Accepted Manuscript Prevention of OprD regulated antibiotic resistance in Pseudomonas aeruginosa biofilm Raavi, Swechha Mishra, Sangeeta Singh PII:

S0882-4010(17)30780-5

DOI:

10.1016/j.micpath.2017.08.007

Reference:

YMPAT 2396

To appear in:

Microbial Pathogenesis

Received Date: 30 June 2017 Revised Date:

2 August 2017

Accepted Date: 4 August 2017

Please cite this article as: Raavi , Mishra S, Singh S, Prevention of OprD regulated antibiotic resistance in Pseudomonas aeruginosa biofilm, Microbial Pathogenesis (2017), doi: 10.1016/ j.micpath.2017.08.007. 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 proof before it is published in its final 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.

ACCEPTED MANUSCRIPT

Prevention of OprD regulated antibiotic resistance in Pseudomonas aeruginosa biofilm Raavi1, Swechha Mishra2 Sangeeta Singh* Applied Science Department, Indian Institute of Information Technology, Allahabad - 211012

RI PT

1

AC C

EP

TE D

M AN U

SC

*Correspondence - [email protected], +91-09458867034

ACCEPTED MANUSCRIPT Acknowledgement: The authors are thankful to the Director, Indian Institute of Information Technology, Allahabad and Head, Applied Science Department, Indian Institute of Information Technology, Allahabad for providing the necessary facilities. Abstract

M AN U

SC

RI PT

In P.aeruginosa biofilms, the issue of antibiotic resistance is of particular importance due to increasing number of infections being reported in medical implants. The current study is focused on CzcR and CopR proteins which are part of two-component signal transduction systems (TCSs) - CzcR–CzcS and CopR–CopS respectively in P.aeruginosa. They both negatively regulate OprD porin expression which affects the intake of antibiotics like carbapenems. These two proteins can be treated as targets to combat antibiotic resistance in P.aeruginosa. Docking was performed on these proteins in search of inhibitors against the CzcR–CzcS and CopR–CopS TCSs. Efficient inhibitory ligands were evaluated on the basis of least binding energy, human oral absorption and ADME properties using a four-tier structure based virtual screening. The resulting ligands displayed high effective inhibitory property and satisfactory pharmacokinetics as compared to inhibitors which have been identified before for two-component signal transduction systems for gram negative bacteria. These potential inhibitors can now be used further in wet lab by performing selectivity assays to determine their inhibition rate against P.aeruginosa biofilms. Identification of potential leads may enable the development of new therapeutic strategies aimed at disrupting P.aeruginosa biofilms. Keywords: Antibiotic resistance, biofilms, Carbapenems, P.aeruginosa, Two Component Signal Transduction Systems.

Introduction

EP

TE D

Treatments of infectious diseases have become a challenging task due to antibiotic resistance in pathogenic bacteria. P.aeruginosa is one of the pathogenic bacteria responsible for such infections. The strenuous challenge with P. aeruginosa is its capability to promptly develop resistance throughout the period of treatment. Many broad spectrum antibiotics have become ineffective on P.aeruginosa due to its antibiotic resistant property [1]. P.aeruginosa biofilms with antibiotic resistance occur at an elevated frequency both in vitro and in lungs of Cystic Fibrosis patients [2]. They have also been reported in medical implants namely - venous catheters and artificial hip prosthesis [3]. P.aeruginosa, a gram negative bacterium develops resistance to antibiotics due to negative regulation of special water-filled protein channels called porins, present on outer membrane. These are substrate specific transport systems which permits the diffusion of vital supplements or nutrients which exist in scarce amount in cells proximity and atones for its wide-ranging permeability. These are culpable for the intake of sugar, amino acid, phosphates and certain antibiotics [4, 5].

AC C

Here, in this study we focus on OprD porin and how its negative regulation contributes to antibiotic resistance in P.aeruginosa biofilms. OprD expedites the utilization of common amino acids, small peptides, and carbapenem antibiotics, such as imipenem, biapenem, meropenem [6]. These antibiotics share common binding sites inside OprD channel [4]. Negative regulation of OprD provides P. aeruginosa a fundamental resistance level to carbapenems, particularly against imipenem [5, 7]. For OprD negative regulation mainly two TCSs are responsible, CzcR–CzcS and CopR–CopS regulatory systems [8, 9]. Two-component signal transduction systems (TCSs) present in bacteria, mainly consist of the histidine kinase (HK) sensor (embedded in membrane, histidine residue dimerizes and autophosphorylates on recognizing signal via ATP utilization) and the response regulator (RR) as its main components. After, phosphorylation output domain is activated to arbitrate the altered retort. Most of the RR proteins exhibit an output domain which harbors DNA-binding action, permits altered transcription [10-13]. TCSs are considered ideal and prominent target for upcoming remedies as anti-TCS agents or compounds for prevailing antibiotics and might be used for multi-drug resistant bacteria.

ACCEPTED MANUSCRIPT

RI PT

CzcR-CzcS two component signal transduction system is metal inducible [14]. Presence of Zinc, Cadmium, Cobalt or Copper positively regulates the activation of CzcR. Later on, CzcR negatively regulates the OprD porin protein, which is the transit path for carbapenem [15-17]. Studies shows that even without CzcR-CzcS TCS, OprD production is negatively regulated due to presence of another TCS, CopR-CopS. The increased production of CopR or CzcR regulator culminates in increased transcription of the czcC gene and decreased transcription of oprD gene. This advocates that a metal-dependent mechanism works to rule the OprD regulated resistance against carbapenem [18]. There are two different TCSs, whose proteins CzcR and CopR have similar mechanism of inducing negative regulation of OprD porin’s expression. Also, CzcR and CopR shows 73% similarity and 55% identity performed by pairwise sequence alignment. Thus, docking studies can be performed on both the proteins and similar resultant inhibitors can be used for inhibition of both target proteins.

SC

Main objective of this study is to prevent binding of metals to the sensor histidine kinase of CzcR and CopR proteins. Thus, it can prevent the autophoshorylation on a conserved histidine residue of both targets. As a result, there will be no phosphoryl group to be transferred on the RR (Response regulator) protein cognate receiver domain in CzcR-CzcS TCS and CopR-CopS TCS as well. Hence, no activation of both CzcR-CzcS and CopR-CopS TCS takes place. Thus, we can inhibit effectively CzcR-CzcS and CopR-CopS regulatory systems by same inhibitor [19,20]. Hence, mitigating negative regulation of OprD porin which leads to resistance against carbapenem.

M AN U

Material and Methods

All computational analysis were carried out on a RedHat 10.2 Linux platform running on a Lenovo PC with an Intel Core i3 processor and 4 GB of RAM. Protein Structure

EP

TE D

Crystal structures for CzcR and CopR have not been modeled till date. Hence, for structure modeling, fasta sequences for both TCSs proteins CzcR (UniProt: Q9RLJ1) and CopR (UniProt: Q02540) were retrieved from NCBI. These fasta sequences were used as templates for structure prediction.Their 3D structures were predicted using RaptorX an online server for Protein Structure and Function Prediction (http://raptorx.uchicago.edu/) [23]. Predicted structures were analyzed and verified using SAVES server provided by UCLA-DOE LAB (http://services.mbi.ucla.edu/SAVES/). Certain tests were performed namely- Ramachandran (RC) plot, ERRAT and Verify3D. Preparation of protein structures were carried out by Maestro, v9.7, Schrӧdinger, LLC,New York, NY, 2014, “Protein preparation wizard” [21]. Hydrogen bonds assignment tool was utilized for H-bonds network optimization. While energy minimization was done by the utilization of RMSD: 0.3Å and force field: OPLS 2005 (Optimized Potentials for Liquid Simulations 2005)in-built constraint. Modeled structure for CzcR and CopR proteins are shown in Fig. 2(a) and (b) respectively. RC plota

Erratb

Verify3Dc

CzcR

96.4%

70.30

91.96%

copR

94.2%

65.596

98.23%

AC C

Receptor

a. RC plot, provides percentage of residues present in allowed region. b. Errat provides overall quality of predicted structure. c. Verfiy3D provides 3D-1D score>=0.2 (above 8% amino acids).

Table 1: Parameters for modelled target proteins

ACCEPTED MANUSCRIPT Active Site Prediction

180

RI PT

No active sites for both target proteins have been reported. Therefore active sites prediction was performed using SiteMap, v3.0, Schrӧdinger, LLC, New York, NY, 2014 [22, 23]. Site map depends on the site as a whole; it explicitly displays the shape and extent of philic and phobic regions. It generates an overall SiteScore which helps in recognizing sites which are agreeable for binding in co-crystallized complexes. Binding sites having foremost site scores were selected as an active site and also an essential condition for receptor grid generation in both CzcR and CopR TCS receptors. 180

-180 -180

0 ɸ

180

SC

Ψ0

M AN U

Ψ0

-180

-180

0 ɸ

(b)

TE D

(a)

AC C

EP

Fig.1 Ramachandran plot for (a) Czcr and (b) CopR Proteins

(a)

180

(b)

SC

RI PT

ACCEPTED MANUSCRIPT

M AN U

Fig. 2 (a) (b) Modelled structure for CzcR and CopR proteins respectively.

Grid Generation

TE D

Receptor grids for both TCSs receptors were generated using Receptor Grid Generation of Glide module of v6.2, Schӧdinger, LLC, New York, NY, 2014. It helped in attaining accurate binding with desired active site. Receptor structure was defined by expelling any other possible co-crystallized ligand presence, settled on position and also on active site size [24]. Receptor’s Grid points for x, y, z axis (5.27, 3.64, 2.55) for CzcR and (3.65, 4.64, 2.46) for CopR were used accordingly inside the grid parameters.Grid generations for both receptors were carried out via using OPLS 2005. Ligand selection and Phase Database Preparation

AC C

EP

No inhibitors have been identified specifically for CzcR-CzcS and CopR-CopS regulatory systems till date. As histidine kinase has been accountable for stimulus recognition, succeeding autophosphorylation in a TCS, Antikinases from various TCSs were used as ligands. Here, antikinases known of various TCSs present in Gram negative bacteria were used as inhibitors. Antikinases act by binding the extremely conserved autokinase domains and therefore inhibit autophosphorylation [25, 26]. Thus, inhibiting the down regulation of OprD porin. Given below the antikinases which were used as inhibitory ligands for CzcR-CzcS and CopR-CopS TCSs- LED209 [27]. - Thienopyridine [28]. - Imidazole derivatives [29]. - Radiciciol [10].

ACCEPTED MANUSCRIPT Following are details of the antikinases mentioned above used as control and also for generating ligand database for docking studies against the given targets:

Antikinases as ligands

Properties

IUPAC nomenclature N-phenyl-4-[[phenyl amino)thioxomethyl]amino]benzenesulfonamide

Thienopyridine

It is a new class of ATP inhibitors against bacterial histidine kinases.

3, 6-diamino-5-cyano-4-phenylthieno[2,3-b]pyridine-2-carboxylic acid (4-bromo-phenyl)-amide.

Imidazole derivatives

They inhibits histidine kinase sensor of YycG/YycF TCS

SC

RI PT

It inhibits Qsec receptor a membrane bound histidine sensor kinase in gram negative pathogen.

LED209

8-chloro-9, 11-dihydroxy-14-methyl1a, 14, 15, 15a-tetrahydro-6Hoxireno[e][2]benzoxacyclotetradecin6,12(7H)-dione.

M AN U

Radiciciol is a natural product. It inhibits histidine kinase sensorPhoQ/PhoP TCS. It is also a Hsp 90 (heat shock protein) inhibitor.

Radiciciol

Table 2: Properties of antikinases chosen as ligands against target Proteins

TE D

Retrieval of all ligand (control) structures was carried out in 2-Dimensional SDF format from pubchem. It was followed by conversion in 3D and for building extended library of ligands using LigPrep, version 3.3, Schrödinger, LLC, New York, NY, 2015. 3D conversion of ligands was necessary for compatibility with protein structures which itself were in 3D format. Following measures were taken while conversion of 2D ligands into 3D and for extended library construction -

EP

- 2D SDF file was given as input file of controls in Ligprep. -Tautomers of ligands were generated. Although specific chirality of every ligand (control) was retained.

AC C

- A limit of 32 conformers per ligand supplied as input was applied. - Low energy conformations per ligand were set as 1. -OPLS 2005 force fields and Truncated Newton Conjugate gradient (TNCG) were used for Geometry minimizations on the 3D database created. - Output format for file was 3D maestro. Compound libraries extension were based on the principle of similar property (molecules similar in structure probably harbours similar properties). For ligands screening and ADME properties (absorption, distribution, metabolism, and excretion) analysis QikProp, v3.9, Schrӧdinger, LLC, New York, NY, 2014 was used [30]. Both Molecular descriptors, and pharmacokinetically pertinent characteristics, were evaluated for all ligands from the phase database. Lipinski’s rule of five (RO5) was among the most important parameters used to demonstrate the drug likeness (orally active) pharmacokinetic outline of the ligands. An extended library of 3094 ligands was obtained.

ACCEPTED MANUSCRIPT Four-Tier Virtual Screening (Docking Study)

G(L)−G(P)− G(PL) =∆Gbind

M AN U

SC

RI PT

In order to identify potential inhibitors for CzcR and CopR protein, docking was performed using a Four-tier structure based virtual screening. The database was subjected to Glide based four-tier docking protocol (Glide, version 6.6, Schrödinger, LLC, New York, NY, 2015): High Throughput virtual screening (HTVS), Standard Precision (SP), Extra Precision (EP) and Molecular mechanics/ Generalized Born Surface Area (MM/GBSA) [25]. High throughput Virtual Screening dealt with database of ligands by reducing low energy conformers by using docking filters. It evaluated the retrieved ligands and the screened compounds which were eligible for second stage of SP docking. Standard Precision allowed weak binding too (used for large scale virtual screening). Intense penalties were imposed by Extra Precision on the ligands provided by SP, which in turn reduced false positives and could be exercised in lead optimization studies where only a small number of compounds can be taken into account for synthesis or other experiments. Considering the glide g (docking) score and glide e-model, the processing provided the potential leads in XP descriptor of virtual screening workflow. The XP visualizer evaluated the particular interactions like H-bonds, ligand- protein energies, hydrophobic, π − π stacking, root mean square deviation, desolvation, etc. In the end, Molecular Mechanics /Generalized Born model and Solvent Accessibility was used for relative estimation of binding similarity for a given database of ligands. (http://www.schrodinger.com/).In the MM-GB(PB)SA formula, the ligand (L) free binding energy to protein (P) for complex formation (PL) was acquired as the difference [31]:

The free energy (P, L, and PL molecular systems) was specified by the following equation TS(X)−Gsolv(X)+ EMM (X) = G(X)

Here, EMM denoted the molecular system X total molecular mechanics energy in the gas phase, Gsolv denoted solvation free energy. It interpreted the fact that X was surrounded by solvent, and S was the entropy of X.

TE D

ADME screening

EP

The Absorption, Distribution, Metabolism and Excretion (ADME) properties of the database were evaluated via QikProp. QikProp also calculated Molecular descriptors and pharmaceutical properties. Initially, compounds were nullified before the QikProp application. Otherwise, it leads to problem in prediction of descriptors in rational mode. QikProp predicted detailed analysis of physical and pharmaceutical properties which included, Percent human oral absorption, log P (octanol/water) and QP% features (especially for HERG and Caco-2 cells), Lipinski rule of 5 was used as an important parameter for classification of potential inhibitors as rational drug [32].

AC C

MD Simulation For performing the molecular dynamics simulation the best complex was chosen.Schrodinger was utilized for performing the task. Out of various complexes the one with minimum energy was chosen for the study. Once the simulation was done RMSD and RMSF plot of the complex was analyzed for ensuring the steadiness of the system and the conformational changes of the system during 20ns simulation.

Results and Discussion In-silico screening against CzcR and CopR receptors Screening against CzcR and CopR receptors was performed using extended library of 3094 ligands.The extended library of ligands was filtered in each step of four-tier virtual screening leading to resultant inhibitor. Primarily, compounds were filtered on the basis of high molecular weight (>500) and presence of reactive functional groups. Any compound which had both or one parameter was filtered out. After basic filter strategy, four-tier structure based virtual screening was used to predict potential leads for Czcr and CopR receptors.

ACCEPTED MANUSCRIPT

SC

CID91074817 features:

RI PT

Redocking of ligands used as controls was also performed using same four-tier structure based virtual screening.The analyzed results of screened compounds after virtual screening were compiled in Table 3, and leads’ 2D structures given in Fig.4.The glide g score taken into account as a function of scoring and the glide emodel (Kcal/mol) was utilized for ranking the ligands poses. As they combined the energy of non-bonded interactions along with extra internal energy for adjustable docking of generated ligand conformation. On the premise of docking & glide g-score and glide e-model & MM/GBSA binding energy, compound id’s named CID91074817 (IUPAC- [7-hydroxy- 3-(4- hydroxyphenyl)-4- oxochromen-5- yl] 2-aminoethanessulfonate) was more effective against both CzcR and CopR protein as compared to the reported antikinases: LED209, Radiciol, Imidiazole derivatives and thienopyridine. CID91074817 occupied the better binding efficiencies in both TCSc receptors with relatively high values of parameters analyzed. On the other hand, compounds CID5353918, CID1916699, CID3883, and CID71296108 were only specific antagonist to CzcR. Similarly, CID5311380, CID13586615, CID 447546, CID3869, CID60651 were specific antagonist to CopR.

M AN U

IUPAC name – [7-hydroxy-3-(4hydroxyphenyl)4oxochromen-5-yl] 2-aminoethanesulfonate. Molecular Formula: C17H15NO7S

Fig.3 3D conformer 91074817

CID91074817

- 4.658

CID5353918

- 4.406

CID5311380

—

glide g score

glide e model

MM/GBSA dG

- 4.686

- 61.398

- 4.782 —

CopR Receptor Docking score

glide g score

glide e model

MM/GBSA dG

- 56.730

- 5.644

- 5.672

- 63.262

- 56.096

- 41.079

- 52.888

—

—

—

—

—

—

- 4.712

- 5.088

- 48.401

- 61.319

—

—

—

—

EP

Docking score

AC C

Compounds

TE D

CzcR Receptor

CID1916699

- 2.608

- 2.608

- 45.994

CID13586615

—

—

—

—

- 5.256

- 5.260

- 26.867

- 32.338

- 3.576

- 3.820

- 57.439

- 45.408

—

—

—

—

—

—

—

—

- 4.550

- 4.550

- 69.862

- 58.963

- 6.560

- 7.774

-71.144

- 54.421

—

—

—

—

CID3869

—

—

—

—

- 7.966

- 7.986

-70.583

- 67.063

CID60651

—

—

—

—

- 6.928

- 7.029

- 41.165

- 51.060

CID3883

CID447546

CID71296108

- 46.388

Table 3: Binding efficacy comparison on the premise of docking score, glide g -score, glide e-model and screened antikinases binding against both CzcR and CopR receptors. ‘—’ symbol represents the corresponding lead which is not a top ranked screened candidate for the receptor

b. CID5353918

c. CID5311380

M AN U

SC

a. CID9104817

RI PT

ACCEPTED MANUSCRIPT

e. CID1916699

f. CID3883

EP

TE D

d. CID13586615

AC C

g. CID447546

i.CID3869

h.CID71296103

j. CID60651

Fig. 4 a-j Structures of all potential antikinase inhibitors

ACCEPTED MANUSCRIPT Binding Mode analysis of CID91074817 in CzcR and CopR receptors CID91074817 bound to Czcr with a -4.658 docking score and -61.398 Kcal/molof glide energy for its inhibition (Table 3). In CzcR-CID91074817 binding, interaction of hydrogen bonds with the side chain amino acid residues ARG 117 and ASP 180 were recognized. Five hydrophobic interactions with the amino acid residues LEU 177, PRO 122, MET 123, ALA 156 and LEU 160 were observed. One negative charge interaction with ASP 180 residue and three positive charge interactions with ARG 163, ARG 117 and ARG 73 amino acid

RI PT

residues were also identified (Fig. 5(a)), which formed a π-π stacking with HIS 139 residue also. Six polar interactions were present which included, SER 176, GLN 120, THR 152, HIS 139, HIS 159 and GLN 124 amino acid residues. The strong interaction of CopR with CID91074817 was identified by highest docking score and glide energy i.e. -5.644 and -63.262 Kcal/mol respectively (Table 3). In CopR-CID91074817, two hydrogen bonds interactions were identified with backbone amino acid residues VAL 142 and ILE 149. Similarly,

SC

interaction of two hydrogen bonds with ARG 148 and ASP 150 side chain amino acid residues. One negative charge interaction with ASP 150 and four positive interactions with ARG 148, ARG 141, ARG 140 and ARG 67 residues were observed (Fig. 5(b)).Two polar interactions were also identified with THR 72 and THR 152

VAL 142 amino acid residues (Table 4).

M AN U

residues. It also showed six hydrophobic interactions with MET 71, ALA 153, LEU 151, PHE 156, ILE 149,

Comparison between identified and reported inhibitors

Docking studies were performed on extended library of ligands and control ligands separately with target proteins. Docking, glide g score and glide e model energy were taken into account to compare the resultant

TE D

inhibitor CID91074817 of Czcr and CopR receptors with previously reported antikinases for TCSs receptors. The result as mentioned in Table 3 displayed that CID91074817 has the highest glide g score, docking score along with glide e model energy and MM/GBSA binding energy as compared to previously reported

ADME screening

EP

antikinases.

Qikprop predicted prominent molecular descriptors and pharmaceutical characteristics as given in Table 5.

AC C

Molecular weights, Hydrogen-bond acceptors, Hydrogen-bond donors, Q log S, QP log po/w, QP log caco, QP log HERG and absorption percentage by human orally were considered as a critical parameter [33]. Resultant inhibitor CID91074817 was in permitted Lipinski’s rule of 5 range [32]. Being in this range makes it completely suitable for wet lab testing.

ACCEPTED MANUSCRIPT Type of Interactions

CzcR-CID91074817

H- bonds No.

CopR-CID91074817

2

ARG 117,ASP 180

VAL 142, ILE 149, ARG 148, ASP 150

π-π stacking

HIS 139

Hydrophobic Interactions

LEU 177, PRO 156, LEU 160

Positive Interactions

ARG 163, ARG 117,ARG 73

-

ARG 148, ARG 141, ARG 140, ARG 67

M AN U

ASP 150

SER 176, GLN 120, THR 152, HIS 139, HIS 159, GLN 124

Polar Interactions

MET 71,ALA 153,LEU 151, PHE 156, ILE 149, VAL 142

SC

122,MET 123,ALA

ASP 180

Negative Interactions

RI PT

H-bond interacting residues

4

THR 72, THR 152

Table 4: Comparison of interactions of CID91074817 with CzcR and CopR receptors

AC C

EP

TE D

Note: ‘-’symbol represents no interactions

(a)

(b)

ACCEPTED MANUSCRIPT

M AN U

SC

RI PT

Fig. 5 (a), (b) interaction of Ligand CID91074817 with Czcr and copR receptor respectively.

AC C

EP

TE D

(a)

(b) Fig.6 (a), (b) represents binding of CID91074817 with CzcR and CopR proteins respectively. Here, ligand in orange (highlighted by blue mesh) binds to target proteins (area in contact with ligands highlighted by grey mesh).

ACCEPTED MANUSCRIPT

CzcR Receptor Docking score

CID 91074817

- 4.658

Radiciol

- 2.691

LED209

glide g score

glide e model

Docking score

glide g score

- 4.686

- 61.398

- 5.644

- 5.672

- 3.405

- 4.082

- 38.664

- 4.795

G lide e model

- 63.262 - 41.134

RI PT

Compounds

CopR Receptor

- 4.439

- 4.439

- 50.993

- 3.488

- 3.488

- 44.009

Thienopyridine

- 3.354

- 3.354

- 16.058

- 4.531

- 4.531

- 23.583

Imidazole Derivatives

- 4.389

- 4.389

- 30.376

- 4.768

- 4.768

- 29.436

SC

Table 5: Comparisons between identified potental inhibitor (91074817) and reported antikinases

MWa

HBAb

HBDc

CID91074817

377.368

8.5

3.0

QP logpo/wd

Q log Se

QP log cacof

QP logHERGg

-0.009

-1.514

6.056

-5.746

% Human oral absorptionh 40.894

TE D

Compound

M AN U

a. Molecular weight of the compound (160 to 500). b. Predicted number of H-Bond acceptors (