In silico identification of putative bifunctional Plk1 inhibitors by integrative virtual screening and structural dynamics approach

Author’s Accepted Manuscript In silico identification of putative bifunctional Plk1 inhibitors by integrative virtual screening and structural dynamics approach Shagufta Shafique, Nousheen Bibi, Sajid Rashid www.elsevier.com/locate/yjtbi

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S0022-5193(15)00499-3 http://dx.doi.org/10.1016/j.jtbi.2015.10.006 YJTBI8385

To appear in: Journal of Theoretical Biology Received date: 6 August 2015 Revised date: 14 September 2015 Accepted date: 10 October 2015 Cite this article as: Shagufta Shafique, Nousheen Bibi and Sajid Rashid, In silico identification of putative bifunctional Plk1 inhibitors by integrative virtual screening and structural dynamics approach, Journal of Theoretical Biology, http://dx.doi.org/10.1016/j.jtbi.2015.10.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

In silico identification of putative bifunctional Plk1 inhibitors by integrative virtual screening and structural dynamics approach Shagufta Shafique, Nousheen Bibi, and Sajid Rashid National Center for Bioinformatics, Faculty of Biological Sciences, Quaid-i-Azam University Islamabad, Pakistan Address for correspondence and reprints: Dr. Sajid Rashid, National Center for Bioinformatics, Faculty of Biological Sciences, Quaid-i-Azam University Islamabad, Pakistan, Tel: +92-51-90644066, Fax: +92-51-2601145, E-mail address: [email protected]

Abstract Polo like kinase (Plk1) is a master regulator of cell cycle and considered as next generation antimitotic target in human. As Plk1 predominantly expresses in the dividing cells with a much higher expression in cancerous cells, it serves as a discriminative target for cancer therapeutics. Here we implied a novel and promising integrative strategy to identify "bifunctional" Plk1 inhibitors that compete simultaneously with ATP and substrate for their binding sites. We integrated structure-based virtual screening (SBVS) and molecular dynamics simulations with emphasis on unique structural properties of Plk1. Through screening of 20,000 compounds, nearly ~2000 hits were enriched and

subjected to SBVS against ATP and substrate binding sites of Plk1. Subsequently, on the basis of their binding abilities to Plk1 kinase and polo box domains, filtration of candidate hits resulted in the isolation of 26 compounds. By exclusion of close analogues or isomers, 10 unique compounds were selected for detailed study. A representative compound was subjected to molecular dynamics simulation assay to have deep structural insights and to gauge critical structural crunch for inhibitor binding against kinase and polo box domains. Our integrative approach may complement high-throughput screening and identify bifunctional Plk1 inhibitors that may contribute in selective targeting of Plk1 to elicit desired biological process. Keywords: Plk1, bifunctional inhibitors, structure based virtual screening, molecular dynamics simulation, and pharmacophore Introduction Polo like kinase (Plk) family of serine/threonine kinases is a central regulator of cell cycle events which is highly conserved from yeast to human [1]. In mammals, five Plk family members (Plk1-5) having distinct tissue distribution appear to exhibit non-overlapping and differential physiological functions [2]. Plk1 is one of the emerging next generation antimitotic targets. It is profound and well-characterized member of Plk family. Plk1 has been a focus of extensive studies due to its strong association with oncogenic transformation of human cell [2, 3]. Plk1 is involved in almost every step of mitosis and its subcellular localization changes accordingly to phosphorylate and promote the mitotic entry of cell division cycle (Cdc25) and cyclin proteins [4]. Plk1 significantly contributes in centrosome maturation and micro tubular nucleation by recruiting γtubulin ring complex to the centrosome [5]. Plk1 is also involved in microtubule dynamics through phosphorylation of ninein-like protein (Nlp) and translationally controlled tumor protein (Tctp) [6, 7]. During interphase and prophase stages, Plk1 is located at centrosome. Later on, Plk1 is localized to kinetochores and regulates the spindle assembly checkpoint during pro-metaphase and metaphase [8]. It activates the anaphase promoting complex (APC) through phosphorylation of early mitotic inhibitor 1 (Emi1) which binds to and inhibits regulatory subunit of APC thus leading to its destruction [9]. Moreover, Plk1 is actively involved in cytokinesis [10-12]. These cell division activities overlap with Plk1 delocalization to central spindles during anaphase and telophase. Thus Plk1 plays a crucial role in various stages of mitotic progression and its expression is sturdily linked with cell proliferation. In addition to promoting cell proliferation, over expression of Plk1 overrides the mitotic check points and leads to immature cell division, chromosomal instability and aneuploidy, which is a hallmark of cancer [13]. Plk1 is a critical factor for DNA damage which induces checkpoint recovery by phosphorylating Cyclindependent kinase (Cdk1) [14]. Overexpression of Plk1 is observed in diverse types of human tumors and its expression is strongly associated with poor prognosis [15-17]. Multiple evidences support that Plk1 diminution or inhibition preferentially kills cancer while keeping the normal cells intact which opens a therapeutic window to target this kinase for cancer treatment [18-20].

Plk1 exhibits two crucial target sites, an N-terminal kinase domain and a C-terminal polo box domain [21]. Thorough knowledge of exact structural configuration and functional operation of Plk1 is a criterion for the development of highly specific Plk1 inhibitors. Plk1 inhibitors are targeted cell cycle blockers of spindle assembly which result in the stimulation of mitotic checkpoints thus inducing entitled polo arrest and ultimately apoptosis in the dividing cells [22, 23]. Multiple inhibitors have been developed that are reliant upon their means of action in competing with ATP binding site of kinase domain or substrate binding site of polo box domain [1, 24, 25]. However, to increase the specificity of Plk1 inhibition, there is a need to develop "bifunctional" inhibitors that may combine high affinity of ATP analogue and specificity of substrate analogue [26]. However, so far, no bifunctional inhibitor exists for Plk1 [27]. Here we applied integrative approach of structure based virtual screening [28-32] and ADMET (administration, distribution, metabolism, excretion and toxicity) check along with crucial structural insights of ATP and substrate binding sites of Plk1 through utilizing knowledge of molecular dynamics simulations. We combined the affinity of ATP binding site and specificity of substrate analogues to identify the putative novel "bifunctional" inhibitors of Plk1 that may compete simultaneously with ATP and substrate for their binding sites. Simultaneous inhibition of kinase and polo box domains consequences in ~3 and ~10 fold decline in kinase activity and phosphospecificity, respectively [33]. Thus our identified inhibitors may crossover the non-specificity of known Plk1 inhibitors and can be used to design novel Plk1 targeted therapies. Material and method Dataset Experimentally known 3-dimensional molecular structures of human Plk1 kinase domain (PDB ID: 3THB) and polo box domain (PDB ID: 4LK1) were retrieved through protein data bank (http://www.rcsb.org/pdb) for present study to explore the structural basis of Plk1 inhibition and design dual action inhibitors. Chemical database generation/enrichment Due to pharmacokinetics and toxicity issues, many drug-like candidate molecules fail in the pre- clinical or clinical trials. Therefore for the success of drug design process, enrichment of chemical database is important to sort or remove the compounds that fail to satisfy drug-like properties. We initiated with a small drug-like molecule library of 20,000 diverse chemical compounds (http://eu.chemdiv.com) that were subjected to energy minimization using Avogadro tool [34] and resulting molecules without proper structural folds were discarded from the study. Each of the filtered molecules was passed through ADMET [35-37] check and compounds that did not pass through these checks as well as Lipinski’s rule of five [38] were excluded from further study. To the end of enrichment procedure, we had shortlisted approximately 2,000 compounds to perform structure-based virtual screening. Structure based virtual screening

Structure-based virtual screening (SBVS) has successfully been applied to design novel drug-like molecules [39]. Many drug molecules fail in the clinical trials due to their affinity and selectivity for target molecule. In this study, we used SBVS, to identify the suitable binding mode and affinity of ligands within the ATP binding and substrate binding pockets of Plk1. The basic SBVS's input was modeled structure of Plk1 (kinase and polo box domains). The target molecules were evaluated for any missing atom and were minimized using amber force field embedded in UCSF Chimera 1.7 [40]. Shortlisted 2,000 compounds were virtually docked into the binding cavity of target Plk1 molecule through AutoDock Vina [41] suit of PyRx to achieve an optimal complementarity of steric and physiochemical properties. The number of runs for each docking analysis was set to100. The Lamarckian genetic algorithm (LGA) was applied with the following parameters: initial population of 150 randomly placed individuals, a maximum number of 27,000 generations, a mutation rate of 0.02, 2.5x10 6 energy evaluations and a crossover rate of 0.80, while the remaining docking parameters were set to default. Accuracy of the docking procedure was evaluated by re-docking of experimentally known structure of kinase domain (PDB ID: 3THB) and polo-box domain (PDBID: 4LK1) followed by the superimposition of docked molecule with co-crystal bound complex. An RMSD value below 2Åwas considered as a successful prediction. Further to crosscheck the consistency of docking results and to eliminate the biasness of docking algorithm, we used SwissDock (www.swissdock.ch/docking) and PatchDock (bioinfo3d.cs.tau.ac.il/PatchDock) to perform comparative docking analysis. Generation of ligand and receptor based pharmacophore hypothesis Structure-based pharmacophore model for kinase and polo box domains of Plk1 was generated through the critical interactions among active site residues of receptor and ligands [28, 42-44]. LigandScout [45] and Discovery Studio v3.1 (www.accelrys.com) were used to extract and interpret the receptor-ligand interactions such as hydrogen bond, charge transfer, receptor-ligand interactions, hydrophobic and aromatic regions and solvent accessible surface area (SAS) within the ATP and substrate binding sites. Multiple features were assigned to each complex and a common hypothesis was generated. Similarly, ligand-based pharmacophore model set of the shortlisted compounds was subjected to LigandScout and Discovery studio to capture the common features of shortlisted molecules critical for binding within the ATP and substrate binding pockets of targeted Plk1 molecule. Molecular dynamics simulation studies Versatile biological functions of proteins and DNA rely on their intense dynamic mechanisms, such as alteration between active and inactive states, cooperative effects [46], allosteric transition [47], intercalation of drugs into DNA [48], and assembly of microtubules [49], which can be revealed by studying their internal motions as elaborated in a comprehensive review [50] and summarized in a recent paper with the title of 'Theoretical and Experimental Biology in One' [51]. Similarly, to exploit the action mechanism of receptor-ligand binding, we should deliberate not only the static structures concerned but also the dynamical statistics obtained by simulating their internal motions or dynamic process. To realize this, the MD simulation is one of the feasible tools. We performed molecular dynamics (MD) simulations on the Apo and inhibitor bound states of kinase and polo box domains of Plk1 to investigate the dynamic nature of interactions and to dissect the comparative structural constraint

between Apo and bound state. ATP and substrate binding domains in complex with screening hit 202, a putative bifunctional Plk1 inhibitor, were used in MD simulation. All MD simulations were performed using GROMOS96 43A1 force field in GROMACS 4.5 package [52]. Briefly, systems were solvated using SPC216 water model [53] in periodic box followed by the addition of Na+ and Cl– counter ions to neutralize the system. Before MD simulations, energy minimization (steepest descent algorithm for 500 steps) was performed by a tolerance of 1000 kJ/mol Å2 to remove initial steric clashes. The energy minimized systems were treated for 1000 ps equilibration run under specific pressure and temperature conditions to relax the systems. Finally, MD simulations were run for 10 ns time scale under constant temperature (300 K) and pressure (1 atm). PME (Particle Mesh Ewald) algorithm was used in all calculations to dissect electrostatic interactions. Stability and time dependent behaviour of each system was investigated at different nano scale intervels. PyMol (http://www.pymol.org) and GROMACS tools were used to analyze the stability and behavior of each system. Results Chemical database enrichment Enrichment of chemical or drug-like database is critical for the success of screening process and to minimize the downstream toxicity and efficacy issues. Our main aim was to identify the chemical compounds that may be able to satisfy the drug-like properties and overcome the selectivity and target affinity issues. We started with a chemical database comprised of 20,000 diverse compounds and through enrichment process; we isolated a set of compounds that satisfied the applied filters of structure refinement, ADMET and Lipinski’s rule of five. To calculate the values of drug-likeness, drug score and toxicity risks, Osiris Property Explorer [36] was used. Similarly, values for the molar masses (MM), logP, logS and PSA were predicted by Molinspiration [54] tool. After applying ADMET criteria, a total of 2,050 compounds showed good blood brain barrier (BBB) permeability, solubility and absorption value tests. Subsequently, these compounds were subjected to Lipinski’s rule of five which states that compounds are well absorbed only when they possess logP values less than 5, molecular weight less than 500 Da and fewer than 5 and 10 hydrogen bond donors and acceptors, respectively. At the end of this filter, chemical database was reduced to ~2000 diverse compounds (Fig. 1).

Fig 1 Schematic illustration of screening strategy. BifunctionalPlk1 inhibitors were identified through initial screening of large chemical database of 20,000 compounds. Resulting ~2000 compounds after enrichment were subjected to docking analysis against ATP binding and polo box domains of Plk1. 92 high energy scoring hits were further filtered out on the basis of affinity profile for both domains. Among 10 shortlisted putative bifunctional hits, one was analyzed through molecular dynamics simulation. Structure-based virtual screening Enriched database of ~2000 diverse chemical compounds was subjected to SBVS also called docking based virtual screening against ATP and substrate binding sites of Plk1 to identify the potential bifunctional Plk1 inhibitors. SBVS was performed to investigate the critical interactions with crucial amino acids present at the binding sites. Through published data and knowledge of bound complexes of Plk1, we gathered information about the critical residues of active site (kinase domain and polo box domain) involved in interactions with ATP/inhibitors and peptide/protein. For polo box domain of Plk1, Trp414, Asp416, Leu490, His538 and Lys540 were the most critical residues for substrate interaction, while Phe535, Leu491, His489, Arg557 and Arg516 were also observed in some interactions [55, 56]. Similarly, for kinase domain, Cys67, Leu132 and Phe183 residues were most critical at ATP binding pocket. Apart from these residues, Cys119, Lys68, Cys133, Arg136, Leu59, Glu140 were also observed in interactions with Plk1 inhibitors [57, 58]. We selected the mentioned key residues of ATP and substrate binding pockets of Plk1 for screening of enriched database containing 2,000 compounds. After SBVS, short-listed 92 high energy scoring hits (high energy score for both domain) were further evaluated (Table 1) and 26 dual action compounds were extracted having favorable interactions with critical residues of ATP and substrate binding sites. These molecules (shortlisted 26 hits) showed interaction with both domains of Plk1 (Fig. 2a and b). A comparative

docking analysis by PatchDock [59] and SwissDock [60] showed consistency in the docking poses and mapped residual interactions. Next, chemistry of each compound was thoroughly analyzed and close analogues and isomers were excluded. Finally, 10 unique compounds were selected for further analysis (Fig. 3). Pharmacokinetic properties of 26 shortlisted compounds are indicated in Table 2.

Fig. 2 Structures of ATP and substrate binding sites with different pockets adopted by 26 putative bifunctional compounds. (a) Structure of ATP binding site is depicted in gray ribbon with various binding pockets; hydrophobic (green and purple), phosphate binding (pink), adoptive pocket (blue) and hinge region (orange) along with respective interacting residues represented in sticks. (b) Substrate binding pocket is depicted in gray ribbon with hydrophobic, hydrogen bonding and electrostatic pockets which are represented in pink, green and golden colors along with interacting residues (represented in sticks) with respective colors.

Fig. 3 Structure of 10 screening hits that are bound to ATP and substrate binding pocket of Plk1.

Table 1 Energy scoring functions of shortlisted compounds with PLKs (PBD & KD) No.

LIGANDS

PBD BE

KD IC

BE

(nm)

IC (nm)

1

14

-10

44

-9

101

2

18

-10

18

-10

14

3

19

-10

38

-9

37

4

161

-8

663

-9

143

5

164

-10

37

-10

11

6

182

-9

53

-9

103

7

187

-8

296

-10

28

8

189

-10

17

-10

10

9

202

-10

13

-10

77

10

349

-10

44

-10

18

11

356

-10

39

-10

34

12

526

-10

17

-10

14

13

527

-8

280

-10

32

14

545

-9

237

-9

101

15

546

-8

499

-9

110

16

548

-8

549

-10

20

17

593

-10

42

-10

11

18

623

-8

1080

-8

709

19

624

-7

2150

-7

2360

20

627

-8

1070

-8

1380

21

630

-8

1230

-8

1330

22

631

-8

1280

-8

1260

23

636

-7

1440

-8

799

24

638

-8

507

-8

429

25

1357

-8

693

-9

100

26

1744

-10

40

-10

32

Note BE, Binding Energy; IC, Inhibition Constant.

(IUPAC)

Applied Chemistry

Union of pure and

International

Drug Score

Drug likness

cLogP

PSA(Å2)

Nrot bond

nOHNH

Weight (g/moL) nON

Molecular

LogP

LogS

Ligand

1,4-xazin-

(hydroxyl

amino)-9-

lohexyl

3(4Chlorocyc

44%

0.6

-3.6

114

4

5

7

517

4.9

-3.5

14

14pentazaocta

methyl2.3.4.11.

4-ylsulfeno)-23

oxy-1,4oxazin-

R)-24 hydr

C(4S,5R,10R,12

56%

0.7

-5.1

112

2

4

9

534

2.7

-3.1

18

ylsulfe no)-8

1,4-oxazin-4-

o]-9-(hydroxyl-

cyclohexylamin

3Hydroxy

3[(1S,3R)-

54%

1.0

-4.7

134

4

6

8

498

3.7

-3.3

19

04,12.05,

[13.7.1.02,14.

azahexacyclo

2.12.21.22tetr

7-fluoro

Cyclohexyl-

20-

38%

-1.8

2.3

50

1

3

5

432

3.3

-3.1

161

triaza2,3,4,4

10a-

n-1-yl)-3,9,

drophthalazi

ylperhy

(4Cyclohex

3-

60%

0.4

0.06

62

2

4

6

431

3.4

-2.8

164

3,9,10a-triaza-

thalazin-1-yl]-

yl)perhydroph

ex

Methylcycloh

3-[4-(4-

50%

-0.4

0.2

62

2

4

6

445

3.7

-2.8

182

4,13.05,10.

9.2.02,15.0

tacyclo[17.

8pentazaoc

.10.13.27.2

22Methyl2

58%

0.1

1.5

54

0

3

6

441

2.1

-2.3

187

Table 2 Drug related properties of Designed molecules and IUPAC names

Names

4ylsulfeno)-

methyl1,2-

a,4b,5,

016,29.020 cyclo[14.10.1.0

10.019,23]tri

2,3,

8methyl1,2di

diazaperhydrofl

,25.026,30 2,6.03,15.04,12

4,4a,4b,5,6,7,8

azaperhydrofl

]triacontan

6,7,8,8a,9,1

,8a,9,10-

cosan-11-ol

-12-ol

uoranthene

didecahydro-

.05,10.020,27.0

0didecahydr

uoranthene

21,26]heptacos

o-1H-

anthren-10-ol

1H-phen ane

phenanthren -10 ol

2-ylthio)-1-

opyrimidin-

hydr

Dimethylper

2-(4,6-

64%

3.8

0.4

100

4

5

7

504

2.9

-3.2

189

[7.7.1.02,7.0

lo

triazatetracyc

10.12-

Methoxy-8.

(5R)-3-(4-

55%

0.2

-5.9

117

2

5

8

509

3.7

-3.2

202

10.13.23pe

5-oxa-3.6.

19-methyl-

ohexyl) -

Chlorocycl

7-(4-

56%

0.4

2

83

1

4

8

496

1.5

-3.1

349

10.13tetraz

oxa-3.6.

methyl-5-

-20-

yclohexyl)

(4Chloroc

7-

43%

-1.7

2.1

80

1

4

7

441

1.9

-3.1

356

2]heptadec-

4.0.02,6.07,1

yclo[11.

tetrazatetrac

2.3.4. 10-

Dimethyl-

(14,15-

62%

1.0

-0.9

90

4

5

8

449

2.1

-2.7

526

razatetracy

2.3.4.10tet

dimethyl-

5-

mino)(14,1

ylmethyla

(Cyclohex

40%

-3.2

0.5

71

4

5

6

405

3.1

-2.4

527

methyl}-5,8-

)cyclohexyl]

ydroxymethyl

ethylamino}h

cyclohexyl]m

Fluoro

({[(1R)-2

3-{[4-

36%

-9.8

-1.7

106

6

5

8

485

1.9

-3.1

545

dioxa -1,3-

methyl)-5,8-

hexyl}

methyl]cyclo

ylamino)

pmenthan-7-

({4[Hydroxy(

3-

33%

-6.4

-0.6

106

7

5

8

509

2.8

-2.8

546

yl)methyl]

}cyclohex

no]methyl

methylami

piperidyl)

(4-

{Hydroxy[

3-[(4-

39%

-4.3

-2.9

118

6

6

9

468

0.7

-2.5

548

ec-14-yl)-

,15]heptad

.0.03,8.011

racyclo[8.7

16triazatet

1.12.

Dimethyl1

5-(4,17-

58%

1.5

1.9

60

2

4

6

431

4.4

-2.8

593

(14-methyl-

ntazahexac

clo[11.4.0.

3-(4methyl

13,17]

5yl)[(1,3diox

-5,8-dioxa-

2.5.19.20-

apentacycl

diazadidecah

yclo[11.11

dioxa-

yclo[9.9.2.0

heptadec-11-

02,6.07,12

cyclo

tetrazahexac

ahexahyd ro-

1,3 diaza

o[11.7.0.0

ydroant

9-thia-

.0.02,10.04

1,3diazadidec

2,7.08,21.01

yl-6methyl -

]heptadec-

hexyl)1,2,

2H-inden-5-

didecahydr

17,22]tetra

2,10.04,8.0

hracene-2,4-

3,9adiaza -

,8.015,23.0

a

2,17.018,22]

5-

4oxadiazol

ane-9,12-

yl)methylam

oanthracen

cosane-

14,19]icos

diol

2,3,4,

hydroanthrac

docos-5-yl)-

yl)methan

idi ne diol

ino]methanol

e-2,4-diol

9,12-diol

ene -2,4-diol

4a,4b,5,6,7,8,

ol

1ethanol

8adecahydro-

1.05,17.07,

cyclo[12.3.

diazapenta

11.18-

Dimethyl-

(3,4-

48%

-2.0

0.5

64

1

4

4

320

1.9

-2.8

623

10,15]octa

17.07,16.0

[12.3.1.05,

pentacyclo

diaza

11.18-

(4-Fluoro-

51%

-1.6

0.04

64

1

4

4

310

1.4

-2.8

624

cyclo[7.7.1

etra

8.12diazat

methyl-

-16-

Dimethoxy

(3,5-

52%

-1.5

-0.2

82

3

4

6

354

1.1

-2.6

627

12yl)meth

dec-

10,15]octa

17.07,16.0

[12.3.1.05,

pentacyclo

11.18diaza

(4-Methyl-

66%

0.03

0.3

64

1

4

4

306

1.7

-2.7

630

dec-

10,15]octa

17.07,16.0

[12.3.1.05,

pentacyclo

11.18diaza

4-methyl-

(3-Fluoro-

52%

-1.3

0.2

64

1

4

4

324

1.6

-3.0

631

17.07,16.0

[12.3.1.05,

cyclo

diazapenta

11. 18-

methoxy-

4-

(3-Chloro-

66%

0.2

0.01

73

2

4

5

356

1.3

-3.2

636

3yl)

nonadec-

16]

8, 17.011,

.3.1.05,18.0

ntacyclo[13

4.19diazape

(13-Methyl-

63%

0.04

0.6

64

1

4

4

320

2.1

-2.4

638

-5-yl)[(3-

1Htetraphen

decahydro-

2,12b-hexa

10,11,11a,1

6a,7,7a,8,9,

2,3,4,4a,5,6,

7,12a-diaza-

(9-Methyl-

41%

-3.4

1.7

59

4

4

5

404

3.5

-2.2

1357

4a-diaza-

5-yl](9-oxa-2,

o-1H-pentalen-

diazahexahydr

1,2-

methyl-6-thia-

hexyl) -1-

(4Chlorocyclo

[3-

31%

1.7

1.3

76

3

2

6

485

2.8

-3.1

1744

16.010,15]

.02,7.013,1

methanedio

dec-12-

10,15]octa

octadec-

12yl)meth

7]heptadec

anedol

yl)methane

1,2,3,4,4b,5, 6,

12-

piperidyl)

7,8,8a,10,10a-

l

methylamin

dideca

dec -12-

11yl)meth

yl)meth

o] methanol

ane diol diol

anediol

yl)methane diol

anediol

hydrophenanth r-2-yl)

Interaction analysis with kinase domain To further evaluate the interactions of putative bifunctional Plk1 inhibitors with the kinase domain, we demonstrated one compound (screening hit 202) in detail having a high scoring function to monitor the structural changes of ATP binding pocket upon binding to inhibitor. Binding of selected compound with the hinge region of Cys133 and Leu132 places the triazatetracyclo group between the adenine and ribose portion of ATP pocket; while, the thiol group was sandwiched between the two hydrophobic pockets created by Leu132, Leu59, Arg136 and Phe183 residues, respectively. Triazatetracyclo group formed two π-alkyl interactions with Arg136 and two π-sigma interactions with Leu59. Thiol group formed hydrogen bonding with Leu132 (at the roof of binding pocket) and Phe183 (at the base of the binding site) of hydrophobic pocket-I and II, respectively. Apart from this, π-sulfur bond was also observed between sulfur and aromatic ring of Phe183. Binding affinity was further monitored by π-π stacking interactions of phenyl ring of Phe183 with heptadecyl ring of screening hit 202. Methyl group was pointed towards the G-loop through, making an alkyl bond with Lys82 and Cys67. The aromatic ring formed π-alkyl interactions with Lys82 and Cys67 residues and a hydrophobic contact with Leu130. Methoxy group was pushed towards the hinge region and observed in polar contact with Leu59 and van dar Waal interactions with Arg136 (Fig. 4a).

Fig. 4 Binding characterization of representative bifunctional hit within ATP and substrate binding pockets of Plk1. (a) ATP binding pocket and (b) substrate binding pocket is depicted in gray line ribbon along with critical interacting residues in pink sticks, while bound inhibitor is shown in green sticks. Binding interactions are shown in dotted line where different color schemes represent distinct interactions.

Interaction analysis with polo box domain Polo box domain is ideally suited for the development of more specific Plk1 inhibitors due to its attractive structural folds and its specificity for protein-protein interactions. Binding of putative bifunctional Plk1 inhibitor within the substrate binding pocket of Plk1 was explored which showed the presence of electrostatic interaction between sulfur group of inhibitor and N2 atom of positively charged His538 along with π-sulfur bond between the aromatic ring of His538 and sulfur moiety. Conventional H-bond was formed between thiol group and H1 atom of His538. Triazatetracyclo group formed two π-sigma interactions with Arg557 and Leu491 apart from one conventional Hbond with H1 atom of Lys540. C1 atom of methyl ring formed strong hydrogen-bond with backbone amide (NH) of Trp414 and amide-π staking interaction with Leu491, while methyl group showed two π-alkyl interactions with Leu490 and Lys413 residues (Fig. 4b). Further to gauge the affinity and to cross validate the binding affinity of our screening hit to both domains (kinase and polo-box domains) of Plk1, we selected Plk1 kinase domain inhibitors; BI2536 [61], ON01910 [62], Scytonemin [63] and GSK461364A [64] for docking analysis against ATP and substrate binding pockets of Plk1. Previously, these inhibitors were reported to bind within the ATP binding site of Plk1. Our aim for performing their docking analysis against both Plk1 domains was to get confidence on dual binding behavior of our inhibitor (hit 202) in comparison to known ATP specific inhibitors. All reported inhibitors were well observed to be docked within the ATP binding pocket of Plk1, while they exhibited poor affinity for polo box domain (Fig. 5a and b). Conversely, our screening hit 202 was observed to be well docked within the potential Plk1 binding pockets with equal affinity.

Fig. 5 Comparative docking analysis of screening hit with published Plk1 inhibitors. (a) ATP binding pocket (b) substrate binding pocket of Plk1, depicted in gray ribbon. Known ATP binding inhibitors and screening hit (from present study) are shown in pink (BI2536), blue (GSK461364A), green (ON01910), yellow (Scytonemin) and red (screening hit) sticks. Our screening hit was observed to bind within the ATP and substrate binding pocket of Plk1 in comparison to known Plk1 inhibitors.

Generation of receptor and ligand-based pharmacophore models Unique structural properties of ATP and substrate binding pockets of Plk1 were used to generate the pharmacophore hypothesis describing the critical features for isolating Plk1 specific inhibitors. The critical interacting residues were observed with their important geometrical constraints and models were generated for each of the kinase and polo box domains bound with 10 unique inhibitors resulted through screening results. Common features shared by all generated hypotheses for kinase and polo box domains were shown in Fig. 6, while remaining features were discarded. Similarly, important pharmacophore features were mapped which revealed following chemical properties; 2 hydrogen bond acceptors, 2 hydrogen bond donors and 1 hydrophobic feature (Fig. 6).

Fig. 6 Pharmacophore feature mapping. Pharmacophore features of identified bifunctional inhibitor (center) and Plk1 (ATP and substrate binding pocket) are mapped (outer circle).Features for inhibitor molecule are depicted in green (hydrogen bond donor), pink (hydrogen bond acceptor), cyan (hydrophobic) and golden (aromatic) colors, while rotatable bonds are shown in blue. For Plk1 hydrophobicity, solvent accessible surface area (SAS), aromatic properties, hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) are shown in outer circles.

Molecular dynamics simulations By running molecular dynamics (MD) simulations at 10ns, resulting trajectories were thoroughly analyzed to gauge stability, convergence, energetic and structural properties during MD simulations. To assess the stability and fluctuations of protein alpha carbon atoms of bound and apo states, time series of root mean square deviation (RMSD) and root mean square fluctuation (RMSF) were evaluated. An overall convergence of energies indicated well behaved-systems. RMSD for each complex was calculated during 10 ns using apo form as a reference. The average RMSD values between C-alpha atom of complex and apo proteins were below 2Å, suggesting the system stability (Fig. 7a and b). Subsequent analysis of RMSF indicated more fluctuations near the ATP binding region of kinase domain and substrate binding pocket of polo box domain (Fig. 7c and d). Interestingly, all fluctuations were observed in the loop regions, while the critical residues of ATP (Phe183, Cyc133, Cys67, Leu59, Cys119, Leu132 and Glu140) and substrate binding pocket (Trp414, Asp416, His489, Lue490 and Leu491) were observed stable with slight changes in orientation to aid in binding.

Fig. 7 Plots to investigate stability and fluctuation of bound and apo states of Plk1. (a) RMSD plot of kinase domain (b) Polo box domain (c) RMSF plot of kinase domain and (d) polo box domain. Bound form is depicted in green and apo form in orange. RMSD plot showed stability for both kinase and polo box simulated systems (bound and apo). RMSF plot depicted fluctuations in the variable regions near the critical binding sites for both kinase and polo box domains, while, residues involved in interactions are stable.

Dynamic trajectories were generated at 1, 3, 6 and 9ns to measure the structural properties and important conformational switches of bound state in comparison to apo form. Through comparative analysis of kinase holoenzyme with its apo state, significant conformational changes were observed near the ATP binding pocket of Plk1. Upon binding of inhibitor, G-loop was significantly pushed outward (Fig. 8a) to open the pocket which created a conformational space accessed by the inhibitor. Similarly, outward movement of G-loop fetched Cys67 (hydrophobic pocket II) closer to the methyl ring of inhibitor. Hinge loop of ATP binding pocket was observed to bend slightly inward (Fig. 8b), resulting in modifying the orientation of Cys133 and Arg136 in a particular docking pose (Fig. 8c) that aided Cys133 to form a classical hydrogen bond with diol ring, while triazatetracyclo group of inhibitor formed three π-alkyl bonds with Arg136. Another significant change was the conversion of small helix (Leu179-Asn181) into variable region (Fig. 8d), resulting in an inward movement of nearby β-sheet which pushed Phe183 (Hydrophobic pocket I) towards the sulfur moiety of the inhibitor. Apart from these critical conformational switches, a number of changes were also observed in the loop regions and secondary structure elements lying far from the ATP binding pocket. By thorough analysis of conformational changes in ATP binding pocket, we monitored substrate binding pocket and captured structural changes upon binding of putative substrate competitive inhibitor. Due to differences in the shapes of ATP and substrate binding pockets, inhibitor bound very differently to substrate binding pocket in comparison to the ATP binding pocket. Owing to small cavity opening, methyl ring and thiol group entered well into the cavity, while bulky triazatetracyclo group was observed to be placed near the cavity opening. Variable region from His489-Pro499 was significantly twisted inward, while region from His538-Asp554 moved downward (Fig. 8e) thus building a conformational pose suitable for the entrance of inhibitor inside the cavity. These structural movements changed the orientation of His489, Leu490, Lue491 and electrostatic residues His538 and Lys540 (Fig. 8f) which bent towards the inhibitor and formed a number of interactions to increase the binding affinity. A considerable shift was observed in a region encompassing Val411-Lys420 residues (Fig. 8g). This important structural twist bowed down the Trp414 (critical for substrate binding) and Asp416 residues towards the inhibitor and stabilized the interactions through hydrogen bonding. A huge conformational change was observed in the βsheets of polo box domain, as upon inhibitor binding, all β-sheets were monitored to sink towards the cavity (Fig. 8h), resulting in a number of changes critical for holding the inhibitor inside the pocket. As residues involved in inhibitor binding are critical for substrate binding activity, our inhibitor might prove competitive for substrate binding, resulting in the inhibition of Plk1 function.

Fig. 8 Conformational changes of kinase and polo box domains of Plk1 upon inhibitor binding. Superimposition of kinase domain complex (pink) and polo box domain complex (orange) with their respective apo state (gray ribbon) reveal significant structural twist critical for inhibitor binding. a, b, c and d represent conformational changes within ATP binding site, while e, f, g and h represent changes within the substrate binding site. Discussion The knowledge of 3D (three-dimensional) structures of target proteins and their binding sites with ligands is vitally important for rational drug design. Although X-ray crystallography is a powerful tool in this regard, it is timeconsuming and expensive, and not all proteins can be successfully crystallized. Particularly, membrane proteins are challenging to crystallize and utmost will not dissolve in usual solvents. Therefore, so far very few membrane protein structures have been determined. Although recent breakthrough in high resolution NMR has indicated that it

is indeed very powerful tool in determining the 3D structures of membrane proteins and their complexes [65, 66], yet it is also time-consuming and costly. To acquire the structural information in a timely manner, series of 3D protein structures and their binding sites with ligands were derived using various structural bioinformatics tools [6769], and were found very useful for drug design. In this study, we aim to use various powerful structural bioinformatics tools. Plk1 is mainly expressed in the dividing cells with a much higher expression in cancer cells [70] which makes it a discriminating target for the development of cancer-specific small molecule drugs. Identification and designing of highly specific Plk1 inhibitors require a detailed knowledge of structural configuration. To date, no bifunctional Plk1 inhibitor exists that is able to target both ATP and substrate binding sites, simultaneously. Such inhibitors combine the affinity of ATP analogue in parallel to specificity of substrate analogue. Moreover, they must be able to discriminate the closely related protein kinases. The information of a binding pocket of a receptor to its ligand is very momentous for drug design, especially for carrying out mutagenesis studies [69]. In literature, binding pocket of a ligand at the receptor is generally defined by residues which have at least one heavy atom at a distance of 5 Å from a heavy atom of the ligand. This criterion was used to define the ATP-binding pocket in the Cdk5-Nck5a complex [71] that later proved to be very useful for identifying functional domains. A similar methodology was used to demarcate the binding pockets of numerous other receptor-ligand interactions which are significant for drug proposal [72-74]. Initially, Plk1 specific inhibitors were designed as ATP analogues to block the ATP binding pocket (classic target for kinase inhibitors) thereby avoiding access to ATP [26] and numeral ATP competitive Plk1 inhibitors has been identified [76, 77]. These include purvalanol A [75, 76], wortmannin [77], Scytonemin, ON01910, BI2536, BI6727 [78], PHA-680626 [57] and GSK461364A. However, there is a high probability that unspecific binding of ATP-competitive Plk1 inhibitors may occur to other members of the Plk family (with the exception of Plk4 which is dissimilar to the rest of Plks). For example, Plk1 inhibitors PHA-680626 and BI2536 are unable to distinguish Plk1, Plk2, and Plk3 for their binding and interact with all three forms. Similarly, second generation inhibitors such as ZK-thiazolidinone, NMS-P937, GSK461364 or BI2767 are believed to be more specific for Plk1 [78], therefore, it is a non-trivial task to develop Plk1-specific inhibitors. Due to the presence of polo box domains, an alternative inhibition strategy was developed to overcome the cross-reactivity of kinase inhibitors by targeting polo box domains of Plk1. Since PBDs are unique in Plks, identification of small molecule inhibitors to target the protein-protein interface is an attractive direction for blocking the vital biological process required for cancer therapy [75]. Poloxin, Thymoquinone, and Purpurogallin (PPG) are the only reported small molecules that potentially bind PBD of Plk1 [80]. Thymoquinone and Poloxin inhibit PBD resulting in abnormal mitosis [81] in cancer cells by impeding the Plk1 localization [82]. Similarly, PPG stops mitosis by causing cellular delocalization of Plk1 in vivo [83]. Apart from the small molecule inhibitors, several peptide derived inhibitors are also reported to inhibit polo box domains of Plk1 including MAGPMQSpTPLNGAKK [55], LLCSpTPNG [84] and PLHSpT [85].

As a crucial step to increase the inhibition specificity, we combined the affinity of ATP inhibitors and specificity of PBD inhibitors, resulting in the isolation of 10 putative bi-functional Plk1 inhibitors. Subsequently, screening hit 202 was demonstrated in detail through monitoring structural constraint of ATP and substrate binding pocket. This inhibitor was observed to dock with in the ATP binding cleft formed by N-terminal β-sheets and Cterminal α-helices connected by hinge region. Involvements of Phe183 (on the base), Cys67 (at the roof) and Leu132 are thought to be important to achieve selectivity against other Plks. As Leu132 is replaced by bulky Tyr or Phe residues in other kinases, contribution of Leu132 residue might provide increased specificity for Plk1. Similarly, for polo box domain, we monitored the contributions of His538, Lys540, Trp414 and Phe535 residues which are critical for the substrate binding. Extensive investigation of dynamic behavior of screening hit 202 revealed important structural details of ATP and substrate binding pockets of Plk1 upon inhibitor binding. For ATP binding pocket, a strikingly dominant and consistent behavior of Phe183, Cys67, Cys133, Arg136 and Leu32 residues were observed throughout the dynamics simulation run which suggested sufficient Plk1 selectivity. Furthermore, comparative analysis of apo state and inhibitor bound ATP pocket complexes exposed significant structural adjustments of hinge region, G-loop and hydrophobic pockets (Fig. 8a-d) that may prove crucial for the selectivity and specificity of Plk1. Similarly, dynamic behavior and structural constraints of substrate binding pocket highlighted crucial structural swings at residual level that are main determinants of interactions. During MD simulations, key substrate interacting residues (Trp414, His538 and Lys540) were predominantly detected in inhibitor binding which indicated that identified inhibitor may strongly compete with substrate for its binding site (Fig. 8e-h). Overall, our deep structural analysis through multiple docking algorithms revealed a high binding affinity of mentioned inhibitor against the ATP and substrate binding cavity of Plk1. Our study based on parallel targeting of Plk1 specific domains opens a new therapeutic perspective. Though the identified bifunctional inhibitor exhibits high binding affinity for ATP and substrate binding sites of Plk1, we cannot exclude the importance of its experimental validation to address the worthiness of our proposed data. In view of deep structural analysis, we propose that inhibitor 202 may prove more effectual and specific for Plk1 targeted therapy. As demonstrated in a series of recent publications [86-90] in developing new prediction methods or showing new findings via computational modeling, publicly accessible web-servers will significantly enhance their impacts [91], we shall make efforts in our future work to provide a web-server for the prediction method presented in this paper. Competing interests The authors of the manuscript declare no conflict of interest. References 1.

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We used in silico integrative approach and put our efforts to contribute in the identification of putative bifunctional Plk1 inhibitors; a most recent and interesting area of selective Plk1 inhibition. We screened database of 20,000 diverse chemical compounds against the ATP and substrate binding pockets of Plk1. Through subsequent filtering and binding analysis against potential drug target sites of Plk1, we extracted 26 putative novel bifunctional Plk1 inhibitors that fulfilled the ADMET and Rule of Five properties. We integrated the screening output with pharmacophore feature mapping and dynamics simulation studies.