WITH Pseudomonas aeruginosa QS...

8 MOLECULAR DOCKING OF PHYTOCHEMICALS

WITH Pseudomonas aeruginosa QS PROTEINS

Molecular docking of phytochemicals with Pseudomonas aeruginosa QS proteins 8.1 Introduction

The development of in silico technology has offered a novel screening strategy for

identification of hit compounds and lead optimization for new drug entities, while

providing the benefits of envisaging the interactions of the compounds with target

proteins. The computational tools afford a cost effective and a rational approach of drug

designing, which aids in reduced time, labour and money investments through the course

of a new drug discovery. The computer aided drug designing is classified as structure

based drug design (SBDD) and ligand based drug design (LBDD). SBDD is the most

commonly followed approach, wherein the structure of the target protein is available, and

ligand molecules are screened for binding with the protein. LBDD is also gaining more

popularity; it involves screening the proteins library for binding with the known active

ligand/s (Meng et al. 2011).

Molecular docking or ‘the process of placing a ligand in the putative binding site of a

macromolecular target, followed by estimating the tightness of binding’ is a useful tool in

SBDD. The docking process involves modelling of the orientation of the ligand in the

protein binding site/s, called as a pose, and further computing a docking score based on

the binding affinity and stability of the complex. The important steps involved in docking

are target (binding site) identification, sampling conformation of the ligand in the target

active site followed by ranking the conformations by a suitable scoring function

(Kalyaanamoorthy & Chen 2011; Meng et al. 2011). The structural information for many

proteins has been obtained with the help of amino acid sequencing, X-ray crystallography

and NMR spectroscopy, and is publically accessible via the online available resource, the

Protein Data Bank (PDB)(www.rcsb.org/pdb). However, when the protein structure is not

available, homology modelling is generally used, which predicts the target structure

based on the structure of homologous protein. Few prominent examples of the homology

models include Mycobacterium avium complex dihydrofolate reductase, DNA methyl

transferase (DNMT) (Kharkar & Kulkarni 2003; Medina-Franco et al. 2011). Generally,

due to the experimental studies on native ligand-receptor interactions or the mutational

studies to determine the catalytic residues, the active site of the protein can be easily

located; however, in case of the absence of this knowledge, computational programs such

School of Science, SVKM’s NMIMS (deemed-to-be) University 13

Molecular docking of phytochemicals with Pseudomonas aeruginosa QS proteins as GRID, POCKET, SurfNet, Qsite finder, CASTp can be used for predicting the putative

binding sites. Once the 3D structure of the protein or its active site is available, the set of

compounds to be screened are docked with the protein. Generally the known library of

natural or synthetic compounds is screened, in a process called as virtual screening.

Alternatively, de novo drug design may be employed, wherein fragment designing is used

to generate new molecules that can effectively bind to the active site residues. Next, the

possible conformations of the ligand within the receptor’s active site (poses) are

modelled, referred to as the sampling methods. Several sampling algorithms are

available, such as matching algorithm, incremental construction method, genetic

algorithms and Monte Carlo algorithm. Finally, the different conformations are ranked by

the scoring function. The definition of scoring function (SF), as given by

Kalyaanamoorthy & Chen, states “the mathematical methods to predict the strength of

the interaction between docked molecules”. Different SFs are available such as force

field based, empirical and knowledge based SFs. More recently, consensus scoring

strategy has been used, wherein the docked poses are scored by several scoring functions

and a consensus is taken to minimize the number of false positives. The most favoured

pose of the ligand is indicated by the highest docking score (with a higher negative

value). Various docking programs are available, such as GOLD, Autodock, Dock, Glide,

FlexX; these differ in the many aspects, such as how the ligand is placed at the beginning,

how it is moved in the binding pocket, binding site flexibility, treatment of metals, water,

cofactors, and the scoring function (Kalyaanamoorthy & Chen 2011; Meng et al. 2011).

The docking process has several limitations – the availability of the 3D structure of the

macromolecular target; the limitation of SF, since no SF is suitable universally; receptor

site flexibility, since in the natural scenario, binding of the ligand might involve

molecular rearrangements at the active site to accommodate the ligand; failure to consider

the tautomeric, ionized and protonated states of the ligands; problem in the docking of

oligopeptides which is still in infancy. Several advancements have been made with

respect to receptor flexibility; scoring functions with respect to entropy, solvation and

target specificity; post processing to improve docking accuracy and docking into

homology models. However, more improvements are needed for applying the docking

School of Science, SVKM’s NMIMS (deemed-to-be) University Page 214

Molecular docking of phytochemicals with Pseudomonas aeruginosa QS proteins

methodology to a broader arena of drug targets and to make it more beneficial in the hit

discovery as well as further process of drug discovery and development (Yuriev &

Ramsland 2013).

Despite the drawbacks in molecular docking, the combination of computational

modelling with experimental validation have been fruitful in the discovery of potential

hits from the libraries of synthetic and natural compounds, prominently for the treatment

of prostate cancer (Armutlu et al. 2009), inhibition of DNMT as anticancer treatment

(Medina-Franco et al. 2011) and treatment of Alzheimer’s disease (Lu et al. 2011).

The proper functioning of LuxI and LuxR proteins in the LuxR/I pathway of QS is vital

for the regulation of the pathway and the expression of the QS controlled genes (as

described in Ch. 2 Section 2.3.1.2). Thus, LuxI and LuxR proteins serve as

prospective targets for designing new anti-quorum sensing drugs. In P.

aeruginosa, LasI (homologous to LuxI) is known to catalyze the synthesis of a long

acyl-HSL, 3-oxo-C12-HSL, which at its threshold concentration, binds to the

transcriptional factor, LasR (homologous to LuxR). The activated LasR complex

further binds to specific promoter regions, regulating the expression of specific

downstream genes (van Delden & Iglewski 1998). Thus, it can be understood that

targeting LasI and LasR can be beneficial for controlling the QS pathway in this

organism. Apart from the Las proteins, the RhlI and RhlR proteins of P. aeruginosa

are also important anti-QS targets; and in Acinetobacter baumannii, the AbaI and AbaR

proteins, homologous to the LasI and LasR respectively, serve as the potential

therapeutic targets.

Previous reports have indicated the potential of LasR inhibitory compounds, such as the

furanones, AHL analogues, etc., in controlling QS regulated gene expression and in vitro

as well as in vivo bacterial virulence (Manefield et al. 1999; Ishida et al. 2007; Geske et

al. 2008; Kim et al. 2008). Hence, LasR has been considered an important target for

discovery of novel QS inhibitors of the organism. Some of the docking studies in the last

decade have thus focussed on the screening of huge compound libraries for LasR

inhibitors, and some potential hits have been identified such as AHL analgoues, triphenyl



compounds, sulphur containing compounds, salicylic acid, chlorzoxazone and

nifuroxazide (Müh et al. 2006; Yang et al. 2009; Borlee et al. 2010; Skovstrup et al.

2013; Tan et al. 2013).

In the present study, the molecules identified in fraction no.5 (described in Ch. 7 Section

7.3) were docked with the LasI and LasR proteins of P. aeruginosa. The structures of

RhlI/R and AbaI/R are not available in the PDB; hence, it was not possible to study the

ligand binding for these proteins. To current knowledge, this is the first docking study

based on LasI as the target protein. The molecules that showed higher docking scores for

LasI and LasR were considered to be the probable QS inhibitors from the plant, Guduchi.


Molecular docking of phytochemicals with Pseudomonas aeruginosa QS proteins 8.2 Materials and methods

Hardware and software

All the molecular docking studies described herein were performed on Lenovo UltraBook

Laptop (Intel® Core™ i5-3317U CPU @ 1.70 GHz, RAM 4 GB) running Windows 7

Home Basic Operating System. The automated docking software, Schrödinger Small-

Molecule Drug Discovery Suite Release 2013-1 (Schrödinger, LLC 2013) and the

products included therein was used for the docking studies.

8.2.1 Docking studies

• Target structures

Glide version 5.7 (Friesner et al. 2004, 2006; Halgren et al. 2004), as implemented in

Schrödinger suite 2013-1, was used for performing docking studies of the test

molecules identified in active fraction no.5 as well as known LasR antagonists (Yang

et al. 2009) with two proteins, LasI and LasR. The structures of the test molecules and

known LasR inhibitors have been represented in Figure 8.1.

For LasI crystal structure, search in Protein Data Bank (PDB) yielded only one

structure (PDB ID 1RO5) (Gould et al. 2004). This structure lacked the native ligand

in it. But there is sufficient information about the acyl-chain binding pocket. It is

mainly formed by residues Trp33, Trp69, Met79, Leu102, Phe105, Thr121, Leu122,

Met125, Leu140, Thr142, Thr144, Val148, Met151, Met152, Ala155, Leu157, Ile178

and Leu188. Many residues such as Met79, Phe105, Thr142 and Thr144 play crucial

role in positioning the acyl chain in the proper orientation during catalysis and are

well conserved in AHL synthases. This information was used to select the centre of

the enclosing box for docking studies on LasI.

For LasR, PDB search resulted in 33 hits. Careful examination led to the selection of

P. aeruginosa LasR bound to its autoinducer (PDB ID 2UV0)(Bottomley et al. 2007).

The ligand binding domain of LasR, containing the autoinducer was used for placing

the enclosing box during receptor grid generation.


O

OH

2-methoxy-4-vinylphenol

OH

2,4-Bis(2-methyl-2-propanyl)phenol

O

O

OO

O

O

O

O

2,3,4-triacetyloxybutyl acetate

O OH

2-(5-ethenyl-5-methyloxolan-2-yl)propan-2-ol

O

(E)-heptadec-15-enal

O

O

Methyl hexadecanoate

O

O

Methyl octadecanoate

O

O

Methyl 16-methylheptadecanoate

OH

Tetracosan-1-ol

OH

Henicosan-1-ol

Figure 8.1 Structures of the test molecules (identified in active fraction) used in the docking studies

O

O

N

O O

H

3-oxo-C12-HSL (native ligand)

NHO

NH

OCl

TP5

N+

O-

N+

O

O-

4-Nitro-pyridine-N-oxide

Cl NH

O

O

Chlorzoxazone

OH

OHO

Salicylic acid

O

O

O

OH

Patulin

O

O

BrBr

C30 furanone

OO

OH

O

Penicillic acid

N

O O

HOH

3-oxo-C12-(2-aminophenol)

OH

O

NHNON+

O

O-

Nifuroxazide

Figure 8.2 Structures of native ligand and known LasR inhibitors used in docking studies

Molecular docking of phytochemicals with Pseudomonas aeruginosa QS proteins • Receptor grid generation

o LasI: The crystal structure of P. aeruginosa (PDB ID 1RO5) was imported in

Maestro 9.4 and subjected to protein preparation. All the default settings were

used as implemented. The termini were capped, missing side chains were added,

H-bonds were optimized and in the end, restrained minimization was performed

wherein the heavy atoms were converged to root mean square deviation (RMSD)

0.3 Å. The prepared protein was then subjected to Receptor Grid Generation

using Glide 5.9. In the absence of the native ligand, centroid of residue Thr144

was used for placing the enclosing box. Water molecules were removed from the

binding site and the generated grid was then used for docking.

o LasR: The LasR crystal structure (PDB ID 2UV0) was subjected to Protein

Preparation as described above. Glide 5.9 was then used for generating Receptor

Grid. The ligand centroid was used as the centre of the enclosing box.

• Small molecule ligands preparation

The ligand structures presented in Figure 8.1 & Figure 8.2 were built in Maestro. All

the structures were then subjected to LigPrep 2.6, as implemented in Schrödinger

suite 2013-1. All the default settings were used. This set of molecules was then

subjected to docking studies.

For docking studies, extra precision (XP) mode was used. The docked poses were

minimized and RMSD to input ligand geometries were calculated.


Molecular docking of phytochemicals with Pseudomonas aeruginosa QS proteins 8.3 Results

• Docking of compounds with P. aeruginosa LasI protein

Table 8.1 depicts the docking scores obtained for binding of the ligand molecules

with the active site of LasI target protein. Highest docking score (-6.529) was

exhibited by 2,3,4-triacetyloxybutyl acetate; the docking poses and binding

interactions of the acyl chain of 3-oxo-C12-HSL and 2,3,4-triacetyloxybutyl acetate

with LasI active site has been shown in Figure 8.3.

Type Compound Docking Score Native Ligand 3-oxo-C12-HSL -6.98

Test molecules

2,4-Bis(2-methyl-2-propanyl)phenol -4.345 Methyl hexadecanoate -2.016 Methyl 16-methylheptadecanoate -3.355 2-Methoxy-4-vinylphenol -3.862 2,3,4-triacetyloxybutyl acetate -6.529 2-(5-ethenyl-5-methyloxolan-2-yl)propan-2-ol -4.123 (E)-heptadec-15-enal -1.868 Henicosan-1-ol -3.324 Methyl octadecanoate -3.441 Tetracosan-1-ol -3.126

Known LasR

inhibitors

Triphenyl compound 5 (TP5) -3.2 4-Nitro-pyridine-N-oxide -1.74 Penicillic acid -4.91 C30 furanone -3.77 Patulin -5.14 Salicylic acid -6.78 Chlorzoxazone -3.85 Nifuroxazide -7.94 3-Oxo-C12-(2-aminophenol) -6.51

Table 8.1 Docking scores of native ligands, known LasR inhibitors and test molecules for binding in the active site of LasI



Figure 8.3 Binding mode of 3-oxo-C12-HSL (a) and 2,3,4-triacetyloxybutyl acetate (b) in the acyl chain binding site of LasI protein. The ligands are shown as ball-and-stick model with H-bonds represented by yellow-dotted lines; oxygen atoms in red and H atoms in white.


Molecular docking of phytochemicals with Pseudomonas aeruginosa QS proteins • Docking of compounds with P. aeruginosa LasR protein

Table 8.2 depicts the docking scores obtained for binding of the ligand molecules

with the active site of LasR target protein. Higher docking scores were exhibited by

methyl 16-methylheptadecanoate (-6.564), 2-(5-ethenyl-5-methyloxolan-2-yl)propan-

2-ol (-6.328), methyl hexadecanoate (-6.209) and 2-methoxy-4-vinylphenol (-6.174).

The docking pose of 3-oxo-C12-HSL and methyl 16-methylheptadecanoate with the

active site of LasR protein has been represented in Figure 8.4. 2D representation of

the binding interactions for some of the molecules (3-oxo-C12-HSL, 2-(5-ethenyl-5-

methyloxolan-2-yl)propan-2-ol, methylhexadecanoate, and 3-oxo-C12-2-

aminophenol) with the active site of LasR has been depicted in Figure 8.5 to Figure

8.8.

Figure 8.4 Binding mode of 3-oxo-C12-HSL (blue) and methyl 16-methylheptadecanoate (white) in the active site of LasR. The ligands are shown as ball-and-stick model with H-bonds represented by yellow-dotted lines.



Type Compound Docking Score Native Ligand 3-oxo-C12-HSL -9.695

Test molecules

2,4-Bis(2-methyl-2-propanyl)phenol -2.172 Methyl hexadecanoate -6.209 Methyl 16-methylheptadecanoate -6.564 2-Methoxy-4-vinylphenol -6.174 2,3,4-triacetyloxybutyl acetate -4.453 2-(5-ethenyl-5-methyloxolan-2-yl)propan-2-ol -6.328 (E)-heptadec-15-enal -5.295 Henicosan-1-ol -- Methyl octadecanoate -5.254 Tetracosan-1-ol --

Known LasR

inhibitors

Triphenyl compound 5 (TP5) -2.473 4-Nitro-pyridine-N-oxide -3.432 Penicillic acid -4.143 C30 furanone -4.71 Patulin -5.221 Salicylic acid -5.982 Chlorzoxazone -6.115 Nifuroxazide -8.36 3-Oxo-C12-(2-aminophenol) -11.98

Table 8.2 Docking scores of native ligands, known LasR inhibitors and test molecules for binding in the ligand binding site of LasR; -- denotes no binding.



Figure 8.5 2D representation of the binding mode of 3-oxo-C12-HSL (native ligand) with the amino acid residues at the active site of LasR; arrows denote hydrogen bonds.

Figure 8.6 2D representation of the binding mode of 2-(5-ethenyl-5-methyloxolan-2-yl)propan-2-ol with the amino acid residues at the active site of LasR; arrow denotes hydrogen bond.



Figure 8.7 2D representation of the binding mode of methyl hexadecanoate with the amino acid residues at the active site of LasR; arrow denotes hydrogen bond.



Figure 8.8 2D representation of the binding mode of 3-oxo-C12-(2-aminophenol) with the amino acid residues at the active site of LasR; arrows denote hydrogen bonds.


Molecular docking of phytochemicals with Pseudomonas aeruginosa QS proteins 8.4 Discussion

Molecular docking predicts the binding of ligand molecules with target proteins, and

based on their docking score, the top ranking compounds are considered as the probable

target protein inhibitors. In the present study, the test ligands, known LasR antagonists

(Yang et al. 2009) as well as the native ligand were docked with the QS proteins, LasI

and LasR. Most of the studies on QSIs have been focussed on AHL analogues that act by

competitive inhibition of LasR. However, LasI is also an important target protein for

controlling quorum sensing, since inhibition of LasI will directly affect AHL production.

A prospective inhibitor of the LuxI homologous protein, TofI of the bacterium,

Burkholderia glumae, was identified in a study by Chung et al. 2011. Here, with the help

of in vitro experimentation, the AHL analogue, J8-C8, was found to inhibit the synthesis

of AHLs by the enzyme TofI. Further, docking of the compound with the ligand binding

sites of TofI was performed, wherein it showed high affinity for the acyl binding site of

TofI, suggesting a competitive inhibition of the enzyme activity.

So, in the present study, in addition to the LasR protein, the ligands were docked with

LasI protein as well. Among the test molecules, 2,3,4-triacetyloxybutyl acetate (-6.529)

showed the highest score, and is suggested to be a probable LasI inhibitor. When the

binding interactions of 2,3,4-triacetyloxybutyl acetate and the acyl chain of 3-oxo-C12-

HSL with LasI was compared, both compounds showed the possibilities of hydrogen

bonds with conserved residues, Arg-30, Phe-105, Thr-144, and in addition to this, 2,3,4-

triacetyloxybutyl acetate showed hydrogen bond formation with Ile-107 (Figure 8.3).

This indicated a strong possibility of the above test compound to be an antagonist of LasI

synthase protein, which will result in reduced production of AHLs, and thus attenuation

of QS and its regulated phenotypes. Some of the known LasR inhibitors presented

docking scores close to that for 3-oxo-C12-HSL (-6.975), such as nifuroxazide (-7.939),

salicylic acid (-6.782) and 3-oxo-C12-(2-aminophenol) (-6.507). It can be proposed that

the above LasR inhibitors may also affect LasI activity; hence, more studies on this

aspect will be helpful.


Molecular docking of phytochemicals with Pseudomonas aeruginosa QS proteins In case of LasR docking in the present study, the known antagonists showed docking

score in the range of -2.473 to -11.979 and for the native ligand, it was -9.695. The test

ligands that showed docking scores comparable to the score range of known inhibitors,

could possibly involve in competitive inhibition of LasR binding with its native ligand, 3-

oxo-C12-HSL. Among these, the top ranking ligands for LasR that showed docking

scores around -6.0, include methyl 16-methylheptadecanoate, 2-(5-ethenyl-5-

methyloxolan-2-yl) propan-2-ol, methyl hexadecanoate and 2-methoxy-4-vinylphenol,

and these may serve as effective LasR inhibitors.

The LasR protein, when bound to its autoinducer, has been found to exist as a dimeric

protein. Each monomer has a C-terminal DNA binding motif and N-terminal autoinducer

binding motif. The autoinducer, 3-oxo-C12-HSL, fits into a large hydrophobic region via

hydrogen bond formation with the amino acid residues such as Tyr-56, Trp-60, Asp-73,

Thr-75, Ser-129, and it also binds to Arg-61 through hydrogen bonding with a water

molecule bound to the residue. Tyr-56, Trp-60, Asp-73 and Ser-129 are highly conserved

among the LuxR homologues (Bottomley et al. 2007). Due to structural similarity of

methyl 16-methylheptadecanoate with the acyl chain of 3-oxo-C12-HSL, their binding

interactions were also similar to some extent, as shown in Figure 8.4. Methyl hexanoate

was also found to have similar hydrogen bond interactions, as in the case of methyl 16-

methylheptadecanoate (Figure 8.7), suggesting its possible involvement in LasR

inhibition. The docking pose of 2-(5-ethenyl-5-methyloxolan-2-yl) propan-2-ol with

LasR, showed the probability of a covalent bond formation with the Cys-79 residue. This

hydrophobic amino acid has been found to line the cavity that binds the long acyl chain

of 3-oxo-C12-HSL, seen only in LasR, rather than other LuxR homologues. Hence, the

above test ligand may act as a specific covalent inhibitor of LasR (Figure 8.6). 2-

methoxy-4-vinylphenol has a small structure that can easily fit into the ligand binding site

of LasR; however, it did not exhibit any interaction with the amino acid residues.

Triphenyl compounds (TP1 to TP5) were screened for their ability to inhibit QS using

molecular docking with LasR and further bioassay evaluation (Müh et al. 2006). Four

compounds were found to act as agonists, while a single compound, TP5 exhibited


Molecular docking of phytochemicals with Pseudomonas aeruginosa QS proteins antagonistic activity. By performing detailed analysis of the LasR-binding interactions of

the above compounds, it was predicted that the LasR agonists will make both Trp-60 and

Asp-73 hydrogen bonds, while the antagonists will only form the Asp-73 hydrogen bond

at the active site of LasR. In the present study, the known LasR inhibitor, 3-oxo-C12-(2-

aminophenol) was predicted to form both Trp-60 and Asp-73 hydrogen bonds (Figure

8.8). Also, the top ranking test ligand, methyl 16-methylheptadecanoate was predicted to

form Trp-60 hydrogen bond (Figure 8.4). In another study by Kim et al. 2008, furanone

derivatives that showed QS inhibitory activity, were found to make hydrogen bonds with

Tyr-56, Leu-36 and Met-116. Tan et al. 2013 presented the results of the virtual screening

of a natural compounds database followed by experimental validation. The most potent

inhibitor compound, designated as G1, was found to make hydrogen bonding interactions

with the amino acid residues, Trp-60, Thr-75, and Tyr-93, in the ligand binding pocket of

LasR. Hence, a comprehensive analysis on the binding interactions of the different LasR

inhibitors is fundamental to understand the diverse mechanisms involved, presenting a

rationale for future in silico studies directed towards the discovery of new anti-QS

compounds.


Molecular docking of phytochemicals with Pseudomonas aeruginosa QS proteins 8.5 Conclusion

Various studies based on the virtual screening of compound libraries have been

successful in discovering novel anti-QS compounds, with the potential to further restrain

the QS regulated virulence and biofilm in P. aeruginosa (Müh et al. 2006; Kim et al.

2008; Yang et al. 2009; Ahumedo et al. 2010; Borlee et al. 2010; Tan et al. 2013). These

compounds may not necessarily be structurally similar to the natural ligand for an

effective binding with LasR. In the present study, the compounds such as the fatty acid

methyl esters (methyl 16-methylheptadecanoate and methyl hexadecanoate) which are

structurally similar to the acyl chain of AHLs, and other structurally dissimilar

compounds, such as 2-(5-ethenyl-5-methyloxolan-2-yl) propan-2-ol, 2,3,4-

triacetyloxybutyl acetate, were found to bind effectively with the ligand binding site of

LasR and LasI proteins. These compounds may serve as prospective anti-QS agents for

the nosocomial pathogen, P. aeruginosa, and eventually emerge as novel anti-pathogenic

drug principles. A biological screening of these compounds, in their pure form, will be

the major objective for the next project, designed as an extension of the present study.


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