structure based pharmacophore, virtual screening...

Virtual Screening and Antibacterial Assay

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STRUCTURE BASED PHARMACOPHORE, VIRTUAL SCREENING AND ANTIBACTERIAL ACTIVITY OF NOVEL MOLECULES

Among all the virtual approaches, structure-based pharmacophore (SBP) and molecular

docking based Virtual Screening (VS) are probably the most efficient methods to identify

potentially potent compounds from chemical databases containing a large amount of

molecules (Valasani et al., 2013). Virtual Screening is a knowledge driven process that

uses computational chemistry techniques to analyze large chemical databases in order to

identify possible new leads (Valasani et al., 2013). Virtual Screening is used as an initial

screen for large databases to prune the number of compounds that are to be screened

experimentally (Lyne et al., 2002; Liu et al., 2013). This process of finding ‘needles in a

haystack’ produces leads that add immense value to the early drug discovery stages. VS

protocols include ligand based screens like 1D filters (e.g. molecular weight), 2D filters

(similarity, substructure fingerprints), 3D filters (3D pharmacophore, 3D shape matching)

and structure based screens like docking (Sirois et al., 2004; Valasani et al., 2013). A key

pre-requisite is the knowledge about the spatial and energetic criteria responsible for

protein–ligand binding (Klebe et al., 2006).

Application of computational methodologies in the drug discovery process is well

established. Technologies like combinatorial chemistry and high-throughput screening

(HTS) (Jhoti et al., 2013) are being used to synthesize and screen large number of

compounds in a short period of time to boost the productivity of the drug discovery

process. In virtual screening, computational models are used to predict the biological

activity of compounds. The computational models can be generated and validated utilizing

either the 3D structure of the target or a set of active analogues specific to the target.

Computational models can also be built combining information from structure of the drug

target and a set of active analogues specific to the target (Moro et al., 2007; Liu et al.,

2013). It is identified that there are no efforts carried to enrich the structure types and find

out novel potent inhibitors against Staphylococcus aureus PheRS using VS technologies.

Hence, it is worthy to develop an effective and accurate virtual screening strategy to design

novel druggable PheRS inhibitors.


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Clearly, there is a need for pharmaceutical agents that inhibit novel bacterial targets

outside the realm of established antimicrobial therapies (Woodford, 2005; Fatima et al.,

2013). The search for such targets is ongoing and has intensified both in scope and volume

in the past few years. Among the numerous enzyme families that have become the focus of

antibacterial research recently, the aminoacyl-tRNA synthetases (AaRS) are particularly

attractive (Schimmel et al., 1998; Tao et al., 2000; Hurdle et al., 2005; Yao et al., 2013).

Since ribosomal protein synthesis absolutely depends on the steady supply of charged

tRNA molecules, AaRS are essential for all living organisms. Inhibition of AaRS results in

arrest of ribosomal protein synthesis for bacteria this causes attenuation and eventual

cessation of growth in vitro and in vivo (Tao et al., 2000).

Herein, we report an effective VS model combining ensemble pharmacophore

models and cascade docking for the discovery of novel phenylalanine-tRNA synthetase

inhibitors (Figure 1). Ten SBP models are generated based on the developed homology

model. The common features among these models are reserved, makes the based model.

However, this model is too large and not sensitive to differentiate the active from inactive.

For simplification, we have used a Receptor−Ligand pharmacophore model (a model focus

on Receptor−Ligand complexes based on the LigandScout algorithm) to support selection

of critical features from the SBP models. The ensemble model is then applied for the VS of

a database containing over 680,000 compounds derived from Asinex, ZINC, National

Cancer Institute (NCI) and SPECS (www.specs.net) databases. The hit compounds are

then evaluated for their binding affinities and conformations with PheRS using a cascade

docking method. Thirty compounds are reserved and evaluated using comparative invitro

inhibitory concentration assay (MIC) against Staphylococcus aureus. Five compounds with

novel scaffolds exhibiting potent target-based and cell-based activities can be served as

promising lead compounds for further optimization.

5.1. MATERIALS AND METHODS

Biological data: For modeling studies, a data set of molecules having activities against

PheRS was selected from the literature (Xiang et al., 2004; Xiang et al., 2004; Richard et

al., 2005). Ten active compounds and ten inactive compounds were selected to validate the


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pharmacophore model. Homology model developed and best complex of MD simulation

was used to generate Structure based pharmacophore in the Discovery studio software.

Figure 1 shows the computational protocol that was applied to screen the database. Full

details of each step are discussed in the following paragraphs.

Figure 1: Flow diagram of the Virtual screening protocol combined with ligand based and

structure based methodologies.

5.1.1. Structure Based Pharmacophore Model Generation

5.1.1.1. Protein and Ligand Preparation

Homology model complex (Extracted from MD trajectory) structure was prepared

using the protein preparation workflow in Discovery Studio 2.5 (DS). Prepare protein

protocol performs adding missing atoms in residues and missing loop regions were

inserted based on SEQRES data. Water and alternate conformations were deleted. Short


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and medium size loop regions were optimized with the LOOPER algorithm (Spassov et al.,

2008) and the remaining loop regions were minimized in the CHARMm force field. All

structures’ pKa were calculated and protonated. (Spassov et al., 2008).

5.1.1.2. Receptor-Ligand Pharmacophore Generation

Receptor-Ligand Pharmacophore Generation protocol is available in DS 3.1

(www.accelrys.com) was used to investigate the essential amino acids that participate in

the binding of ligands with PheRS enzyme. This protocol acts by generating selective

pharmacophore models by employing Genetic Function Approximation (GFA) technique.

The pharmacophore hypotheses were constructed using the LigandScout algorithm

(Wolber et al., 2004). The LigandScout algorithm allowed the automatic construction of

the pharmacophore model from the structural data of the protein−ligand complex. The

resulting pharmacophore model is based on receptor-ligand interactions and adds certain

features such as Hydrogen bond acceptor (HBA), Hydrogen bond donor (HBD),

Hydrophobic (HY), Negative ionizable (NI), Positive ionizable (PI) and Ring aromatic

(RA). The essential amino acids will be identified in which the ligand interacts with and

these amino acids will be considered later when the final pharmacophore model is being

built using Interaction Generation protocol. This protocol extracts pharmacophore query

from the Ludi interaction map which is created inside the active site sphere and only

assigns three main features namely HBA, HBD and HY. All the parameters within this

protocol were left as their default values (Fei et al., 2013).

5.1.1.3. Pharmacophore Validation

How to modify a pharmacophore model based on protein structures is always a

perplexing issue. Researchers used a test cluster of active and inactive inhibitors to validate

and modify the pharmacophore model. So while generating the pharmacophore to validate

the pharmacophore we have given 10 actives and 10 inactive molecules as positive and

negative controls. This will aid us to do the different validations to the developed SBP

model like ROC curve, selectivity and sensitivity tables. Herein we use a pharmacophore

model based on Receptor-Ligand which focuses more attention on ligands clusters. The

pharmacophore model built on the Receptor-Ligand complex reflects more features of the

interaction between PheRS and ligands than which built on ligands. The pharmacophore


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model built on receptor structures can factually represent the energy contributed by critical

points in the process of interacting between protein and ligands. However, these

hypotheses were usually used to design inhibitors.

5.1.1.4. Enrichment Factor Validation

For validating the reliability of the constructed pharmacophore models, the

enrichment factor (EF) (Halgren et al., 2004; Anighoro et al., 2013) was calculated using a

decoy database of 1000 molecules. The EF was calculated using the following formula.

Where n = total number of hits, a = the total number of active molecules in the n hits, N =

total number of molecules in database and A = the total number of actives in the database.

The decoy database was built by mixing the 10 active ligands (IC50 <1 μ M) with

990 compounds (selected randomly from ZINC database). Total 5% actives were

considered in the dataset. All the compounds were converted to 3D structures and multiple

conformers were generated using the Diverse Conformation Generation module in DS

running with the Best conformations option. The energy of each compound was minimized

using CHARMm (Chemistry at HARvard Macromolecular Mechanics) force field.

5.2. CHEMICAL LIBRARY DESIGN

In view of the structural diversity and availability at the time of study, among the

numerous commercial and academic compound databases, the Asinex, NCI Plated 2007

Database and SPECS Natural Database were selected for the screening purpose. The total

databases with 680,000 compounds were filtered by well-known Lipinski’s rule of five

(Lipinski et al., 2004) which states, in general; an orally active drug has no more than one

violation of the following criteria:

Not more than 5 hydrogen bond donors (nitrogen or oxygen atoms with one

or more hydrogen atoms).

Not more than 10 hydrogen bond acceptors (nitrogen or oxygen atoms).


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A molecular mass less than 500 Daltons.

An octanol-water partition coefficient log P not greater than 5.

Molecules which are not fitting in the Lipinski rule were eliminated from the hits

list. Then the database was calculated to predict Absorption, Distribution, Metabolism,

Excretion and Toxicity (ADMET) properties by Pipeline Pilot (PP) 8.0. The molecules

which had unacceptable ADMET properties were removed. Those compounds that passed

all of the screening experiments were retained for further study. The filtered database was

built of multi conformers by using the ‘Build 3D Database’ module in DS (best method,

maximum number of conformers = 255).

5.3. CASCADE DOCKING

Native-Docking: The output Molecular dynamics protein ligand complex is used to

conduct Native-Docking. Complex ligand was docked back into protein structure using DS

2.5 CDOCKER (Wu et al., 2003) and Schrodinger Glide (Glide Version 3.5, Schrodinger,

L.L.C., New York. 2006). The docking results were evaluated through comparison of the

best docked ligands binding modes with the input pose. The root-mean-square deviation

(RMSD) was used to compare differences between the atomic distances of the docked

poses and the real input ligand pose to measure docking reliability. The docking software

with the smallest RMSD was selected to perform Docking.

Docking with Decoy: A decoy set was used to validate the docking efficiency of the

programs. Docking with decoy set (http://dud.docking.org) benefits to determine the

percentage of the ranked compounds that we should select in the docking. The parameters

of the docking function were determined by Native-Docking (Thilagavathi et al., 2010;

Tubert-Brohman et al., 2013). Enrichment factor (EF) in the top 5%, 10% and 25% was

calculated.


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5.4. VIRTUAL SCREENING

5.4.1. Pharmacophore Screening The chemical database with about 860,827 molecules was screened employing the

ensemble pharmacophore model using the Search 3D Database module with the fast

flexible search method in DS. The index ‘Fit value’ was calculated to rank the screened

molecules. The molecules with the Fit value greater than 3 were retained. Conformers

belonged to the same molecule were also ranked and the best conformer with the highest

Fit value remained.

5.4.2. Docking Screening All the molecules which passed the pharmacophore screening were aligned in

Schrödinger 2009 and processed the cascade docking with the parameter discussed above.

Then compounds were evaluated by consistency scoring functions in Glide and Gold 5.0

using several algorithms: Ludi, Goldscore, Chemscore, ASP, CHEMPLP, LigScore1,

LigScore2, Jain and Ludi Energy Estimate 1 (Jain, 2006). The consistent score was

calculated and ranked. 2% of molecules with a ranked consistent score were retained and

clustered to 10 sets by their similarity using Tanimoto in DS. Finally, one or two

compounds with the highest consistent score were picked out from each set subject to the

bioassay.

5.5. BIOLOGICAL ASSAY

The potency (activity) of an antibiotic product is expressed as the ratio of the dose

that inhibits the growth of a suitable susceptible microorganism to the dose of an

International Biological Standard, an International Biological Reference Preparation, or an

International Chemical Reference Substance of that antibiotic that produces similar

inhibition. Properly validated secondary reference materials may also be utilized in the

assay. To carry out the assay a comparison is made between the inhibition of the growth of

microorganisms produced by known concentrations of the reference material and that

produced by measured dilutions of the test substance. This response is measured by the

disk diffusion method (Negi et al., 2012; Krishnamurthy et al., 2013; Oksuz et al., 2013;

Nagarajan et al., 2013).


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5.5.1. Assay Procedure

Petri dishes are filled to a depth of 3-4 mm, with a culture medium that has

previously been inoculated with a suitable inoculum of a susceptible test organism. The

nutrient agar (Antibiotic assay medium) composed based on standard concentrations of the

ingredients (Nagarajan et al., 2013). The concentration of the inoculum was selected that

the sharpest zones of inhibition and suitable dose response at different concentrations of

the standard are obtained. When using the inoculum, an inoculated medium containing 1

ml of inoculum per 100 ml of the culture medium is selected. When the inoculum consists

of vegetative organisms, the temperature of the molten agar medium was not exceeding to

48-50 °C. The dishes were specially selected with flat bottoms. During the filling they are

placed on a flat, horizontal surface so as to ensure that the layer of the medium will be of a

uniform thickness.

For the application of the test solution, previously sterilized borer 8-10 mm in

diameter was bored on the surface of the inoculated medium to make the holes. The holes

arrangement on the plate was made such that overlapping of zones is avoided.

Solutions of the reference material of known concentration and corresponding

dilutions of the test substance at the same concentration are prepared in a sterile buffer of a

suitable pH value. To assess the validity of the assay 3 different doses of the reference

material is used together with an equal number of doses of the test substance. The dose

levels were used in the geometric progression. Once the relationship between the logarithm

of concentration of the antibiotic and the diameter of the zone of inhibition has been shown

to be approximately rectilinear, routine assays were carried out using only 2 concentrations

of the reference material and 2 dilutions of the test substance.

The solutions of the reference material and the test substance were arranged on

each dish so that the solutions of the reference material and those of the test substance

alternate around the dish and are placed in such a manner that the highest concentrations of

the reference material and of the test substance are not adjacent. The solutions are placed in

the holes by means of a pipette that delivers a uniform volume of liquid. Delivered

volumes were sufficient to fill holes almost completely.


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The plates are incubated at a suitable temperature, the selected temperature (33°C)

being controlled at ±0.5 °C, for approximately 16 hours and the diameters or areas of the

zones of inhibition produced by the varied concentrations of the standard and of the test

substance are measured accurately, by using a suitable measuring device. From the results,

the potency of the tested substance was calculated. Conditions for the assay of individual

antibiotics and suitable test organisms were considered according to Indian pharmacopeia

and WHO guidelines.

Figure 2: Mother culture of S. aureus NCIM 5345.

5.5.2. Culture Media Preparation Culture medium was prepared according to Indian pharmacopeia standard

antibiotic assay method media preparation section (Table 1). The described raw materials,

reagents and test solutions were used. Mother cultures obtained from National Collection

of Industrial Microorganisms (NCIM), Pune (Figure 2) was used to prepare culture slants

(Figure 3).


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Table 1: Reference antibiotic assay conditions for Staphylococcus aureus as test organism.

Antibiotic Test organism

Culture medium

aPhosphate buffer, sterile,

pHa, TS

bConcentration (weight or

International Units per ml)

Incubation temperature (°C) final pH

Cefalexin Staphylococcus aureus cCm1; 6 10-40 μg 32-35 NCTC 6571 6.5-6.6 NCIM 5345 Staphylococcus aureus Cm1; 6 10-40 μg 32-35 ATCC 6538-P 6.5-6.6

a Phosphate buffers, sterile, of suitable pH. Buffers designated as TS, TS1, or TS2 may be used. b Range within which suitable concentrations may be found. cCm: Culture media.

Figure 3: Aseptically prepared cultures of Staphylococcus aureus.

5.5.3. Preparation of Inoculums

Mother cultures of Staphylococcus aureus are obtained from National Collection of

Industrial Microorganisms (NCIM), Pune, INDIA. The strain NCIM5345 is chosen as test

organism as it has close resembles with many other stains. The test organism is grown

overnight on culture medium Cm1 (pH 6.5-6.6 after sterilization) at a temperature of 35-37

°C. A suspension was prepared by washing off the growth with saline test solution and

diluting to a suitable opacity, such that a 1-cm layer transmits 50% of the incident light

when examined at 650 nm.


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5.5.4. Calculation of Results

Inhibition zone diameters measured to compare the inhibition of the tested

substance with the reference molecule. Standard methods were used to carry out the

statistical evaluation of the microbiological assay of antibiotics (Bliss et al., 1952).

5.6. RESULTS AND DISCUSSION

5.6.1. Proteins and Ligands Preparation

As initial input is highly validated and minimized homology model complex there

is no missing side chain atoms, alternate conformations and loops to build. Bonds and

bond orders have been checked and corrected. Only some side chains movement is

occurred due to CHARMm minimization process. Minimized protein complex is

considered for further Receptor based pharmacophore generation.

5.6.2. Structure Based Pharmacophore Model Generation

A Receptor-Ligand Pharmacophore Generation protocol in the DS2.1 software was

used to develop ten pharmacophore models ranking them according to their selectivity

score, the higher the better. According to the results, ten structure based pharmacophore

models (SBP) that scored selectivity scores from 2.75 to 1.11 were generated from features

that matched the receptor-ligand interactions HBD, HBD, HY, HY, PI (Table 2). These

interactions revealed the important amino acids that are helpful to choose the final

structure-based pharmacophore model.

The active site of PheRS enzyme has three major binding areas (Figure 4). The first

and the most important area is the Polar pocket 1; which is formed by Met314 and Glu216.

Second, Two hydrophobic pockets formed by Phe212, Arg318 and Phe254 respectively.

Hydrophobic pocket 2 is inserted deeply inside the active site, which can accommodate up

to two aromatic rings. Finally polar pocket 2 which is composed of Val261 and Ser252.

The hydrophobic pocket was filled by one or two HY features in all ten generated

pharmacophores. On the other hand, the key interactions of the active site represented by

HBD, PI features correlated to the polar amino acids that are present.


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Table 2: The generated pharmacophore models with their selectivity scores based on

PheRS enzyme bound to its ligand.

Figure 4: A 3D view representing the three main areas that forms the active site of PheRS

enzyme. Ligands are represented in tube and the active site residues are represented in

sticks. Mapped ligands in grey colour.


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The results have shown a similar trend to the Ligand based pharmacophore

generation protocol by indicating the presence of two hydrophobic pockets and the

Hydrogen bond donors and one Positive ionization feature points. Pharmacophore mapped

with the best active ligand showed in the figure 5.

5.6.3. Pharmacophore validation

While generating the pharmacophore a test cluster of active and inactive inhibitors

were added to validate and modify the pharmacophore model. Cluster of 10 actives and 10

inactive molecules as positive and negative controls were used. This helps in validating the

generated models by generating ROC curve, selectivity and sensitivity tables. The

pharmacophore model built on the Receptor −Ligand complex reflects features of the

interactions between PheRS and ligands. The best Structure based pharmacophore (SBP)

model poses good sensitivity towards actives and inactive. It could able separate actives

from inactive with less/no number of true negatives (Table 3). Sensitivity table along with

ROC curve for best two pharmacophore with accuracy rate 0.899 gives confidence on the

model to use for virtual screening (Figure 6).

Figure 5: Structure-based pharmacophore of the active site of PheRS. 3D representation of

this pharmacophore; HY: cyan; HBD: magenta; PI: Red. Mapped ligand in green colour.


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Table 3: Statistical parameters of best pharmacophore to differentiate the actives and

inactive.

Figure 6: ROC curves for best two pharmacophore shows good sensitivity score.

5.6.4. Enrichment factor Validation

Enrichment factor studies were carried out to know the ability of the SBP model to

retrieve the actives from the decoys. Total of 1000 molecules database was created as

described in the methodology. The database was submitted for ligand pharmacophore

mapping with the best SBP model. An enrichment validation result shows that SBP model

could able to fish out the actives in top 15% of the hits. Same set was submitted to


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enrichment studies using two famous docking protocols i.e., SP docking from Schrodinger

software and CDOCKER from Accelrys DS2.5. All the enrichment results were given in

the table 4. The results of enrichment study gives confidence to use SBP model for

screening virtual databases to identify the novel hits against PheRS target.

Table 4: Enrichment results for three protocols.

Pharmacophore model Protocol Total

Ligand set No.

Actives 5% 10% 25% Total retrieved

PheRS Homology

model-Ligand

Ligand pharmacophore mapping

1000 10 5 2 1 8

SP 1000 10 4 0 1 5

CDOCKER 1000 10 4 2 1 7

5.6.5. Chemical library design

Until recently, it had been standard practice to filter drug candidates with Lipinski’s ‘Rule-

of-5′ criteria to enhance the drugability of the hits from VS. Lipinski’ criterion was used to

pick the molecules for database preparation, limits are: 500 ≤ MW ≤ 700, ALogP ≥ 5,

N_Rings ≥3, N_HBA ≥ 5, N_HBD ≤ 5. These chemical properties used to filter virtual

databases and accelerate the process of hit identification by lowering both cost and time.

With the Rule-of-5 criteria 600,026 compounds had passed out from the chemical database

containing 680055 molecules database.

ADMET (absorption, distribution, metabolism, excretion and toxicity) profiles

played critical roles in the discovery/development of drugs. It was estimated that 40–60%

of new chemical entity failures was due to poor ADMET properties. Hence, during virtual

screening ADMET descriptors allowed researchers not only to eliminate compounds with

unfavorable characteristics early to avoid expensive reformulation later but also to evaluate

proposed structural refinements that were designed to improve ADMET properties, prior to

resource expenditure on synthesis. Herein we used the QSAR models to estimate a wide

range of ADMET related properties of small molecules in Asinex, NCI and SPECS

databases in DS2.5. Six descriptors (aqueous solubility, blood brain barrier (BBB)

penetration, cytochrome P450 2D6 inhibition, hepatotoxicity, human intestinal absorption

and plasma protein binding) were selected to calculate for estimation. Compounds with


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good absorption level (level 0 according to DS), optimal solubility (level 3 or 4), low BBB

penetrability (level 3), CYP450 2D6 non-inhibition and non-hepatotoxic properties were

selected as druggable compounds. Finally, 21,827 molecules were selected from 82026

molecules filtered with ‘Rule-of-5-for-PheRS’ criteria.

5.6.6. Docking of known inhibitors

According to the results of Native-Docking of modeled protein to its native ligand,

docking using the Glide program (Schrodinger, 2009) had the smallest average RMSD and

standard deviation. Although docking using CDOCKER had an approximate std (0.18), its

average RMSD was not acceptable. The reproducibility of the Glide program was higher

than CDOOKER programs. Therefore, we have used Glide program to dock protein with

inhibitors. Ideally it’s better to select the protein after conducting a cross docking study

with its available crystal structures, in this case we don’t have the luxury of multiple

crystal structures. For this reason, some known actives and inactives to PheRS target were

selected to perform Glide docking study. This study enables to understand the activity

difference between actives and inactives. Understanding the activity difference in protein

and interactions point of view is very crucial to pick best hits from the virtual screening.

5.6.7. Understanding the activity difference

A molecular dynamics study expedited to understand the binding mode of the most

active molecule in the Chapter IV. In the process of virtual screening it is important to

understand the inactive molecules and their features to reduce the false negatives in the

VS. To do so we have compared the docking pose of best active molecule 47 with two

least active molecules 3 and 9.

In most active compound 47 binding pose, the ligand NH of amine forms an ionic

and hydrogen bond with Glu216 and forms key interaction with Met314. Ligand Phenyl

moiety and extended alkyl chain occupies a relatively wide hydrophobic pocket (P1)

formed by Val261, Phe254, Thr257, Phe312, Gly217 and Gly290. In addition to the

essential interactions between PheRS antagonists and Met314 ligand has the cation –π

interactions with Arg318. Where as in compound 3 and 9 molecules has steric clash with

Arg318 and Phe212, also they are missing donor interactions with Val261 and Ala289.

Binding modes of the both molecule can be observed in Figure 7 and 8.


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Figure 7: Activity difference between most active molecule 47 Vs. Least active 3.

Color legends: Green: Most active ligand # 47; Grey: Least active #3.

Figure 8: Activity difference between most active molecule 47 Vs. Least active 9.

Color legends: Green: Most active ligand # 47; Grey: Least active #9.

Missing polar interaction

Non-favorable steric clash

Non-favorable steric clash

Missing polar interaction


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5.7. VIRTUAL SCREENING RESULTS

5.7.1. Pharmacophore Screening The database constructed using Build database module in Discovery studio with 82,000

molecules which were filtered out from the Lipinski rule filter. The constructed database

was used to screen against final validated structure-based pharmacophore (SBP) model.

At this step, it is important to find candidate structures that share similarity with

active molecules reported in the literature and fulfill the requirements of being able to fit in

the generated structure-based pharmacophore model which is a reflection of the active site

geometry. Consequently, both 2D and 3D criteria will be attained. The Screen Library

protocol was run for database consists of 82,686 compounds molecules which are filtered

out from the Lipinski rule filter. Database was generated by applying Best Conformation

generation of 255 conformers for each structure with 20 kcal energy threshold. This

screening step has returned 2,373 compounds able to match the structure-based

pharmacophore with a minimum of three features out of five being satisfied and every

compound should map the critical binding feature and the HBD and PI feature adjacent to

it. SBP model mapping of compound 4299493 is depicted in figure 9. Total five features

were fulfilled by the compound; the benzo[1,3]dioxole ring fits the HY, the NH and OH

groups nicely mapped with HBD and PI features, hydrophobic part of the piperdine group

maps with second HY features and finally benzyl group try to manage in the polar HBD

region. Compound has good fit value 3.89, while the one of the feature was not mapped

well still protein has the some hydrophobic region to accommodate the hydrophobic part of

the ligand.

For the final selection of candidate inhibitors of PheRS, the fit values of these

compounds will be compared with the consensus scoring of the molecular docking.


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Figure 9: Compound #4299493 mapped over the generated structure-based

pharmacophore. 2D-structure of the compound given as well.

Color legends: Grey: Most active ligand # 47; Yellow: Virtual screening hit #4299493.

5.7.2. Docking of Screening Hits

The hits obtained from Pharmacophore based screening were further validated with

docking program to understand the binding mode and to prioritize for bio assay. Total

2,373 has returned from the pharmacophore screening.

The 2,373 compounds were next docked by using two docking programs with three

scoring functions. Final model from homology efforts was used as the working protein and

Glide was used as the docking program and 2% of ranked molecules would remain.

Consensus scoring was performed by summing the values of the three scoring functions

and then comparing them with the fitting values performed in the previous step. The


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candidate molecules were selected based on the ability of the chemical structures to map

the generated structure-based pharmacophore, getting the highest ranks in the consensus

scoring tactic and visually examining the binding pattern of the docked poses.

Consequently, 30 compounds were chosen to be potential inhibitors which contain various

functional groups performing binding with critical amino acids Met214 and Glu216 at the

active site (Table 5). For example, 13658686 is a 1,3-diphenylthiourea based structure in

which the two NH donor groups forming critical interactions with Met314 and Glu216.

Two dimethyl substituted morpholine groups occupying the hydrophobic pockets on both

sides which formed by Phe212 and Phe312, Phe256 respectively. (Figure 10).

Figure 10: Top VS hit Compound #13658686 mapped over the generated structure-based

pharmacophore. 2D- structure of the compound given as well.

Color legends: Grey: Most active ligand # 47; Yellow: Virtual screening hit #13658686.


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Table 5: Structures, consensus scoring and predicted activities of candidate compounds as

PheRS inhibitors.


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Table 5 contd.

5.8. BIOLOGICAL ASSAY RESULTS

After screening with ensemble pharmacophore model and cascade docking, fifteen

novel compounds with various scaffolds were selected for biological tests. Based on the

availability of the molecules with the vendor, total thirty compounds were tested against

Staphylococcus aureus using minimum inhibitory concentration assay using ciprofloxacin

as reference molecule. Reference molecule and test molecules were tested at different

concentrations like 5, 10, 15, 20 µg/ml concentrations of the reference and test substance

were used to test in plate method.


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Interestingly 10 out of 30 molecules showed good inhibition against

Staphylococcus aureus. Five novel scaffolds showed better activity than existing drug

ciprofloxacin. Results were given in table 6.

5.8.1. Assay results for Compound 4299493

Figure 11: Top VS hit Compound #4299493 inhibition zones against

Staphylococcus aureus in petri dish MIC assay. Both standard compound and sample are

tested at concentrations 5, 10, 20 and 30 µg/ml.

Most scored virtual screen hit 4299493 was tested for inhibitory activity against

Staphylococcus aureus. MIC results show that tested compound has better clear zones of

Inhibition zone


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inhibition than the reference compound see figure 11. This gives confidence on the ligand

and structure based methods, the hints gained from the analysis.


One of the best scored virtual screen hit no. 13658686 was tested for inhibitory

activity against Staphylococcus aureus. MIC results show that tested compound has better

and clear zones of inhibition than the reference compound see figure 12. This gives

confidence on the ligand and structure based methods, the hints gained from the analysis.

Figure 12: Top VS hit Compound #13658686 inhibition zones against Staphylococcus

aureus in petri dish MIC assay. Both standard compound and sample are tested at

concentrations 5, 10, 20 and 30 µg/ml.


Virtual screen hit no. 20406024 was tested for inhibitory activity against Staphylococcus

aureus. MIC results show that tested compound has better and clear zones of inhibition

Inhibition zone


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than the reference compound see figure 13. This gives confidence on the ligand and

structure based methods, the hints gained from the analysis.

Figure 13: Top VS hit Compound #20406024 inhibition zones against Staphylococcus

aureus in petri dish MIC assay. Both standard compound and sample are tested at

concentrations 5, 10, 20 and 30 µg/ml.

Total thirty candidate compounds were purchased from Asinex and were later

tested for minimum inhibitory concentration at Chandra labs, an ISO certified company for

microbiology services, Hyderabad, India. Following the ligand based virtual screening and

finally validated by docking with the PheRS model, docking score, fit value and estimated

activity of top 30 molecules screened and suggested as inhibitors of PheRS. The selected

compounds with Asinex id is given in the table 6.

Inhibition zone


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Table 6: Screened Compounds for PheRS with inhibition activity and glide docking score.


199

Contd..

In our study, we successfully demonstrated an efficient virtual screening model

using ligand protein based combined pharmacophore based screening model to remove

false positives. Initially, pharmacophore model was developed and validated using known

PheRS inhibitors to retrieve the crucial features. The validated pharmacophore model was

used as a query to screen database of compounds and top 2726 hits were selected based

pharmacophore fit value. Subsequently, feature & docking studies developed using the

predicted bound conformation of the highest active compound and successfully validated

based on enrichment factor.

Virtual screening has been established as a powerful alternative complementary to

high throughput screening (Morya et al., 2012). When virtual screening is performed

optimally, impressive hit rates have been reported. Though few Virtual screening studies

were reported in the literature on different receptors of the Staphylococcus (Lee JY et al.,

2012; Ananthula et al., 2012; Sabatini et al., 2013), there are no report on virtual screening

tools to identify novel compounds specifically for PheRS receptors. Virtual screening with

a receptor model is hindered by at least three factors: the model reliability, the absence of a

good docking score and the low throughput. Many scoring functions for ligand-protein

docking have been developed in the past decade and several successful examples have


200

been reported (Jain, 2006; Tubert-Brohman et al., 2013) but they are still far from perfect

and have yet to be proven adequate for predicting binding affinities. Docking is relatively

slow and currently, virtual screening of a 33 lakhs compounds takes extensive

computational resources (Hongwu et al., 2008). To overcome these uncertainties scientists

using combined ligand and structure-based virtual screening as a tool for detecting novel

lead compounds.( Mladenovic et al., 2013; Zhou et al., 2013; Wang et al., 2014).

The present study established that combined ligand and structure-based virtual

screening is a useful tool for detecting novel lead compounds for the antibiotics such as the

Staphylococcus aureus PheRS receptors. The hit molecule serves as a lead structure for

novel PheRS antagonists that are intended for therapeutic use. We have reported the

development and validation of common feature-based pharmacophore models for PheRS

receptor ligands. In addition, the present study also demonstrated that refined PheRS

receptor homology model provides a relevant structural basis for rationalizing the PheRS

receptor binding.

The established insilico receptor-based screening approach and consensus scoring

functions (Jain, 2006). Previously developed, studied homology model of PheRS is

currently used in 3D database searches to identify new chemical scaffolds for PheRS

antagonist. This virtual screening workflow resulted in the success of 10 out of 30

compounds in biological testing against Staphylococcus aureus. Five novel and diverse

compounds tested in vitro biology assays for Staphylococcus aureus can be subjected to

medicinal chemistry optimization in order to obtain new leads for subsequent

pharmacological evaluation. This study demonstrated the power of virtual screening with a

pharmacophore model and using the modeling techniques in performance was essential.

CONCLUSIONS

Successful approaches should optimize each procedure in virtual screening to

distinguish actives from false positives. From chemical database to pharmacophore model,

docking screening and result validation, our work has made much effort to optimize the VS

strategy special for PheRS. Comparing to classic VS strategy, our VS strategy combined

an ensemble pharmacophore model with cascade docking. The chemical database with

appropriate chemical space was built for screening drug-like molecules. Cascade docking


201

was used to ensure that the protein, the docking program and evaluation were optimal for

this VS process. We constructed a pharmacophore model, which ensemble a Receptor–

Ligand complex-based pharmacophore model to simply the SBP model, with the proper

size and shape for screening nonpeptide inhibitors. With the effective VS strategy, five

novel scaffolds for inhibiting the PheRS were identified from a modest database of

commercially available compounds. In addition to the success with chemical diversity, the

hit rate was nearly 30%. This demonstrated the ability of our VS strategy to broadly and

effectively search and identify more diverse inhibitors.


202

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