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CHAPTER-5

MOLECULAR DOCKING STUDIES

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CHAPTER -5

MOLECULAR DOCKING STUDIES

5. Molecular docking studies

This chapter discusses about the molecular docking studies of the

synthesized compounds with different enzyme target which we have employed.

Docking studies were performed on commercial software like GOLD from

CCDC, GLIDE from Schrodinger and free-wares like AutoDock Vina from

Scripps Research Institute. Structures of different protein crystal structures were

retrieved from the Protein Data Bank.

5.1 Background

Molecular docking is a well established computational technique

which predicts the interaction energy between two molecules. This technique

mainly incorporates algorithms like molecular dynamics, Monte Carlo

stimulation, fragment based search methods which are mentioned in details in

later part (Lengauer and Rarey. 1996).

Molecular docking studies are used to determine the interaction of two

molecules and to find the best orientation of ligand which would form a complex

with overall minimum energy. The small molecule, known as ligand usually fits

within protein’s cavity which is predicted by the search algorithm. These protein

cavities become active when they come in contact with any external compounds

and are thus called as active sites.

Docking is frequently used to predict the binding orientation of small

molecule drug candidates to their protein targets in order to predict the affinity

and activity of the small molecule. Hence docking plays an important role in the

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rational drug design. Given the biological and pharmaceutical significance of

molecular docking, considerable efforts have been directed towards improving

the methods used to predict docking.

The results are analyzed by a statistical scoring function which

converts interacting energy into numerical values called as the docking score;

and also the interacting energy is calculated. The 3D pose of the bound ligand

can be visualized using different visualizing tools like Pymol, Rasmol etc which

could help in inference of the best fit of ligand. Predicting the mode of protein-

ligand interaction can assume the active site of the protein molecule and further

help in protein annotation. Moreover molecular docking has major application in

drug designing and discovery.

5.2. Different types of Interactions

Interactions between particles can be defined as a consequence of

forces between the molecules contained by the particles. These forces are

divided into four categories.

5.2.1. Electrostatic forces - Forces with electrostatic origin due to the charges

residing in the matter. The most common interactions are charge-charge, charge-

dipole and dipole-dipole.

5.2.2. Electrodynamics forces-The most widely known is the Van der Waals

interactions.

5.2.3 Steric forces – Steric forces are generated when atoms in different

molecules come into very close contact with one another and start affecting the

reactivity of each other. The resulting forces can affect chemical reactions and

the free energy of a system.

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5.2.4. Solvent-related forces - These are forces generated due to chemical

reactions between the solvent and the protein or ligand. Examples are Hydrogen

bonds (hydrophilic interactions) and hydrophobic interactions.

5.2.5. Other physical factors - Conformational changes in the protein and the

ligand are often necessary for successful docking.

Molecular docking can be divided into two separate sections.

1) Search algorithm – These algorithms determine all possible optimal

conformations for a given complex (protein-protein, protein-ligand) in a

environment i.e. the position and orientation of both molecules relative to each

other. They can also calculate the energy of the resulting complex and of each

individual interaction.

The different types of algorithms that can be used for docking analysis are given

below.

• Molecular dynamics

• Monte Carlo methods

• Genetic algorithms

• Fragment-based methods

• Point complementary methods

• Distance geometry methods

• Systematic searches

2) Scoring function – These are mathematical methods used to predict the

strength of the non-covalent interaction called as binding affinity, between two

molecules after they have been docked. Scoring functions have also been

developed to predict the strength of other types of intermolecular interactions,

for example between two proteins or between protein and DNA or protein and

drug. These configurations are evaluated using scoring functions to distinguish

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the experimental binding modes from all other modes explored through the

searching algorithm (Kitchen et al. 2004).

For example:

• Empirical scoring function of any docking program

Fitness = vdW + Hbond + Elec

• Binding Energy

∆G bind = ∆Gvdw + ∆Ghbond + ∆Gelect + ∆Gconform + ∆G tor + ∆G sol

5.3. Types of Docking -The following are type of docking used often.

5.3.1. Lock and Key or Rigid Docking – In rigid docking, both the internal

geometry of the receptor and ligand is kept fixed during docking.

5.3.2. Induced fit or Flexible Docking - In this model, both the ligand and side

chain of the protein is kept flexible and the energy for different conformations of

the ligand fitting into the protein is calculated. For induced fit docking, the main

chain is also moved to incorporate the conformational changes of the protein

upon ligand binding. Though it is time consuming and omputationally

expensive, yet this method can evaluate many different possible conformations

which make it more exhaustive and possibly simulate real life phenomenon and

hence trustworthy.

5.3.3. Applications

A binding interaction between a small molecule ligand and

an enzyme protein may result in activation or inhibition of the enzyme. If the

protein is a receptor, ligand binding may result in agonism or antagonism.

Docking is most commonly used in the field of drug design — most drugs are

small organic molecules, and docking may be applied to:

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Hit identification – docking combined with a scoring function can be used to

quickly screen large databases of potential drugs in silico to identify

molecules that are likely to bind to protein target of interest.

Lead optimization – docking can be used to predict the relative orientation of

a ligand that binds to a protein (also referred to as the binding mode or pose).

This information may in turn be used to design more potent and selective

analogs.

Bioremediation – Protein ligand docking can also be used to predict

pollutants that can be degraded by enzymes (Suresh et al. 2008).

5.4. Brief Introduction of the docking software used in the study

5.4.1. Cambridge Crystallographic Data Centre (CCDC) GOLD

GOLD (Genetic Optimization for Ligand Docking) (Jones.G et al.

1995) is a genetic algorithm for docking flexible ligands into protein binding

sites. GOLD is supplied as part of the GOLD Suite, which includes two

additional software components, Hermes and Goldmine.

The Hermes visualiser can be used to assist the preparation of input

files for docking with GOLD, visualization of docking results and calculation of

descriptors The hermes visualizer is also used for interactive docking setup, e.g.

for defining the binding site and the setting of constraints.

Gold provides all the functionality required for docking ligands into

protein binding sites from prepared input files and although Hermes can be used

to assist the preparation of input files e.g. the addition of hydrogen atoms,

including those necessary for defining the correct ionization and tautomeric

states of protein residues. GOLD is likely be used in conjunction with a

modeling program to create and edit starting models. Gold offers a choice of

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scoring functions, GoldScore, ChemScore, ASP and user defined Score which

allows users to modify an existing function or implement their own scoring

function, with respect to using the GoldScore, ChemScore or ASP scoring

functions one may give a successful prediction where the other fails, but their

overall success are about the same.

5.4.2. Schrodinger Glide

Glide docks flexible ligands into a rigid/flexible receptor structure by

rapid sampling of the conformational, orientational, and positional degrees of

freedom of the ligand. There are three modes of running Glide which differ in

how ligand degrees of freedom are sampled and in the scoring function

employed. All three modes generate an exhaustive set of conformers for a ligand

and employ a series of hierarchical filters to enable rapid evaluation of ligand

degrees of freedom. The SP GlideScore scoring function is used to rank

compounds docked by SP or HTVS Glide. XP Glide begins with SP Glide

docking and then refines the predicted docking modes using an anchor-and-grow

algorithm to more thoroughly sample ligand degrees of freedom. The XP

GlideScore scoring function includes special recognition terms to identify and

reward structural motifs important to binding.

5.4.3. AutoDock Vina

AutoDock Vina is a comparatively new open-source program for drug

discovery, molecular docking and virtual screening, offering multi-core

capability, high performance, enhanced accuracy and ease of use. AutoDock

Vina has been designed and implemented by Dr. Oleg Trott (2010) in the

Molecular Graphics Lab at The Scripps Research Institute. AutoDock Vina

automatically calculates the grid maps and clusters the results in a way

transparent to the user.

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5.4.3.1. Features

Accuracy

AutoDock Vina significantly improves the average accuracy of the

binding mode predictions compared to AutoDock4. Additionally, AutoDock

Vina has been tested against a virtual screening benchmark called the Directory

of Useful Decoys by the Watowich group, and was found to be "a strong

competitor against the other programs and at the top of the pack in many cases".

It should be noted that all six of the other docking programs, to which it was

compared, are distributed commercially.

For its input and output, Vina uses the PDBQT molecular structure file

format used by AutoDock. PDBQT files can be generated (interactively or in

batch mode) and viewed using MGL Tools. Other files, such as the AutoDock

and AutoGrid parameter files (GPF, DPF) and grid map files are not needed.

5.5. Molecular docking studies with DNA topoisomerase-I of leishmania

donovani

All molecules under study were docked in to the binding site of the

receptor using Glide (Grid-Based Ligand Docking With Energetics) software

from Schrodinger. The three dimensional structure of the complex Ld-

Topoisomerase-1-DNA was retrieved from the Protein Data Bank (PDB code:

2B9S) (Davies et al. 2006) .

5.5.1. Methodology

Maestro is used as graphical user interface. Protein is prepared using

protein preparation wizard and following functions are performed:

a) Automatically imported full PDB files — or any chain within a PDB file

— from local databases or the PDB website.

b) Automatically missing hydrogen atoms are added.

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c) Metal ionization states are corrected to ensure proper formal charge and

force field treatment

d) Bond orders are enumerated to HET groups

e) Co-crystallized water molecules are removed at the user's discretion.

f) Residues with missing atoms or multiple occupancies are highlighted.

g) Quickly and easily determine the most likely ligand protonation state as

well as the energy penalties associated with alternate protonation states

h) Optimal protonation states for histidine residues are determined.

i) Potentially transposed heavy atoms in arginine, glutamine, and histidine

side chains are corrected.

j) The protein's hydrogen bond network is optimized by means of a

systematic, cluster-based approach, which greatly decreases preparation

times.

k) A restrained minimization is performed that allows hydrogen atoms to be

freely minimized, while allowing for sufficient heavy-atom movement to

relax strained bonds, angles, and clashes.

The ligands are prepared using Ligprep module with the following functions:

Chemically correct models: LigPrep generates accurate, energy

minimized 3D molecular structures. LigPrep also applies sophisticated rules to

correct Lewis structures and to eliminate mistakes in ligands in order to reduce

downstream computational errors.

Maximum diversity: LigPrep optionally expands tautomeric and

ionization states, ring conformations, and stereoisomers to produce broad

chemical and structural diversity from a single input structure.

The prepared protein is loaded into maestro environment and the active

site is defined. Grid centre is defined for the active site and box sizes are set. The

next step is to generate glide grid. After successful generation of the grids,

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prepared ligands are loaded into maestro. Ligands are kept flexible, while the

protein is rigid and docking started with extra precision mode (XP mode). The

docking calculation generated few poses for each ligand. The selection of the

best pose was done on the interaction energy between the ligand and the protein

as well as on the interactions the ligand shows with experimentally proved

important residues.

The docking results for all the inhibitory compounds under study are

reported in Table.5.1. The compounds bind in the pocket defined by Asn 178,

Met 254, Lys 250, cytosine 111 and adenine 11 from the DNA. All the

compounds are observed to exhibit hydrogen bonds with the DNA molecule

(Figure.5.1.). The best compound forms hydrogen bond with amino group of

both adenine 11 and cytosine 111 (Figure 5.2.). Only one of the naphthoquinone

monomer moieties participate in the hydrogen bond formation while the other

one does not. The N-dimethylamino compound, apart from forming the

hydrogen bonds binds electrostatically with the DNA molecule. The molecular

electrostatic potential map of the binding site in the receptor has been generated

to prove this. The partial positive charge of the protonated nitrogen in the ligand

is completely surrounded by the predominantly negatively charged surface in the

binding site (Figure.5.3.)

Ligand

IC50

simultaneous Glide score Kcal/mole

1 51 -6.26 2 37.5 -9.00 3 74 -4.28 4 70 -8.69

5 No inhibition N.D. Lawsone Dimer No inhibition N.D.

Table.5.1. IC50 and Glide score of the compounds

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Figure.5.1. Binding mode of 1, 2, 3 and 4 (clockwise)

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Figure.5.2. H-bond interactions of 2. Hydrogen bonds are shown by yellow dotted lines given in picture

Figure.5.3. MESP map of 2 in binding site. Positively charged surface is expressed by blue mesh and negatively charged surface by red mesh

5.6. Molecular docking studies with Cycloxygenase-2 (COX-2) enzyme

Docking studies have been performed with COX-2 enzyme using

AutoDock Vina. We have used PDB: ID 3NTG for docking studies. We used the

best compound for docking studies.

5.6.1. Docking methodology

Both the protein and the ligand are prepared for docking with

AutoDock tools software and loaded on PyRx platform (which is used as

graphical user interface) for docking. These processes are quite automated. The

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docking calculation generated ten poses. The selection of the best pose was done

on the interaction energy between the ligand and the protein as well as on the

interactions the ligand shows with experimentally proved important residues.

Blue doted lines are showing hydrogen bond interaction with COX 2

enzyme. Apart from this, the cationic side chain of Arg 106 forms π-cation

(guanidinium moiety of Arg 106) interaction with thiazole ring of the ligand.

Moreover, Ser 339 also forms a π-π interaction with one of the naphthoquinone

rings. These interactions are marked with yellow lines.

Figure.5.4. Interaction of best compound with COX-2. Here yellow and blue

doted lines showing hydrogen bond interaction with enzyme.

Yellow solid lines show pie-pie and pie-cation interactions.

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5.7. Molecular docking studies of diospyrin’s derivatives with human DNA

topoisomerase-I

From the previous published study we came to know that human DNA

topoisomerase-I is the established target for diospyrin and its derivatives

(Tazi et al. 2005).

5.7.1. Docking methodology

GOLD (Jones.G et al. 1995) was used for docking studies. At first all

the water molecules, metals and ligands are removed from the PDB: 1SC7

(Staker. et al.2005). Protein structure and was loaded in the Hermes module of

GOLD.Subsequently hydrogen atoms were also added. The histidine protonation

states are also determined and fixed in the protein structure. Binding site is

determined using the previous knowledge of the original ligand interaction site.

Goldscore was taken as the scoring function to rank the compounds to be

investigated. The ligands are kept flexible while the protein was taken as rigid.

Rest of the parameters was kept default as given in the software.

5.7.2. Molecular Interaction with DNA Topoisomerase-I

Human topoisomerase-I is having four major domains. 1)

The NH2 terminal domain is comprised between Met-1 and lys-197, and seems

dispensable for in-vitro activity. Residues Glu-198 to Ile-651 form the highly

conserved “core domain” followed by a short un-conserved linker (asp 652-glu

696).This linker has been found to be highly positive charged and may bind

directly to DNA. C-terminal domain, comprised between Gln-697 and Phe-795,

is highly conserved and contain the active site Tyr- 723 (Staker.et al.2005). The

catalytic residues of human DNA topoisomerase-I is Asn-722, Lys-532, Asp-

533, Arg-364, Asn-352 and Tyr-723.

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It has been proposed that diospyrin and its derivatives form a direct

interaction with enzyme and interferes with camptothecin-dependent

topoisomerase-I mediated DNA cleavage and thus inhibit the kinase activity of

topoisomerase-I (Tazi et al. 2005). For our study we have used only

topoisomerase-I enzyme after removal of DNA from PDB complex (PDB:

1SC7) (Staker.et al.2005).We have used Gold software 5.1 (Jones. et al. 1995) to

dock all the compounds into the active site of DNA-Topo-1(PDB: 1SC7)

(Staker.et al.2005).Gold is a well known genetic algorithm program for docking

flexible ligands into protein binding sites (Lauria. A. and Ippolito, M. 2007).

The binding site was defined to include all residues within 10 Å of the

ligand in original complex of human DNA-Topo-1. Preparation of protein for

docking included extraction of DNA using Discovery Studio (Accelary,San

diego,CA) and removal of ligand, water molecules and addition of hydrogens

were performed with GOLD. Preparation of ligands for docking included energy

minimization using MMFFs in Vlife program (Thomas A. Halgren. 1999).

Addition of hydrogens and the protonation of charged group were set by GOLD

(Jones. et al. 1995) as default. The default calculation mode which provides the

best docked results was selected for calculations. Chemscore was used as the

scoring function. Results were saved in mol.2 file. The final choice of the

models was based on interactions with key residues and correlation with the

biological activity. Pymol (The PyMOL Molecular Graphics System), V 1.5.0.4

Schrödinger. LLC) was used for the purpose of visualization. Diospyrin is a

binaphthoquinone so for our convenience we have divided diospyrin into 4 rings,

first and second naphthoquinone moiety known as 1/2 and 3/4 rings respectively.

The highly active compounds (D1, D14, D2, D7) were showing hydrogen bond

interactions with essential residues like Arg-364, Arg-488, Tyr-723, Asp-533,

and Asn-722 and additionally Van-der Waals interactions with Asp-533, Asn-

480, Asn-722, Glu-356, Tyr-426, Asn-352, Asn-430 were also observed. D14 and

diospyrin were the most active compounds where in D14 (acetyl amino

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derivative) quinone (C=O) of ring 3/4 was showing hydrogen bond interactions

with Arg 488 and Tyr-723 (Figure-5.5A). The -NH group of -NHCOCH3 in

position 3 of ring 4 was also forming hydrogen bond interaction with Asp-533,

Van- der Waals interaction also found in ring 3/4 with Asp-533. No hydrogen

bond interaction in ring 1/2 was observed but Van-der Waals interactions were

found between quinone and Asn-722. In the case of diospyrin, hydroxyl group

of ring 1/2 was showing hydrogen bond interaction with Asn-722 and the of

quinone and hydroxyl group of ring 1/2 as well as quinone of ring 3/4 were

showing Van-der Waals interactions with Asn-430 and Asn-722.

We have synthesized some new amino acid ester derivatives of

diospyrin which was showing moderate IC50 on human colon-205 in comparison

to diospyrin and its amino derivatives. In amino acid ester derivatives Valine and

leucine amino acid ester were found most active compounds. The Valine

(Figure-5.5B) derivative was showing hydrogen bond interactions with Arg-364,

Ser-423 and Glu-418 as well as Van-der Waals interactions are also noticed with

Glu- 418, Asp-533, Tyr-426, and Ala-351.

It was observed from the binding modes of amino acid ester

derivatives that these compounds occupy approximately the same space in the

ligand binding pocket as the more potent compounds (D1, D14, D2 and D7) but

their naphthoquinone moiety (ring 3/4) do not lie towards active residues and

they lack the favorable interaction with active site residues (Arg-364, Tyr -723).

It may be due to substitution of bulky groups on position 3 of ring 4.

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Fig-A

Fig.B

Figure.5.5 [A] The binding interaction of the most active compound [D14]

[B] [3a] (Valine methyl diospyrin’s dimethyl ether) right side

against human DNA Topoisomerase-I of 1SC7

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Residues are shown in green color. Docking poses were visualized with

PYMOL molecular graphics software. Interaction of amino acid residues with

compound D14 and 3a with highest score stimulation are shown. Hydrogen bonding

is shown through blue dotted lines.D14 and 3a are shown yellow in color.

5.8. Molecular docking studies of best compound 2 with the H5N1

neuraminidase active site

5.8.1. Docking methodology

Docking studies were performed using GOLD 5.1 (Jones.et al. 1995)

software. The crystal structures of H5N1 neuraminidase (PDB ID: 2HTY

(Russell et al. 2006) and (PDB ID: 2HU4 (Russell et al. 2006) where loop-150

were in open and closed conformation, respectively, were used in the study. At

first all the water molecules, metals and ligands are removed from both the PDB

protein structures and was loaded in the Hermes module of GOLD. Subsequently

hydrogen atoms were also added. The histidine protonation states are also

determined and fixed in the protein structure. Binding site is determined using

the previous knowledge of the original ligand interaction site. Goldscore was

taken as the scoring function to rank the compounds to be investigated.

In docking stimulations each ligand was kept flexible but the amino

acid residues of the proteins were held rigid. Preparation of protein and ligands

(removal of water molecule, extraction of original ligands from the protein

active site, addition of hydrogen and protonation state of charge group) were

done with GOLD as per default settings. For the simulation runs default

parameter values were taken. The selection of atoms in the active site within 6 Å

of original ligand was chosen as default. The minimum genetic algorithm run of

10,000 selected as default. The number of generated poses was set to 10 and top

ranked solutions were kept, with the early termination option turned on. The

Chemscore was selected for scoring function. The results were saved in mol.2

file.

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5.8.2. Loop 150 dynamics and its implications in contemporary anti-

neuraminidase research

As already reported in literature, The N1 and N2 neuraminidases of

viruses currently circulating in humans belong to two phylogenetically distinct

groups. Group-1 contains N1, N4, N5 and N8 subtypes, group-2, on the other

hand, contains N2, N3, N6, N7 and N9.In (2006) Russell et al. reported the

crystal structures of N1, N4 and N8 group-1 neuraminidases and when

comparison of active sites with N9 neuraminidase (group-2) were done,

specifically, on the ‘150-loop’ (residues 147–152) the following differences in

conformation were observed: 1] The C position of from Val 149 of group-1 is

about 7 Å distant from the equivalent isoleucine residue in group-2 and

hydrophobic side chain at position 149 is pointed away from the active site in

group-1 but towards it in group-2 (Landon et al. 2008).

There is a difference of 1.5Å in the side-chain position of the

conserved aspartic acid residue at position 151 between group-1 and group-2

neuraminidases (Cheng et al.2008). In group-2 structures Glu 119 forms a

hydrogen bond with Arg 156 but in group-1 it adopts a conformation such that

its carboxylate points in approximately the opposite direction. Due to this

difference in conformational aspects, a cavity observed to be forming, more

known as ‘150-cavity’ adjacent to the active site in group-1 but not in group-2

neuraminidases (Figure-5.6). Evidences were also found where the loop-150

changes its conformation upon inhibitor binding, a striking feature, which can

form basis of a different school of thought in future in context of drug design

against neuraminidases.

This differential inhibitor-binding concept was later supported by

Zhang and co-workers who utilized molecular docking, molecular dynamics

simulations, and MM/PBSA free energy calculation to confirm this. Inspired

from this discovery, Amaro et al. (2007) and conducted molecular dynamics

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simulations with explicit solvent system , taking apo form as well as oseltamivir

bound into the active site, proposing that, the loop 150 has the capability to open

wider than that was shown in crystal structures. This motion of loop 150 is

simultaneously with loop 430 (comprising of residues Arg 430-Thr 439), which

makes the active site even wider; however, the loop movements tend to form a

closed conformation when oseltamivir is bound. Continuing to work further, the

same group, Cheng et al.(2008) proposed the presence of novel hot spots within

flexible binding regions (150 and 430 loop) of the N1 neuraminidase extensive

MD simulations, conformational clustering, and CS-Map and if utilized, novel

inhibitors can be discovered with enhanced oral bioavailability and less

susceptible to structural mutations.

In the same year, Amaro et al. (2007), identified 27 drug like

compounds, the best three being NSC 109836, NSC 211332 and NSC 45583.

The work utilized ensembles from MD simulations on crystal structures and the

proposed location of hot spots from the previous work (Russell et al 2006;

Landon et al. 2008) and the flexible regions from loop 150 and loop 430. Jo and

co-workers, similarly, utilized the 150-loop region of the H5N1 subtype to

design novel oseltamivir derivatives with proper shape and atomic charge to fit

inside the 150 cavity.

Attachment of chemical groups at the C3 position of oseltamivir

successfully improved the binding affinity with neuraminidase subtype N1.

Wang and Zhang in 2010 also proposed that ligand with a small basic group,

such as amino (as in oseltamivir), favor the closed conformation of H5N1 NA

otherwise, for those inhibitors possessing a large, positively charged group, such

as guandinium, binding to the open conformation of H5N1 NA is favored.

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Figure 5.6. 150-cavity adjacent to the active site in group-1 but not in

group-2 neuraminidases

Until then, all group 1 neuraminidases have been reported as having an

open conformation and all group 2 neuraminidases have been reported as having

a closed conformation of the loop 150. Perhaps, one of the most surprising

discoveries of this year was the finding that group specific 150 cavity is absent

in H9N1 crystal structure (Li et al.2010). This finding implies that

neuraminidase inhibitors targeted to the 150-cavity will probably be less

effective against 09H1N1 variants. Recently, the single most unsolved structure

of group-1 neuraminidase, ie, N5 was also solved (Wang et al. 2011). The results

demonstrate that N5 possesses the common characteristics of the reported typical

group 1 NAs, including the presence of loop 150, which is in open conformation

but the loop closes when the protein is bound with zanamivir.

However, upon closer comparison of the uncomplexed N5 active site

with those of all other known structure group 1 NAs, it was observed that the N5

150-cavity is extended relative to those of all other group 1 structures

(Figure-5.7.). Although crystallography studies proved that 09N1 does not have

a 150 cavity, but contrary to this experimental evidence, long-term molecular

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dynamics simulation conducted by Rommie E Amaro et al.(2011) revealed that

the that the pandemic 09N1 NA is able to adopt open 150-cavity conformations

and even it appears to favour an open 150-cavity conformation overall.

Figure 5.7. Showed extention of 150-cavity in group 1 neuraminidases

5.8.3 Interaction of compound 2 to the active site

Molecular docking studies demonstrated possible mechanism on the

interaction of compound 2 in open and closed conformations. In the open

conformation, the binding pocket of the H5N1 avian influenza NA (PDB code

2HTY) was defined to include Arg 371, Tyr 347, Arg 292, Asn 224, Glu 276,

Ser 246, Glu 277, Arg 224, Ile 222, Arg 152, Ser 179, Trp 178, Asp 151, Glu

119, Arg 118, Arg 156 and Tyr 406. Compound 2 makes hydrogen bonds with

Arg 118, Arg 371, Tyr 406, Glu 277, Asp 151 and Arg 152. There are also

hydrophobic interactions between this compound and Tyr 347(Figure.5.8A).

In the closed conformation the binding pocket of the H5N1 avian

influenza NA [PDB code 2HU4] was also defined to include same set of amino

acids (Figure.5.8B). We observed a marked change in the binding mode of

compound 2 upon docking in the closed conformation of loop 150 (Amaro et al.

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2011). The ligand is shown to be protruding with the non-naphthoquinone aryl

group moving far from Tyr 347, thus losing the hydrophobic interaction with

this residue. The heavy atom RMSD of the docked poses of this particular ligand

between loop 150 open and closed conformations were observed to be 1.78Å.

Although the hydrogen bond interactions observed in open

conformation binding mode are retained but the ligand has moved closer to

residues Asp 151 and Arg 152. This is probably due to the hinge effect as the

loop 150 closes, and we observed one of the non-naphthoquinone aromatic

moiety interacts hydrophobically with the alkyl portion of the side chain of Arg

224, which is a commonly observed in oseltamivir carboxylate’s binding [PDB:

2HU4, (Figure.5.9.)] and as well as binding of DANA [PDB: 1NNB, and

described with details in Stoll et al. in 2003. The hydrophobic interaction

observed is not as elaborate to cover Trp 178, Ile 222 and Arg 224 as described

in Stoll et al. (2003) but it is restricted only to Arg 224.Moreover, there are

chances that the aryl moiety forms a hydrogen bond interaction with Ser 246

during actual molecular movement (Figure.5.8B).

In contrast, lawsone does not bind with any of the residues in the

arginine triad but it has hydrogen bond interaction with Glu 277 and most

probably making π-cation interaction with Arg 224. Thus, it is not surprising that

this compound showed low neuraminidase inhibitory property.

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Figure 5.8 The interaction of the most active compound [compound-2]

against H5N1 neuraminidase 2HTY [5.8 A]–loop 150 open. [5.8 B] 2HU4–

loop 150 closed

A

B

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Docking pose was generated with Pymol molecular graphics software.

Hydrophobic residues are shown in red color where as rest of the residues in

green. Loop 150 is shown in blue color. Interaction of amino acid residues with

compound 2 with highest score stimulation are shown. Hydrogen bonding is

shown through yellow dotted lines.

Figure 5.9. Description of hydrophobic interaction of oseltamivir with Arg

224. http://www.rcsb.org/pdb/explore.do?structureId=2hu4