ORIGINAL RESEARCH

Strategy in structure-based drug design for influenza A virustargeting M2 channel proteins

Nhut Tran • Linh Tran • Ly Le

Received: 3 January 2013 / Accepted: 24 April 2013

� Springer Science+Business Media New York 2013

Abstract The 2009 influenza A virus pandemic, with

high level of drug resistance reported, has highlighted the

urgent need of more effective anti-influenza drugs. M2

channel proteins on the influenza A virus membrane have

emerged as an efficient structure-based drug design target

since variety of M2 channel protein structures were con-

structed by different experiment methods to generate the

high resolution of crystal, solution NMR and solid-state

NMR structure. In an effort to facilitate the future design of

M2 channel inhibitors, the binding modes of 200 Ada-

mantane-based drugs in four different types of M2 channel

protein structures were evaluated by the critical interac-

tions in terms of ligand binding affinity. The molecular

docking results and statistic testing of binding affinity

showed that the effect of each representative type from M2

channel protein structures was significantly different.

Moreover, pharmacophore analysis revealed that there are

two mechanisms of binding interactions to critical residues,

Ser31 in holo structures and Ala30 in apo structures,

respectively. Molecular docking studies, drug-like filters,

and structure-based pharmacophore approaches success-

fully led us identifying the final hits reduced the false

positives and false negatives in strategy of designing new

potential group of future M2 channel inhibitors.

Keywords M2 channel protein structures �Drug binding site � Molecular docking � Lipinski’s rule �Pharmacophore analysis

Introduction

M2 channel proteins are responsible for the proton trans-

port across the viral membrane of influenza A virus which

have caused several world-wide pandemics (Morens et al.,

2010). This protein provides a reliable structural basis for

rational drug design against influenza A virus. Two FDA

approved drugs targeting the M2 channel proteins, Aman-

tadine and Rimantadine, unfortunately, are highly resistant

to current strains (Hayden and Hay, 1992; Monto, 2008;

Pielak et al., 2009). By the end of the 2009–2010, all the

tested 2009 pandemic H1N1 viruses were resistant to these

two Adamantane-based drugs.

Several M2 channel protein structures have been solved

so far by different methods (Table 1) and deposited to the

protein data bank (PDB). Very recently, using high-reso-

lution nuclear magnetic resonance (NMR) spectroscopy,

Schnell and Chou (2008) for the first time successfully

determined the solution M2 structure (residues 18–60) with

the presence of Rimantadine drug. At the same time,

Stouffer et al. (2008) reported another M2 structure (resi-

dues 22–46) with the G34A mutation, crystallized at pH

5.3 with Amantadine drug. The M2 channel protein

structures have also been extensively studied by solid-state

NMR, while Cady et al. (2010) employed experimental

side chain dihedral restraints and REDOR distance

Nhut Tran and Linh Tran contributed equally to this study.

N. Tran � L. Tran � L. Le

Life Science Laboratory, Institute for Computational Science

and Technology at Ho Chi Minh City, Quarter 6, Linh Trung

Ward, Thu Duc District, Ho Chi Minh City, Vietnam

L. Le (&)

School of Biotechnology, Ho Chi Minh International University,

Quarter 6, Linh Trung Ward, Thu Duc District,

Ho Chi Minh City, Vietnam

e-mail: ly.le@hcmiu.edu.vn

123

Med Chem Res

DOI 10.1007/s00044-013-0599-z

MEDICINALCHEMISTRYRESEARCH

measurements to obtain a well-defined structure of the M2

channel protein–Amantadine complex (residues 22–46),

Cross et al. (Sharma et al., 2010) used PISEMA solid-state

NMR spectroscopy to construct a reliable M2 channel

protein structure (residues 22–62). Several considerations

about the structural differences in M2 channel proteins

solved by different methods and the overall interactions

between those M2 channel structures with ligands remain

unstudied at the molecular level (Pielak and Chou, 2011;

Wang et al., 2011; Cross et al., 2012).

In addition, identifying the favorable drug binding site is

extremely essential for new drug design strategy against

the resistant strains of influenza A viruses (Le and Leluk,

2011). The M2 crystal structure proposed that the drug

directly blocks the proton transport by physically

obstructing the M2 channel protein (Jing et al., 2008). In

contrast, in the M2 solution structure, Rimantadine drug

binds to the external area, between two adjacent trans-

membrane helices, to stabilize the closed state (Schnell and

Chou, 2008). Previously, both experimental and computa-

tional studies of the influenza A virus M2 proton-selective

ion channel and its inhibition by Amantadine or Rimanta-

dine suggested that drug binding outside the M2 channel

protein is not the primary site associated with the phar-

macological inhibition (Khurana et al., 2011). More

recently, a new solid-state NMR study showed that the M2

channel protein in its closed conformation has both high

affinity in Amantadine binding inside position and four

sites with lower affinity on the C-terminal protein surface

(Cady et al., 2010). Previous study shows that when the

ratio of stoichiometric Amantadine: peptide reaches at 1:4,

the drug binds only inside the M2 channel protein whereas

the binding outside starts in excess condition (Kozakov

et al., 2010). When the drugs reach high concentrations in

lipid bilayer (40 lM Rimantadine), the surface binding site

likely becomes the second lipid binding site rather than the

true inhibition site (Cady et al., 2009).

While several evidences suggested that drug binding site

located inside the M2 channel protein, the exact functional

binding position still remains unidentified despite signifi-

cant research has been carried out over the last two decades

(Pielak and Chou, 2010). In this study, the computational

methods were employed to study the specific binding sites

of Adamantane-based drugs including Amantadine and

Rimantadine inside four different M2 channel protein

structures. In addition, the binding affinity of M2 proteins

solved by different methods: X-ray crystallography, solu-

tion NMR and solid-state NMR with 200 new compounds

from our previous study (Tran et al., 2011) was evaluated

and compared. Three inhibitors showing highest potent

inhibitory profile in binding to all four M2 channel proteins

were selected and generated pharmacophore models at the

active sites. The results from this study provided promising

data for further studies on drug inhibition, drug resistance

and to avoid false negative or false positive in M2 channel

inhibitors design.

Results and discussion

Binding modes of amantadine and rimantadine

inside M2 channel proteins

Molecular docking results of Amantadine and Rimantadine

with four different M2 channel protein structures (2KQT,

2L0J, 2RLF and 3C9J) generally showed that the binding

affinity of Rimantadine was lower than Amantadine in all

cases (Table 2). This clearly indicated that Rimantadine

binds to M2 channel proteins energetically more favorable

than Amantadine. This finding was strongly supported by

the experimental evidence in which the IC50 value of

0.98 ± 0.10 lg ml-1 for Rimantadine–M2 channel protein

was lower than that of 13.8 ± 1.7 lg ml-1 for the

Amantadine–M2 channel protein complex (Kelly et al.,

1999).

At M2 channel protein binding site (PDB code: 2KQT),

Kerr and Sansom (1993) found the hydrophobic adamantly

cage of the drug interacts with Val27 side chain while the

amino group forms favorable electrostatic interactions with

the Ser31 hydroxyls (Fig. 1). However, while Kerr and

Sansom (1993) found the drug amino group forms favor-

able electrostatic interactions with the Ser31 hydroxyls, our

results showed that the amino group of Rimantadine

formed a Hydrogen bond with the Ser31 which agreed well

with Yi et al. (2008) study from computer simulation, that

is the amino group from the drug created one or more

Table 1 Experimental techniques summary of four different M2 channel protein structures

PDB code Attached ligand binding Source Experiment method Protein structure

2RLF Rimantadine H3N2 influenza A virus Solution NMR Micelles

3C9J Amantadine Synthetic X-ray diffraction Lipid bilayer

2KQT Rimantadine Synthetic Solid-state NMR DMPC lipid bilayer

2L0J Unbound drug H3N2 influenza A virus Solid-state NMR Hydrated lipid bilayer

Med Chem Res

123

alternating hydrogen bonds between hydroxyls of Ser31

and backbone carbonyl of Ala30.

The location of two Adamantane-based drugs inside the

M2 channel protein structure (PDB code: 2L0J) was pre-

sented in Fig. 2. The hydrophobic adamantyl cage of both

Amantadine and Rimantadine coordinated with Ser31 side

chain while Amantadine amino group formed hydrogen

bond interaction with Ala30 and Rimantadine amino group

formed favorable electrostatic interaction with Val27

inside M2 channel protein.

Within M2 channel protein binding site (PDB code:

2RLF), the hydrophobic cage of drugs moved to the widest

region of the M2 channel protein and coordinated with

Gly34 residue, which is consistent with Gandhi et al.

(1999) finding. However, the amino group of Amantadine

pointed toward the N-terminal instead of interacting with

imidazoles of His 37 (Fig. 3). A reasonable explanation for

this discrepancy was proposed that although M2 channel

protein (PDB code: 2RLF) was solved at high pH to obtain

the closed state of the M2 protein, the process to form

PDBQT input from PDB had changed through the addition

of a partial charge to the original PDB, this leads to a

significant change in the net charge of M2 channel protein.

The His37 residue have 0.0002 Gasteiger charge for each

residue and is fully protonated with ?0.0008 Gasteiger

charge while Amantadine and Rimantadine have approxi-

mately ?1.0 Gasteiger charge. Those positive charges

repulse each other, which results in more consume energy

to form hydrogen bond. Therefore, the lower binding

energy conformation was obtained with the amino acid

group point toward N-terminal. In vivo, the pH value of our

body is always slightly fluctuated leading to charging

His37 residue in real-time.

With the M2 channel protein structure (PDB code:

3C9J), Amantadine created only one hydrogen bond with

Ser10 and no hydrophobic interaction was found. In

Rimantadine binding site, two hydrogen bonds were

formed with Ser10 and possibly a hydrogen bond with Ala6

and hydrophobic interaction with Val6 was observed

(Fig. 4). This finding was consistent with previous sug-

gestions (Kelly et al., 1999) and highly contributed in

explanation that why the S31A mutant has caused drug

Table 2 Docking results of four different M2 channel protein structures with Amantadine and Rimantadine at defined active site

M2 channel

protein

Binding affinity Binding site Hydrophobic interaction H-bond forming residue No. of H-bond

A

H2N

R

CH3 NH2

A R A R A R

2RLF -6.2 -6.7 Leu26, Val27, Ala30,

Ser31, Gly34, His37

His37, Gly34 Gly34

2L0J -6.1 -6.6 Leu26, Val27, Ala30,

Ser31, Gly34, His37

Ala30, Gly34 Ser31, Val27 Ser31 1

2KQT -5.4 -6.2 Leu26, Val27, Ala30,

Ser31, Gly34, His37

Val27 Val27, Ala30 Ser31 Ser31 1 1

3C9J -6.2 -6.7 Leu5, Val6, Ala9,

Ser10, Ala13, His16

Val6 Ser10 Ser10 1 2

A Amantadine, R Rimantadine

Fig. 1 Location of Amantadine and Rimantadine inside M2 channel

protein (PDB code: 2KQT), respectively. a Amino group of

Amantadine forms electrostatic interactions with hydroxyl group of

Val27 and Ser31 inside M2 channel protein. b Rimantadine created a

hydrophobic interaction with Val27 and formed hydrogen bond

interaction with Ser31 inside M2 channel protein

Med Chem Res

123

resistance to Amantadine but remains sensitive to Riman-

tadine (Pielak et al., 2009). Despite of the mutations,

Rimantadine still created a hydrophobic interaction with

Val6 and formed a hydrogen bond with another Ala6 res-

idue inside M2 channel protein while Amantadine could

not form hydrogen bond with alternative Ala6 and no

interaction with Val26 results in lost of their activity.

There are three out of four cases, Amantadine and

Rimantadine located near N-terminal to form several

interactions mainly with Val27, Ala30, and Ser31, this

finding indicated that these regions were more favorable

for drug binding. The only one exceptional case for 2RLF

structure in which Amantadine and Rimantadine moved to

Gly34 region, considering as a second pocket which was

separated from the first one by Gly34 residue and ended by

His37, the drugs will travel to this site only if the binding

interactions in the first pocket is not strong enough to keep

the drug stand stability in the pocket.

Fig. 2 Location of Amantadine and Rimantadine inside M2 channel

protein (PDB code: 2L0J), respectively. a Amantadine cage formed

hydrophobic interaction with Ser31 while their amino group created a

hydrogen bond interaction with Ala30 residue inside M2 channel

protein. b Rimantadine cage formed hydrophobic interaction with

Ser31 while an electrostatic interaction was found between amino

group of Amantadine and Val27 residue inside M2 channel protein

Fig. 3 Location of Amantadine and Rimantadine inside M2 channel

protein (PDB code: 2RLF), respectively. a Amantadine cage formed

hydrophobic interactions with Gly34 and His37 inside M2 channel

protein. b Rimantadine cage formed a hydrophobic interaction with

Gly34. The hydrophobic residues and positive charge of His37

prevented the generation of hydrogen bond and electrostatic interac-

tion with the amino group

Fig. 4 Location of Amantadine and Rimantadine inside M2 channel

protein (PDB code: 3C9J), respectively. a Amino group of Aman-

tadine formed hydrogen bond interaction with Ser 10. b Rimantadine

cage create a hydrophobic interaction with Val6 while their amino

drug form two hydrogen bonds interaction to two adjacent Ser10

Med Chem Res

123

The differences in four M2 channel protein structures

Even though there were structural differences between M2

channel proteins, we still could not conclude that they will

have different interactions to ligands. The results from

molecular docking were further analyzed using statistical

software SPSS (Alferes and Kenny, 2009) to evaluate the

effect of different M2 channel protein structures on ligand

binding affinity. From the docking results, we hypothesized

that there is a significant difference in binding affinity

between four different structures of M2 channel proteins.

H0 : l2KQT ¼ l2L0J ¼ l2RLF ¼ l3C9J

The results from SPSS agreed well with our proposed

hypothesis (Table 3). The M2 channel protein structures

solved by different methods cause a big effect on the ligand

binding affinity. In terms of binding affinity, the 2RLF was

found to be the best structure with the lowest mean of binding

affinity (-8.1870 kcal mol-1) and was significantly different

from all others, 2L0J was different from 2KQT and 3C0J

was not different from 2L0J and 2KQT. The average

binding affinity of both 3C9J and 2KQT are not different

(Sig = 0.129).

Ranking top ten compounds binding to M2 channel

proteins and Lipinski’s rule of five

The most convenient and effective way to deliver drugs to

human circulation system is through oral route, so the high

oral bioavailability is one of the most important consider-

ations for the development of bioactive molecules as

therapeutic agents. As a pioneer, Lipinski has so far

developed the primary criteria to analysis the structures of

administered drugs and drug candidates correlating physi-

cal properties with successful drug development (Lipinski,

2000; Lipinski et al., 2012). This analysis has been very

useful and has led to a set of rules relating to the impor-

tance of lipophilicity (octanol–water partition), molecular

weight (MW), and the number of hydrogen bond donors

and acceptors.

The docking results were ranked based on their binding

affinity with four different structures of M2 channel pro-

teins. Ten compounds in common represent for top binding

affinity in all four structures were selected. The top ten

docking ligands properties was analyzed for lipophilicity,

molecular weight, number of hydrogen bond donors and

acceptors and rotatable bonds to decide their drug-like or

non drug-like compounds. In order to avoid false negative

and false positive results, three final selected ligands, Nos.

14, 16, and 23, which presented in all M2 channel protein

structures and satisfied all physiochemical properties were

considered as the most potential hits among 200 com-

pounds (Table 4).

Pharmacophore analysis of top binding ligands–M2

channel protein interactions

Top three compounds, Nos. 14, 16, and 23, showed sig-

nificantly lower binding affinity than Amantadine, this

would suggest that these three compounds have higher

potential in binding to M2 channel proteins. Interestingly,

all top three compounds (Nos. 14, 16, and 23) share slightly

similar molecular structure design. This result could imply

similar key residues from M2 channel proteins that bind to

each ligand.

Comparing the above top binding compounds, the

training set compounds were selected from top binding

ligands, which include three compounds Nos. 14, 16, and

23. The most common pharmacophore feature was chosen

from top three binding compounds using Common Feature

Pharmacophore Generation/Discovery Studio. As a result,

the generated pharmacophore contains three hydrophobic

(H) group and two hydrogen bond acceptor (HBA). All

three training set compounds were mapped with the com-

mon pharmacophore feature as illustrated in Fig. 5 and

further analyzed using structure-based pharmacophore to

study specific interactions between these top hit inhibitors

and amino acids in the active sites from four M2 channel

protein structures (Fig. 6).

The docking conformations were investigated to further

study the interactions between each ligand–M2 channel

protein pair at the key residues in the binding site. In this

study, four different M2 channel protein structures (PDB

codes: 2KQT, 2L0J, 2RLF, and 3C9J) bound with three top

binding compounds Nos. 14, 16, and 23 were selected as

input for structure-based pharmacophore generation.

Totally twelve generated pharmacophore models with its

geometrical constraints at the active sites were presented in

Fig. 6.

For all three compounds in M2 channel protein struc-

ture (PDB code: 2KQT), the docking conformations

showed in close proximity to Ser31 residue in each

Table 3 Statistic testing for binding affinity difference between M2

channel protein structures

Structure N Subset for a = 0.01

1 2 3

2RLF 200 -8.1870

2L0J 200 -7.3600

3C9J 200 -7.2695 -7.2695

2KQT 200 -7.1065

Sig. 1.000 0.620 0.129

N degree of freedom

Med Chem Res

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Table 4 Pharmacological properties calculation for selection of drug-like compounds

No. MW No.

rotatable

bond

C log P H-bond

donor

H-bond

acceptor

H-bond

total

Polar

surface

area

Volume

N19

N

N

344.587 4 6.68 2 0 2 6.476 370.241

60

N

CH3

301.518 2 7.33 1 0 1 3.238 323.664

N3

N

301.518 3 7.69 1 0 1 3.238 323.879

53

NCH3

315.545 2 7.89 1 0 1 3.238 340.466

23

NH2

NH2

CH3

262.441 1 3.08 2 4 6 52.046 276.418

16

NH2

CH3 CH3

NH2

276.468 1 3.60 2 4 6 52.046 293.005

15

NH2

CH3 CH3

261.453 1 5.58 1 2 3 26.023 282.04

Med Chem Res

123

protein. Furthermore, the generated pharmacophore con-

tains one Hydrogen Bond Acceptor (HBA) toward Ser31

and at least two hydrophobic (H) chemical features

pointed toward Ala30. Similarly, the generated pharma-

cophore features of all three compounds in M2 channel

protein structure (PDB code: 3C9J) contains at least two

HBA points toward Ser10 and H chemical group toward

Ala13. For all three compounds in M2 channel protein

structure (PDB code: 2L0J), the generated pharmacophore

consists of one HBA or one H chemical feature which

points toward Ala30. Similarly, one to two HBA or more

than one H chemical group which points toward Ala30

was generated in pharmacophore features from M2

channel protein (PDB code: 2RLF).

Despite the different binding mode of each ligand

within four M2 channel protein structures, they created

similar chemical features with Ala30 in all cases. Inter-

estingly, while the generated pharmacophore features

from two M2 channel proteins (PDB codes are 2KQT and

3C9J) shared the same critical interaction residue,

hydrogen bond acceptor toward Ser31 residue, the one

from other two M2 channel proteins (PDB codes are 2L0J

and 2RLF) involved the same interactions with Ala30

residue. In order to explain for the difference, we eval-

uated the position of those residues and the results

showed that Ser31 side chain tends to turn into M2

channel protein lining while Ala30 point toward mem-

brane in 3C9J and 2KQT structures, this variability made

the ligand difficult to reach to Ser31 and facilitate the

interaction with Ala30. In contrast, Ser31 side chain

tends to turn out the pore lining while Ala30 point toward

the pore lining in 2L0J and 2RLF structures. Notably,

2KQT and 3C9J were solved in holo form while 2L0J and

2RLF were solved in apo form, hence we suggested that

Ser31 is a key residue when the receptor was in drug-

bound conformation and Ala30 is a critical residue during

the receptor in free form, those binding residues play an

important role in forming strong interactions with

ligands, leading to inhibit the proton transport activity of

M2 channel proteins.

Table 4 continued

No. MW No.

rotatable

bond

C log P H-bond

donor

H-bond

acceptor

H-bond

total

Polar

surface

area

Volume

14

NH2

CH3

NH2

276.418 1 3.08 2 4 6 52.046 276.418

M24

N

N

316.533 4 5.56 2 0 2 6.476 336.987

N7

N OH

O

307.478 5 5.43 3 2 5 43.694 317.503

Rotatable bonds were defined as any single bond, not in a ring, bound to a nonterminal heavy (i.e., non-hydrogen) atom. Excluded from the count

were amide C–N bonds because of their high rotational energy barrier

Octanol–water partition coefficients, C log P, were calculated using the BioByte ClogP 4.0 estimator, as implemented in the Daylight Chemical

Information System software, v. 4.71.20

Hydrogen bond donors were taken as any heteroatom with at least one bonded hydrogen

Hydrogen bond acceptors were taken as any heteroatom without a formal positive charge, excluding halogens, pyrrole nitrogen, heteroaromatic

oxygen and sulfur, and higher oxidation states of nitrogen, phosphorus, and sulfur but including the oxygens bonded to them

The polar surface area was calculated based on the summation of tabulated surface contributions of polar fragments (atoms regarding also their

environment) (Ertl et al., 2000). These fragment contributions were determined by least squares fitting to the single conformer 3D PSA for

34,810 drugs from the World Drug Index

Med Chem Res

123

Fig. 5 Common

pharmacophore feature and its

overlaid with training set

compounds. a Chemical

features of common

pharmacophore created from

top three binding compounds.

b–d Compound Nos. 14, 16, and

23, respectively, the training set

compounds, overlaid on

common pharmacophore

feature. Color coding pink HD

(hydrogen bond donor); cyanHY (hydrophobic) (for

interpretation of the references

to color in this figure legend, the

reader is referred to the web

version of this article.) (Color

figure online)

Fig. 6 Structure-based pharmacophore analysis. Hydrogen bond acceptor (HBA) was shown as green vectors; hydrophobic (H) was illustrated as yellowspheres (for interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) (Color figure online)

Med Chem Res

123

Experimental section

Ligands and proteins preparation

Adamantine-based compounds from our prior study

(Tran et al., 2011) were taken for molecular docking

experiments. The 3D structures of 200 compounds were

built using Gaussview 5.0 software and then optimized

geometrically by MOPAC 2009 using the PM6 Hamilto-

nian Restricted Hartree–Fock method.

The 3D structures of M2 channel protein including

solution NMR method (PDB code: 2RLF) for full length

structure, X-ray crystallography method (PDB code: 3C9J)

and solid-state NMR method (PDB codes: 2KQT and 2L0J)

were taken from the Protein Data Bank (Berman et al.,

2000). These four M2 channel protein structures were

selected based on the different methods and whether it was

in complex structure, with and without ligand bound. These

M2 channel protein structures were prepared for molecular

docking and structure-based virtual screening process by

removing all water molecules. Attached ligands (Amanta-

dine and Rimantadine) were removed and saved as two

separate pdb files for control docking and specific binding

site analysis because Amantadine was solved in X-ray

(3C9J) and SSNMR (2KQT) structure while Rimantadine

was solved in solution NMR (2RLF) structure. Therefore, it

is important to determine that there was no major difference

in the inhibitory action of the two drugs (Fig. 7).

Molecular docking experiment

Molecular docking was performed using AutoDock Vina

due to its accuracy, high performance and high speed in

comparison with previous versions. AutoDock Vina

achieves an approximately two orders of magnitude speed-

up compared to the AutoDock 4 and significantly improves

the accuracy of the binding mode predictions with a com-

paratively low standard error of binding affinity (Trott and

Olson, 2010). 200 compounds were docked into each four

M2 channel protein structures, respectively (PDB codes:

2KQT, 2L0J, 2RLF and 3C9J). The M2 channel protein

structures were kept rigid and MGL Tools 1.5.6 rc2 was

utilized to prepare the input pdbqt file that contains a protein

structure with Kollman charges and hydrogens in all polar

residues, it is then used by the docking program in AutoDock

Vina to obtain the binding affinity value. The MGL 1.5.6 rc2

was also utilized to merge non-polar hydrogen, add charges

and set up rotatable bonds for each ligand. The docking site

on protein targets was defined by establishing a binding box

with the appropriate dimension 30 9 30 9 30 A using

1.000 A spacing. Because M2 channel protein structures

were solved by different methods containing different

coordinations, so the grid box cavity size and center were set

separately for each site of the receptor. The binding box was

positioned to cover all possible binding sites including

binding residues and location of resistance mutation residues

consists of Leu26, Val27, Ala30, Ser31, Gly34, and His37.

For molecular docking method, Monte Carlo algorithm was

used to do docking experiments on Autodock Vina with 100

of exhaustiveness. As a result, total 10 docking conforma-

tions with the lowest binding energies were recorded.

Finally, 800 docking results were then carried out using

statistic test by SPSS v16 (Alferes and Kenny, 2009) to check

whether it is significant different from the others. The top 10

docking results with lowest binding energy and in common

for all structures were selected for further analysis. Physio-

chemical properties of these top 10 compounds were calcu-

lated to reject non drug-like compounds, the remaining top

three compounds were then analyzed for pharmacophore

generation.

Fig. 7 Comparison of M2

channel protein structures. The

Ala30 and Ser31 residues were

presented as red and greencolor, respectively. Amantadine

and Rimantadine were hidden

for clearer observation. a Solid-

state NMR M2 structure of

Amantadine-bound (22–46)

(PDB code: 2KQT). b Solid-

state NMR M2 structure of

drug-unbound (22–62) (PDB

code: 2L0J). c Solution NMR

M2 structure of Rimantadine-

bound (18–60) (PDB code:

2RLF). d Crystal M2 structure

of Amantadine-bound (22–46)

(PDB code: 3C9J) (Color figure

online)

Med Chem Res

123

Pharmacophore analysis

The identification of most common pharmacophore fea-

tures from the top binding compounds with four M2

channel proteins would be helpful to search new potential

hits. In this work, three top binding compounds Nos. 14,

16, and 23 were selected for training set molecules to

generate the most common pharmacophore features using

HipHop module of Discovery Studio 2.5 software (Accel-

rys Inc., 2009). The best common pharmacophore feature

which has the highest fit value and highest number of

compounds fitting was chosen.

Structure-based pharmacophore generation is one of the

productive tools to discover the important interactions

between top binding compounds and receptor. The inter-

actions between receptor and ligand complexes in the

active sites of M2 channel proteins are important to

understand the behavior of ligand inhibition. Ligandscout

(Wolber and Langer, 2005) creates pharmacophores from

structure-based complex data, and allows sophisticated

pharmacophore analysis and fine-tuning to create selective

pharmacophoric screening filters for a specific target. We

used Ligandscout as a tool for automatic construction and

visualization of pharmacophore model derived from the 3D

coordinates of the proteins and the excluded volume

spheres were added to the structure-based model to identify

the inaccessible areas for potential ligands. The top three

common selected compounds 14, 16 and 23 from total 800

molecular docking results were chosen as input training set

for structure-based pharmacophore generation. The gener-

ated twelve pharmacophore models with their geometrical

constrain and active sites were illustrated in Fig. 6.

Conclusions

The study has successfully clarified the binding modes and

molecular interactions of Amantadine and Rimantadine in

four different structures of M2 channel protein. Depend on

different structure; the drugs will bind at approximately

different locations from Val27 to Gly34 inside the M2

channel proteins. This study also contributed to explain the

differences between the binding sites for each M2 channel

protein structures and provided reliable explanations for

drug resistance. In addition, virtual screening with Auto-

dock Vina was applied to 200 new compounds with four

M2 channel protein structures constructed by different

methods (for example drug-bound and drug-unbound

conditions) helped us to present a principle concept for

critical interactions between ligands and M2 channel

protein.

The results have shown that there are significant dif-

ferences between four M2 channel protein structures

(PDB codes: 2KQT, 2L0J, 2RLF and 3C9J) in terms of

binding affinity. In particular, 2RLF and 2L0J with C-ter-

minal extension bind to ligands better than 2KQT and 3C9J

with only transmembrane domain. In addition, M2 channel

protein with four Rimantadine drugs binding outside (PDB

code: 2RLF) gets advantage to keep their structure more

stable in binding to ligands better than M2 channel protein

without drug bound (PDB code: 2L0J). Hence, we deter-

mined that the more stable M2 channel protein structures

are, the less binding affinity they have with ligands binding

in drug design strategy.

The significant important contribution was found from

this study is an approach that has defined the critical

interaction residues from various M2 channel protein

structures. This finding is necessary to avoid false positive

and false negative in selection process for M2 channel drug

candidates. Binding modes, the molecular interactions and

pharmacophore features of the three top hits were analyzed

for the essential interactions with important residues at the

M2 channel protein active sites. Finally, three compounds

with a great potential to be utilized in future M2 channel

inhibitor designing are reported as a final outcome of this

study. However, due to molecular docking methods and

limitations, further study on ligand–M2 complexes using

molecular dynamic simulation which includes protein

flexibility is highly suggested.

Acknowledgments The work was funded by the Vietnam National

Foundation for Science and Technology Development (NAFOSTED)

under grant number 106.01-2012.66. Computing resources and sup-

port provided by the Institute for Computational Science and Tech-

nology—Ho Chi Minh City is gratefully acknowledged.

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