GOLD docking is en-sured by two scoring functions- GoldScore and ChemScore (Verdonket al., 2003). The default GoldScore operates by scoring the sum ofprotein-ligand van der Waals energy and H-bonding energy, ligandtorsional strain and internal van der Waals energy, whereas ChemScoreassesses the total free energy associated with ligand binding along withmetal-binding and hydrogen-bonding interactions.

The 3D structure of Hsp90 complexed with compound PYU (2-(1H-pyrrol-1-ylcarbonyl) benzene-1, 3, 5-triol) was retrieved from PDB (ID:3EKO). Prior to docking, the protein was prepared by utilizing the CleanProtein module present in DS followed by supplementing the missingresidues and hydrogen atoms after removing water molecules and thebound ligand PYU. To gauge on the docking parameters, the ligand incrystal structure was redocked into the active site that resulted in the

binding of docked pose at the site of inbound ligand, (Supplementary1). This finding validates the docking methodology as well as ensuresthe molecular docking parameters and the same were applied further.The active site was predicted that comprises of all atoms within 10 Årange around the co-crystallized ligand. The obtained drug-like mole-cules were subsequently docked along with reference compounds, intothe defined active site of Hsp90, allowing 50 conformers to be gener-ated for each ligand while keeping all other parameters as default. Thiswas followed by clustering, in which the best binding mode was re-trieved from the largest cluster after examining compounds with higherdock scores than reference molecules and key interactions at thebinding pocket. Correspondingly, the selected poses were furtherevaluated by molecular dynamics simulations using GROningenMAchine for Chemical Simulations (GROMACS) v5.0.6 (Abraham et al.,2015).

2.5. Molecular dynamics simulation studies

To further decipher the dynamic behavior of filtered hits at theactive site of Hsp90 for affirming the obtained binding modes retrievedfrom docking results, molecular dynamics (MD) simulations were exe-cuted. GROMACS was employed for assessing the best docked posesutilizing CHARMm27 force field (Van Der Spoel et al., 2005; Rampoguet al., 2018a) and ligand topologies generated by SwissParam (Zoeteet al., 2011; Rampogu et al., 2018c). Simulations were undertaken in adodecahedron water box solvating with TIP3P water model and systemwas neutralized with counter ions. The steepest descent energy mini-mization algorithm was executed to circumvent bad contacts from theinitial structures, further subjecting them to NVT and NPT equilibra-tion, independently. The NVT ensemble (constant number of particles,volume and temperature) was orchestrated at 300 K for 1 ns with a V-rescale thermostat complemented with NPT ensemble (constantnumber of particles, pressure and temperature) at 1 bar pressure for1 ns with a Parrinello-Rahman barostat (Parrinello and Rahman, 1981).The geometry of water molecules and bond constrains were monitoredby SETTLE (Miyamoto and Kollman, 1992) and LINear ConstraintSolver (LINCS) (Hess et al., 1997) algorithms. This was followed byemploying Particle Mesh Ewald (PME) (Darden et al., 1993) for com-puting long-range electrostatic interactions with a cut-off of 1.2 nm,while calculating short-range non-bonded interactions within a cut-offof 1.2 nm. The equilibrated NPT ensembles of each system were sub-jected to MD simulations for 10 ns. The obtained results were

Fig. 5. The binding mode of co-crystallized ligands from 27 X-ray crystalstructures with better resolution than the PDB ID: 3EKO within the ATP-bindingpocket of NTD of Hsp90. The chosen crystal structure display a similar bindingpattern.

Fig. 6. MM/PBSA estimated protein-ligand total energy of Hsp90 complexed with two reference inhibitors and three hit compounds revealed that the identified hitswere better than the reference compounds.

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7

investigated using visual molecular dynamics (VMD) (Humphrey et al.,1996) on the basis of root mean square deviation (RMSD). MolecularMechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methodologywas employed for each of the five systems, extracting 25 snapshots fromstable MD trajectories by energy_MM.xvg and the results were eval-uated on the basis of protein-ligand total energy (Baker et al., 2001;Kumari et al., 2014).

3. Results

3.1. Structure-based pharmacophore model reveals features required foreffective inhibition of Hsp90

A structure-based pharmacophore model generated from humanHsp90α domain complexed with PYU, comprises of a hydrogen bonddonor (HBD), hydrogen bond acceptor (HBA) and hydrophobic (HyP)features (Fig. 1A and B). The three-featured model with interfeaturedistance constraints between the obtained features (Fig. 1C) is con-sistent with the key residues, Asp93 and Thr184 that are required forthe inhibition of Hsp90 along with residues Ala55, Lys58, Ile96, andMet98.

3.2. Decoy set validation of the pharmacophore model

The pharmacophore model was subsequently validated by goodnessof fit score (GF) for evaluating its ability in distinguishing active andinactive compounds, according to Güner-Henry (GH) scoring method.As expected, the pharmacophore model had the capability of dis-criminating known actives from the inactives. This validation was in-stigated by screening an in-house database (D) of 160 molecules andDirectory of Useful Decoys (DUD) database (D) of 83 molecules,keeping the 22 active molecules (A) in both the databases. The GF of0.668 (in-house database) and 0.88 (DUD database) confirmed the re-liability of the pharmacophore model. Furthermore, the percentage

ratio of actives was found to be 77.27, thus approving that the selectedpharmacophore exhibited an excellent quality (Table 1).

3.3. Identification of drug-like natural compounds by virtual screening

The validated pharmacophore model retrieved 135 phytochemicalsthat satisfied the pharmacophore features, which were subsequentlyfiltered on basis of their ADMET properties and Lipinski’s rule of five(Ro5), which resulted in 95 natural compounds as potential candidatesto inhibit Hsp90 (Fig. 2). These 95 phytochemicals along with referencemolecules were taken forward to scrutinize their inter-molecular in-teractions with key residues Asp93 and Thr184 as well as other residuesincluding Asn51, Ala55, Lys58, Gly97, Met98 and Phe138 by dockingthem at the ATP-binding site of Hsp90.

3.4. Molecular docking studies to discover potential hits for Hsp90inhibition

Virtual screening was followed by molecular docking to discoverpotential hits based on their binding affinity with Hsp90. The 95 phy-tochemicals were subjected to molecular docking along with two clin-ical drugs – GA and RD as reference molecules. Ten phytochemicalsdemonstrating a higher dock score than the reference compounds wereinvestigated in detail for suitable molecular interactions needed for theinhibition of Hsp90. The top three compounds herein referred to as hitsconferred a dock score of 73.04 (Hit1), 72.92 (Hit2) and 68.12 (Hit3),while reference compounds displayed a lower dock score of 48.27 forGA (Reference 1) and 40.90 for RD (Reference 2) (Table 2). The threehits and two reference molecules were manually probed for their in-teractions with the key residues and analyzed through MD simulationsfor their stability in the Hsp90 active site.

Fig. 7. Binding mode of reference (73S) and hit compounds within the ATP-binding pocket of Hsp90 (PDB ID: 5LRL).

S. Rampogu, et al. Computational Biology and Chemistry 83 (2019) 107113

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3.5. Molecular dynamics simulations

MD simulations discern the dynamic behavior of hit compounds atthe active site of Hsp90 and were thoroughly analyzed with their rootmean square deviation (RMSD) profiles, binding modes and hydrogenbond counts. The inferred RMSD profiles demonstrated that all thecompounds recorded an average RMSD of 0.30 nm. While Reference 1and Reference 2 rendered a RMSD of 0.20 nm and 0.19 nm, the Hit1,Hit2 and Hit3 documented values of 0.20 nm, 0.20 nm and 0.18 nmrespectively (Fig. 3A). Additionally, the conformations from last 2 nswere extracted and superimposed for subsequent binding mode analysisof the hit compounds and it was observed that the three hit compoundsand the two reference compounds positioned in the binding pocket in asimilar manner as the co-crystallized ligand PYU anchored by hydrogenbonding, van der Waals and hydrophobic interactions, (Fig. 3B). Deli-neating on the hydrogen bond interactions between the hits and keyresidues of Hsp90 revealed that the hits have rendered comparativelyhigher number of hydrogen bonds as compared to reference compounds(Fig. 3C).

Further, upon outlining the intermolecular interactions of hits withHsp90, it was observed that reference compound, GA formed sevenhydrogen bonds with residues Asn51, Lys58, Asp93, Ile96, Gly97,Asn106, and Gly135, while Leu48, Ser52, Gly95, Asp102, Val136,Gly137, Thr152, Gly183, Thr184 and Val186 hold GA via van derWaals interactions. The ND2 atom of residue Asn51 formed a hydrogenbond of length 3.2 Å with the O4 atom of the benzoquinone ring of theligand. The NZ atom of Lys58 interacted with the O3 atom of the ansaring of ligand by hydrogen bond length of 3.0 Å. Another hydrogenbond was formed between the OD1 atom of Asp93 and the H67 atom ofthe ansa ring of ligand with an acceptable bond length of 2.1 Å. The Natom of both Ile96 and Gly97 have interacted by hydrogen bondingwith bond lengths of 3.1 Å and 2.5 Å, respectively with the O9 atom ofansa ring of the ligand. The O8 atom and the H56 atom of ansa ringhave formed hydrogen bonds with atoms ND2 and O of Asn106 andGly135 with bond length of 2.7 Å, respectively. Additionally, Asn51 andAla55 have formed π-π stacked and π-alkyl hydrophobic interactionswith the benzoquinone ring of the ligand with bond distance of 4.0 Åand 4.7 Å, respectively. Met98 interacted with the ansa ring via twoalkyl hydrophobic bonds of lengths 4.9 Å and 3.0 Å. Furthermore,Leu107 and the benzene ring of Phe138 formed alkyl and π-alkyl in-teractions with C18 atom of the ansa ring with a distance of 4.3 Å and4.1 Å, respectively. In addition, Asn106 and Gly135 hold GA viacarbon-hydrogen bonds (Fig. 4A and Table 2, Supplementary 2).

Reference compound, RD has formed two hydrogen bonds withresidues Lys58 and Asp93 of Hsp90. The NZ atom of Lys58 has inter-acted with O2 atom of the epoxide moeity of RD with a bond length of3.0 Å. Another hydrogen bond was observed near the resorcicol ring ofthe ligand with OD2 atom of Asp93 interacting with H41 atom by abond length of 2.6 Å. The residue Ala55 formed a hydrophobic alkylbond with C13 of the ansa ring with a bond distance of 3.5 Å. The ansaring of RD interacted with Met98 via alkyl bond of length 4.0 Å. Theresidues Leu48 and Val186 held RD firmly through alkyl hydrophobicinteractions with a bond distance of 4.2 Å and 4.4 Å, respectively, whilebenzene ring of Phe138 formed a π-alkyl bond of 4.1 Å at the Cl atom ofresorcinol ring. Furthermore, the residues Asn51, Ser52, Asp54, Ile91,Ile96, Asp102, Asn106, Leu107, and Thr184 have assisted the bindingof RD via van der Waals interactions and Gly97 via carbon hydrogenbond (Fig. 4B and Table 2, Supplementary 2).

The hit compound, Hit1 formed four hydrogen bonds with residuesLys58, Asp93, Asn106 and Thr184 with bond lengths< 3 Å. The OD1atom of Asp93 interacted with H80 atom of Hit1 with a length of 2.1 Å.Another hydrogen bond was observed between the OG1 atom of Thr184and O42 atom of the ligand with bond distance 2.1 Å. The O44 atom ofHit1 bonded with NZ atom of Lys58 at an acceptable bond length of2.7 Å. The O atom of Asn106 and H74 atom of ligand formed hydrogenbond of length 2.1 Å. Furthermore, the ring A has interacted with Ala55

Table4

.Intermolecular

interactions

anddo

ckingscores

ofreferenc

ean

dhitco

mpo

unds.

Com

poun

dNam

eGOLD

Fitness

Score

Hyd

roge

nbo

ndinteractions

(Å)

vande

rWaa

lsinteractions

π-π/

π-alky

linteractions

Referen

ce(73S

)67

.77

Met98

,Leu

103,

Tyr139

Phe2

2,Gln23

,Ile26

,Leu

48,A

sn51

,Ser52

,Asp93

,Ile96

,Gly97

,Ile10

4,Gly10

8,Ile1

10,G

ly13

5,Val13

6,Ph

e170

,Th

r184

Ala55

,Leu

107,

Ala11

,Phe

138,

Val15

0,Trp1

62,V

al18

6Hit1

82.71

Ala21

,Tyr13

9,Th

r184

Phe2

2,Gln23

,Ile26

,Asn51

,Ser52

,Asp54

,Ala55

,Lys58

,Ile91

,Asp93

,Ile96

,Gly97

,Leu

103,

Ile1

04,G

ly10

8,Ile1

10,

Ala11

1,Gly13

2,Gly13

5,Val13

6,Gly13

7,Ph

e138

,Tyr13

9,Trp1

62Le

u48,

Leu1

07,Ph

e170

,Val18

6

Hit2

75.86

Met98

,Gly13

5,Ty

r139

Ala21

,Phe

22,Gln23

,Ile26

,Ile49

,Asn51

,Ser52

,Lys58

,Val92

,Asp93

,Ile96

,Asp10

2,Le

u103

,Ile1

04,Asn10

5,Le

u107

,Gly10

8,Ile1

10,Ala11

1,Val13

6,Gly13

7,Ph

e138

,Val15

0,His15

4,Trp1

62,P

he17

0,Th

r184

,Lys18

5Le

u48,

Ala55

,Ile91

,Val18

6

Hit3

72.25

Ile1

04Ph

e22,

Ile2

6,Le

u48,

Ile4

9,Asn51

,Ser52

,Asp54

,Ile91

,Val92

,Asp93

,Ile96

,Gly97

,Asp10

2,Le

u103

,Asn10

5,Gly10

8,Ile1

10,Ala11

1,Gly13

5,Ty

r139

,Val13

6,Trp1

62,Ph

e170

,Thr18

4,Ly

s185

Ala55

,Lys58

,Met98

,Ph

e138

,Val18

6

S. Rampogu, et al. Computational Biology and Chemistry 83 (2019) 107113

9

(π-σ, bond length 3.0 Å) and Met98 (π-alkyl, bond length 5.3 Å), ring Bhas interacted with Met98 (π-alkyl, bond length 4.5 Å), ring C has in-teracted with Val186 (π-alkyl, bond length 5.4 Å), Met98 (π-sulfur,bond length 5.3 Å) and ring D has joined with Lys112 (π-alkyl, bondlength 4.5 Å). Additionally, Hit1 is held firmly by the residues Ile26,Leu48, Ser52, Asp54, Gly95, Ile96, Gly97, Asp102, Leu103, Asn105,Leu107, Ile110, Ala111, Val136, Gly137, Phe138, Tyr139 and Gly183via van der Waals interactions and residues Asn51 and Gly135 viacarbon hydrogen bonds (Fig. 4C and Table 2, Supplementary 2).

The hit compound, Hit2 formed three hydrogen bonds with residuesAsn51, Ile91 and Phe138 with bond lengths< 3 Å, one carbon hy-drogen bond with Asn51 and one π-donor hydrogen bond with Ser52.The O atom of residues Asn51 and Ile91 have interacted via hydrogenbonding with H59 and H74 of ligand at bond lengths of 2.7 Å and 2.4 Å,respectively. Another hydrogen bond was observed between N atom ofPhe138 and O5 of ligand at 2.7 Å. Additionally,the ring A has interactedwith Lys112 (π-alkyl, bond length 4.0 Å) and Val136 (π-alkyl, bondlength 5.4 Å). The ring B has interacted with Leu48 (π-alkyl, bondlength 5.2 Å), Asn51 (π-π stacked, bond length 4.7 Å) and Val186 (π-σ,bond length 3.2 Å) as well as ring C has interacted with Lys58 (π-cation,bond length 4.2 Å), Met98 (π-sulfur, bond length 5.9 Å) and Asp102 (π-anion, bond length 4.5 Å), respectively. Furthermore, the residuesIle26, Asp54, Ala55, Val92, Asp93, Ile96, Gly97, Asn106, Leu107,Ile110, Ala111, Gly132, Gly135, Gly137, Tyr139, His154, Thr184 andLys185 have positioned the ligand firmly at its site via van der Waalsinteractions (Fig. 4D and Table 2, Supplementary 2).

The hit compound, Hit3 formed one hydrogen bond with residueThr184, where OG1 atom of the residue interacted with O7 atom ofHit3 with bond length of 2.5 Å. Moreover, the ring A has interactedwith residues Val136 and Lys112 (π-alkyl, bond length 4.9 Å). The ring

B has formed a bond with Leu107 via alkyl hydrophobic interaction oflength 5.2 Å. The residues Ala55 (π-alkyl, bond length 4.7 Å) and Met98(π-alkyl, bond length 5.1 Å) have both interacted with the ring C ofHit3. Ring D has formed hydrophobic interactions with Leu48 (π-alkyl,bond length 4.7 Å), Asn51 (π-π stacked, bond length 5.0 Å), Ile91 (π-alkyl, bond length 5.0 Å) and Val186 (π-alkyl, bond length 4.9 Å). Inaddition, van der Waals interactions with residues Ile26, Glu47, Lys58,Val92, Asp93, Ile96, Asn106, Gly108, Ile110, Ala111, Thr115, Gly132,Gly135, Gly137, Phe138, Tyr139, and Lys185, carbon hydrogen bondwith Gly97 and π-donor hydrogen bond with Ser52 has held the ligandfirmly at the ATP-binding pocket of Hsp90 (Fig. 4E and Table 2, Sup-plementary 2).

4. Discussion

The heat shock protein, Hsp90 comprises of about 1–2% of the totalcytosolic proteins that are found to communicate with around 200client proteins including the co-chaperones, thereby demonstrating asignificant role in protein-folding and thus gained importance as a drugtarget for various diseases (Toft, 1998; Amolins and Blagg, 2009). Theinhibition of Hsp90 has a great potential in breast cancer therapeuticsdue to their amplitude in hindering a host of signalling pathways re-sponsible for oncogenesis (Zagouri et al., 2013). It is well documentedthat the Hsp90 inhibitors degrade the HER2 and regulates the signallingof estrogen and progesterone receptor signals as they are the Hsp90client proteins (Bagatell et al., 2001; Münster et al., 2001; Zagouri et al.,2012). These scientific evidences provide glimpses about the role ofHsp90 inhibitors across the major breast cancer subtypes (Caldas-Lopeset al., 2009; Song et al., 2010; Zagouri et al., 2012). Encouraged from

Fig. 8. Intermolecular interactions of reference and hit compounds with key residues of Hsp90 (PDB ID: 5LRL).

S. Rampogu, et al. Computational Biology and Chemistry 83 (2019) 107113

10

the scientific reports, we pursued our research towards identifying thephytochemical compounds as inhibitors for Hsp90. Earlier, our grouphas investigated for the Hsp90 inhibitors employing 3D-QSAR approach(Sakkiah et al., 2010) and that motivated to proceed with the structure-based pharmacophore modelling. Structure-based pharmacophoremodelling works by employing the key interactions existing betweenthe protein residues and the cocrystallized ligand.

The top three natural NPACT compounds (hits) from the resultingten compounds were further evaluated on basis of their binding modewithin the ATP-binding pocket of Hsp90 protein and the intermolecularinteractions between hit compounds and active site residues of Hsp90.Importantly, the emphasis was given on critical amino acids – Asp93and Thr184 that are required for Hsp90 inhibition. MD simulations ofthe three hits revealed that the natural compounds retain their inter-molecular interactions and position in the binding pocket as observedwith the reference compounds.

Although there are many Hsp90 experimentally determined struc-tures reported till date, Hsp90 co-crystallized with PYU (PDB ID: 3EKO)was considered based on the atomic resolution (1.55 Å) and owing tothe small size of co-crystallized ligand. Additionally, we analysed thecritical amino acids for Hsp90 inhibition from 27 X-ray structures withbetter resolution than above mentioned structure, as reported in theprevious study (Table 3) (Sakkiah et al., 2010). We intended at dis-covering phytochemicals larger than the co-crystallized ligand con-sidering the ligand as a ‘fragment’ around which the obtained hits canbe structured, with the perspective of identifying inhibitors having lessadverse effects. We also analysed whether the ligands positioned at theATP-binding pocket of Hsp90 by superimposing the 28 protein-ligandcomplexes as displayed (Fig. 5). It was observed that the ligands reflectsimilar binding modes as witnessed in chosen crystal structure whichdemonstrates its aptness.

The validated pharmacophore model and the molecular dynamicsstudies have retrieved three hit compounds displaying a higher dockscore than the reference compounds, Geldanamycin and Radicicol. Theidentified phytochemicals have additionally demonstrated the key re-sidue interactions with an RMSD below 3 Å throughout the simulations.

The intermolecular interactions put forth that the identified hitshave nested in the active site clamped by several key residues. Uponscrupulous analysis of the hits, it was revealed that the key residueAsp93 has demonstrated a hydrogen bond interaction with Hit1, whilein Hit2 and Hit3, it represented a van der Waals interaction thereby thesignificant interaction was preserved (Table 2). Furthermore, the MDsimulation studies have demonstrated the presence of Ala55 renderedby hydrophobic π-π/π-alkyl interactions, in both the reference and inthe hits. On the contrary, in the Hit2, Ala55 residue has prompted a vander Waals interactions, (Table 2). Such interactions were also pre-viously reported and illuminates that the identified inhibitors might beeffective Hsp90 inhibitors (Abbasi et al., 2017). The intermolecularhydrogen bond interaction analysis have implied that the identified hitshave demonstrated a higher number of hydrogen bonds than the re-ference compounds guiding to contemplate on the therapeutic usabilityof the hit compounds and have portrayed with the required pharma-cophore features (Supplementary 3).

The MM/PBSA of Hsp90-ligand complexes with the reference li-gands and three hits for 25 snapshots was computed to quantify theprotein-ligand total energy (Fig. 6). Total energy of hit compounds, Hit1 (−367.57 kcal/mol) and Hit 2 (−329.38 kcal/mol) was observed tobe comparatively lower as compared to reference compounds GA(−277.67 kcal/mol) and RD (−239.58 kcal/mol), while Hit 3(−262.95 kcal/mol) demonstrated higher total energy than GA andlower than RD.

Owing to the small size of our co-crystallized ligand (PDB ID: 3EKO)and large size of obtained hits, the crystal structure (PDB ID: 5LRL) witha relatively larger ligand size and better resolution was considered forcomparing its interaction and docking score (Table 3), with that of ourhits. Docking parameters were optimized by redocking the co-

crystallized ligand (2-azanyl-5-chloranyl-˜{N}-[(9˜{R})-4-(1˜{H}-imi-dazo[4,5-c]pyridin-2-yl)-9˜{H}-fluoren-9-yl] pyrimidine-4-carbox-amide, 73S) into the active site, resulting in a structural overlap of thedocked pose with 73S. The three hit compounds were subsequentlydocked into the active site and evaluated on the basis of their Gold-Score, binding mode and intermolecular interactions. The hit com-pounds positioned in the binding pocket in a similar fashion as the co-crystallized ligand, 73S (PDB ID: 5LRL) (Fig. 7). The hit compoundsHit1, Hit2 and Hit3 rendered better docking scores than 73S, retainingvital intermolecular interactions at the binding site (Table 4) and(Fig. 8). Manual scrutiny of the hits and compound, 73S revealed thatthe key residue Met98 formed hydrogen bond interaction with re-ference and Hit2, while in Hit1 and Hit3 it formed a π-lone pair. Fur-thermore, Leu103 was observed to hydrogen bond with reference,whereas it formed a van der Waals interaction with hit compounds.Hydrogen bond with Tyr139 was observed with all compounds, exceptHit3, where it formed a van der Waals interaction.

5. Conclusion

Receptor-based pharmacophore model employing the 3D structureof Hsp90 protein bound with co-crystallized PYU revealed the phar-macophore features required for Hsp90 effective inhibition. The modeltargeting the ATP-binding site of Hsp90 comprises three features- hy-drogen bond donor, hydrogen bond acceptor and hydrophobic features,which was subsequently used for virtual screening against a phyto-chemical dataset. The acquired 95 drug-like natural compounds afterfiltering by ADMET properties and Lipinski’s rule of five were takenforward to discover potential inhibitors against Hsp90. The three phy-tochemicals showed higher dock scores than the reference compounds(Geldanamycin and Radicicol) and significant binding interaction withAsp93 via hydrogen bond and van der Waals interactions. The identi-fied hits demonstrated effective binding with Hsp90 throughout the MDsimulations. Finally, the three identified hits can serve as novel scaf-folds for developing efficient N-terminal domain ATP-binding site in-hibitors against Hsp90 for breast cancer therapeutics.

Acknowledgements

This research was supported by the Bio & Medical TechnologyDevelopment Program of the National Research Foundation (NRF) andfunded by the Korean government (MSIT) (No. NRF-2018M3A9A7057263).

This research was supported by a grant of the Korea HealthTechnology R&D Project through the Korea Health IndustryDevelopment Institute (KHIDI), funded by the Ministry of Health &Welfare, Republic of Korea (grant number: HI18C1728).

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in theonline version, at https://doi.org/10.1016/j.compbiolchem.2019.107113.

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