Two Birds with One Stone? Possible Dual-Targeting H1N1
Inhibitors from Traditional Chinese Medicine
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Chang, Su-Sen, Hung-Jin Huang, and Calvin Yu-Chian Chen.
“Two Birds with One Stone? Possible Dual-Targeting H1N1
Inhibitors from Traditional Chinese Medicine.” Ed. Kuo-Chen
Chou. PLoS Computational Biology 7.12 (2011): e1002315.
Web. 8 Feb. 2012.
As Published
http://dx.doi.org/10.1371/journal.pcbi.1002315
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Thu May 26 23:41:36 EDT 2016
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http://hdl.handle.net/1721.1/69046
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Two Birds with One Stone? Possible Dual-Targeting
H1N1 Inhibitors from Traditional Chinese Medicine
Su-Sen Chang1, Hung-Jin Huang2, Calvin Yu-Chian Chen1,3,4,5,6*
1 Laboratory of Computational and Systems Biology, China Medical University, Taichung, Taiwan, 2 Sciences and Chinese Medicine Resources, China Medical University,
Taichung, Taiwan, 3 Department of Bioinformatics, Asia University, Taichung, Taiwan, 4 China Medical University Beigang Hospital, Yunlin, Taiwan, 5 Department of
Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America, 6 Computational and Systems Biology, Massachusetts Institute of Technology,
Cambridge, Massachusetts, United States of America
Abstract
The H1N1 influenza pandemic of 2009 has claimed over 18,000 lives. During this pandemic, development of drug resistance
further complicated efforts to control and treat the widespread illness. This research utilizes traditional Chinese medicine
Database@Taiwan (TCM Database@Taiwan) to screen for compounds that simultaneously target H1 and N1 to overcome
current difficulties with virus mutations. The top three candidates were de novo derivatives of xylopine and rosmaricine.
Bioactivity of the de novo derivatives against N1 were validated by multiple machine learning prediction models. Ability of the
de novo compounds to maintain CoMFA/CoMSIA contour and form key interactions implied bioactivity within H1 as well.
Addition of a pyridinium fragment was critical to form stable interactions in H1 and N1 as supported by molecular dynamics
(MD) simulation. Results from MD, hydrophobic interactions, and torsion angles are consistent and support the findings of
docking. Multiple anchors and lack of binding to residues prone to mutation suggest that the TCM de novo derivatives may be
resistant to drug resistance and are advantageous over conventional H1N1 treatments such as oseltamivir. These results
suggest that the TCM de novo derivatives may be suitable candidates of dual-targeting drugs for influenza.
Citation: Chang S-S, Huang H-J, Chen CY-C (2011) Two Birds with One Stone? Possible Dual-Targeting H1N1 Inhibitors from Traditional Chinese Medicine. PLoS
Comput Biol 7(12): e1002315. doi:10.1371/journal.pcbi.1002315
Editor: Kuo-Chen Chou, Gordon Life Science Institute, United States of America
Received July 25, 2011; Accepted November 3, 2011; Published December 22, 2011
Copyright: ß 2011 Chang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The research was supported by grants from the National Science Council of Taiwan (NSC 99-2221-E-039-013-), Committee on Chinese Medicine and
Pharmacy (CCMP100-RD-030), China Medical University and Asia University (CMU98-TCM, CMU99-TCM, CMU99-S-02, CMU99-ASIA-25, CMU99-ASIA-26 CMU99ASIA-27 CMU99-ASIA-28). This study is also supported in part by Taiwan Department of Health Clinical Trial and Research Center of Excellence (DOH100-TD-B-111004) and Taiwan Department of Health Cancer Research Center of Excellence (DOH100-TD-C-111-005). The funders had no role in study design, data collection
and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: ycc929@MIT.EDU
influenza A and B [15,16], allowing more in depth studies
regarding its biological function and action mechanism [17–19].
These proteins have been used as targets for rational attempts to
design drugs for influenza [20–27].
The H1N1/09 virus strain is a triple reassortant that contains
gene segments from avian, swine and human influenza viruses
[28]. In addition to antigenic shift that can lead to fundamental
changes in influenza surface antigens, antigenic drift could reduce
binding affinity of host antibodies to antigens [29,30]. A major
challenge in influenza vaccine development is the rapid evolution
of influenza viruses, causing vaccines to be easily outdated and
reformulation necessary each year [31–33]. Although the H1N1/
09 virus is susceptible to neuraminidase inhibitors, cases regarding
oseltamivir-resistant viruses with neuraminidase mutation (such as
H275Y) have been reported [34,35]. Given that influenza viruses
have RNA genomes that are prone to changes, it is imperative to
devise new therapies. Much effort has been made to investigate the
mechanism and devise alternative drugs against the drugresistance issue of H1N1 [36–40]. Developing inhibitors that
target both H1 and N1 antigens can reduce resistance issues
resulting from the mutation of a single target antigen.
Computational approaches have been widely applied to
molecular biology and medicine [41–50]. Structure-based methods, including docking and MD simulation, are invaluable tools in
Introduction
The first global pandemic of the 21st century was announced by
the World Health Organization (WHO) in 2009 due to the
worldwide spread of influenza A subtype H1N1 (H1N1/09) [1].
More than 214 countries have reported laboratory confirmed
cases, and more than 18,449 deaths have been recorded [2].
Currently, the neuraminidase inhibitor TamifluH (oseltamivir)
remains the primary drug prescribed to patients infected with
H1N1/09 [3]. However, the emergence of drug resistant viral
strains [4] and limited drug administration window [5] exemplifies
the need for additional therapies.
Important constituents of influenza surface membrane proteins
include hemagglutinin, neuraminidase, and the matrix protein 2
(M2) proton channel [6,7]. Hemagglutinin mediates the binding of
viral particles to host cell surface sialic acid and the invasion of
viruses into host cell [8–10]. Neuraminidase is responsible for the
cleavage of sialic acid residues to promote the release of progeny
viruses [11,12]. M2 proton channels are critical for viral mRNA
incorporation into the virion and virus budding [13]. Over one
hundred serological subtypes [14] have been identified through
different combinations of the 16 hemagglutinin (H1–H16) and
nine neuraminidase groups (N1–N9) currently known. The 3Dstructure of M2 proton channels have recently been solved in both
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Author Summary
Table 1. Docking results of top ten de novo derivatives.
The influenza A subtype H1N1 (H1N1/09) pandemic raised
public concerns due to drug resistance strains. Drug resistance
occurs from conformational changes causing the original drug
to lose binding ability and exhibit biological effects. The
world’s largest TCM Database@Taiwan was employed to
screen for potential leads that simultaneously bind to H1 and
N1. Three de novo compounds derived from Rosemarinus
officinalis and Guatteria amplifolia were identified as having
dual binding properties to H1 and N1. Structural analysis
indicated that the candidates bind to multiple residues in both
H1 and N1. In addition, the de novo derivatives were predicted
as bioactive using four different computational models. The
compounds are validated as potent dual targeting influenza
drug candidates through multiple validations. Key advantages
of the candidates include (1) binding to H1 and N1 through
multiple amino acids, and (2) not binding to known mutation
residues in H1 or N1. Such advantages can reduce drug
resistance caused by single point mutations. On a broader
context, features important for successful H1N1 drug development are discussed in hopes of providing starting templates
for drug development and improvements.
Docking
Name
H1
N1
H1+N1
Xylopine_2
144.183
139.023
283.206
Rosmaricine_14
138.916
125.808
264.724
Rosmaricine_15
135.671
121.759
257.430
Rosmaricine_5
97.921
116.324
214.245
Rosmaricine_16
96.528
113.750
210.278
Rosmaricine_23
95.854
113.573
209.427
Rosmaricine_12
95.520
113.207
208.727
Rosmaricine_6
95.429
113.069
208.498
Rosmaricine_21
95.233
112.762
207.995
Rosmaricine_11
94.731
112.283
207.014
48.545
87.794
137.482
TamifluH
*
*control.
doi:10.1371/journal.pcbi.1002315.t001
Figure 1. The flowchart of dual-target H1N1 experimental procedures.
doi:10.1371/journal.pcbi.1002315.g001
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and biological functions of biomacromolecules has opened new
frontiers. Vast amounts of information on molecular recognition
and binding [68–71], conformations or conformational changes
[72–75], molecular mechanisms of bioactivity and stability [76–
79], and drug discovery [80–84] have been found. To understand
interaction of drugs with proteins or DNA, consideration should
be given not only to the static structures but dynamical
information obtained by simulation through a dynamic process.
In this regard, both docking and MD simulation were utilized in
this study to provide comprehensive analysis protein-ligand
interactions under static and dynamic conditions.
Much effort has been placed on developing new, effective
influenza treatments, but most have focused on neuraminidase or
M2 as the target protein [37,38,85–87]. To date, no hemagglutinin inhibitor is available. Traditional Chinese medicine (TCM)
has been used extensively for finding effective drugs [88], and we
Figure 2. Structures of the top three de novo derivatives and
their corresponding native TCM compounds.
doi:10.1371/journal.pcbi.1002315.g002
drug discovery and design. Computational docking is important
for investigating ligand-protein interactions and elucidating
binding mechanisms [51–57]. Since publication of the pioneer
paper in 1977 [58], it has been established that low-frequency
motions existing in proteins and DNA can help reveal dynamic
mechanisms underlying fundamental biological functions [59–63].
NMR observation later confirmed such inferences and the findings
were applied to medical treatments [64–67]. In recent years,
application of molecular dynamics to investigate internal motions
Figure 3. Docking pose of TCM candidates and TamifluH in N1.
(A) Xylopine_2, (B) Rosmaricine_14, (C) Rosmaricine_15 and (D)
TamifluH.
doi:10.1371/journal.pcbi.1002315.g003
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Figure 4. Correlation between observed and predicted activities (pIC50) of 27 neuraminidase inhibitors using different
prediction models. (A) MLR and (B) SVM.
doi:10.1371/journal.pcbi.1002315.g004
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binding capacities to H1 and N1 were ranked by combined
DockScore and the top ten derivatives are listed in Table 1. Nine
of the ten top ranking de novo compounds were derived from
Rosmaricine, a natural compound isolated from Rosemarinus
officinalis [96]. The remaining de novo compound was based on
Xylopine, which is naturally found in Guatteria amplifolia [97].
The top three derivatives, Xylopine_2, Rosmaricine_14 and
Rosmaricine_15, have in common a pyridinium addition to their
native structure (Figure 2). The pyridinium addition could be the
main explanation for higher DockScores of these three derivatives
compared to their native compounds and the other derivatives.
Rosmaricine_14 and Rosmaricine_15 differed by the number of
fused rings, but the slight difference in DockScore suggests that
addition of an acyclic ring has little influence on binding affinity.
Table 2. N1 activity prediction (pIC50) of top three de novo
derivatives by MLR and SVM models.
Name
MLR
Xylopine_2
9.837
SVM
7.295
Rosmaricine_14
14.069
5.963
Rosmaricine_15
14.116
5.915
TamifluH*
7.613
6.372
*control.
doi:10.1371/journal.pcbi.1002315.t002
have successfully designed novel medicinal compounds and
identified potential drug leads through traditional Chinese
Medicine Database@Taiwan (TCM@Taiwan) [89]. Preliminary
studies conducted in this lab show potential for TCM compounds
to serve as neuramindase and hemagglutinin inhibitors individually [90–95]. In view of the current needs for drugs effective
against native and mutant H1N1/09 and our promising
preliminary results, the present study integrates the concept of
‘‘dual targeting’’ with the aforementioned computational tools and
TCM in the attempt to identify dual-targeting inhibitors of H1N1
that may be useful for drug development.
Characteristics of De Novo Product Binding Poses
Docking of the de novo compounds back to the receptor
provides insights to modifications that can be made to modulate
or enhance molecular properties and also highlights important
protein-ligand interactions. When docked into the N1 protein
binding site, Xylopine_2 interacts with Asp151 via a protonated
amino group and has pi and hydrogen bond (H-bond)
Results/Discussion
Screening and Structure Analysis
The experimental procedures and screening results after each
filtering step are summarized in Figure 1. Among the 829 native
TCM compounds, 81 docked into both H1 and N1 and were used
for de novo evolution (Table S1). De novo compounds with dual
Figure 6. Important locations for ligand-N1 stability (left) and
relative spatial arrangements (right) within the N1 protein
binding site. (A) Xylopine_2, (B) Rosmaricine_14, (C) Rosmaricine_15,
and (D) TamifluH. Ligands are presented in cyan, amino acids in yellow,
and hydrogen bond formations in white dashed lines.
doi:10.1371/journal.pcbi.1002315.g006
Figure 5. RMSD and total energy profiles of top three de novo
derivatives and TamifluH in N1 complexes during 20 ns MD simulation.
doi:10.1371/journal.pcbi.1002315.g005
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prediction bands and the r2 value = 0.8043. The SVM model was
constructed using identical molecular descriptors and ligands as
the MLR model. The r2 value of the SVM model was 0.8605 and
the correlation between observed and predicted activities of 27
ligands are illustrated in Figure 4B.
Table 2 summarizes the pIC50 values of TamifluH and the top
three candidates as predicted by the generated MLR and SVM
models. The predicted activity of TamifluH using the generated
MLR model (pIC50 = 7.613) is similar to observed bioactivity
values reported in the literature (pIC50 = 7.823) [101]. This
indicates that the generated MLR model is a good prediction
model. Predicted activity values using the SVM model indicate a
lower pIC50 with regard to TamifluH. Nonetheless, both models
indicate that all TCM de novo derivatives are good candidates with
neuraminidase inhibitory activity. MLR and SVM models for
predicting hemagglutinin inhibitory activity were not established
due to the lack of available hemagglutinin inhibitor structures in
the literature.
interactions with Trp179 and Glu228, respectively (Figure 3A).
Rosmaricine_14 (Figure 3B) and Rosmaricine_15 (Figure 3C),
have interactions with Asp151 and Arg293 via the carbonyl
group and Glu228 through the 2-aminopyridinium group.
TamifluH forms H-bond interactions with Arg156, Arg293 and
Arg368, but not with Asp151 or Glu228 (Figure 3D). Both
Asp151 and Glu228 have been reported as one of the major
residues in the N1 ligand binding site [98,99]. The ability of the
de novo derivatives to form interactions with both Asp151 and
Glu228 may account for the higher DockScores.
Binding of the top three de novo derivatives to H1 site is detailed
elsewhere [92]. The ability to bind with important H1 residues
Asp103 and Arg238 [100] indicates the dual targeting possibility of
the candidates.
Prediction of N1 Inhibition by Support Vector Machine
(SVM) and Multiple Linear Regression (MLR)
The top ranking model generated by genetic function
approximation (GFA) includes the following descriptors: ES_
Sum_dssC, CHI_3_C, Kappa_1, Jurs_PNSA_1, and Jurs_RPCS.
Utilizing these five descriptors, the MLR model established for the
neuramindase inhibitors is:
pIC50 ~3:8919
Molecular Dynamics (MD) Simulation
Stability profile analysis. Root mean square deviation
(RMSD) and total energy results from MD are summarized in
Figure 5 and provide information on N1-ligand complex and
ligand stability. During the 20 ns simulation process, the RMSDs
of the four complexes ranged between 1.4–1.7 Å. Xylopine_2
stabilized after 15 ns, and the total energy of the complex
equilibrated at 219,000 kcal/mol. The ligand RMSD of
Rosmaricine_14 remained stable throughout the simulation, and
no evident changes in total energy were observed after 17 ns. The
RMSD and total energy of Rosmaricine_15 stabilized after 13 ns.
Fluctuations in ligand RMSD was observed in TamifluH for the
first 5 ns, but no changes were observed in ligand RMSD and total
energy from 5 ns until the end of MD. The larger ligand RMSD
1:0012 ES Sum dssC
z2:116 CHI 3 C
z0:50012 Kappa 1 0:029158 Jurs PNSA 1
z2:0289 Jurs RPCS
Correlation between the observed and predicted activities of the
27 ligands are shown in Figure 4A. All values were within the 95%
Table 3. H-bond analysis of top three de novo derivatives and TamifluH in N1 during MD simulation.
Name
Xylopine_2
Rosmaricine_14
Rosmaricine_15
TamifluH
H-bond
Ligand atom
Amino acid
H-bond occupancy
H-bond_1
H55
Glu228 : OE2
98.23%
H-bond_2
H57
Glu228 : OE1
7.23%
H-bond_3
H57
Glu228 : OE2
2.73%
H-bond_4
H66
Glu228 : OE1
80.50%
H-bond_5
H66
Glu228 : OE2
35.50%
H-bond_6
H57
Leu224 : O
19.28%
H-bond 1
O1
Arg293 : HH22
4.70%
H-bond 2
H66
Glu119 : OE1
98.63%
H-bond-3
H66
Glu119 : OE2
5.38%
H-bond-4
H55
Glu228 : OE2
96.05%
H-bond 1
O1
Arg293:HH22
99.80%
H-bond 2
H48
Asn344:OD1
78.10%
H-bond-3
H53
Glu228:OE2
34.00%
H-bond_4
H64
Thr226:O
45.95%
H-bond_5
H55
Thr226:O
26.50%
H-bond_6
H27
Tyr402:OH
7.13%
H-bond 1
O3
Arg368:HH22
82.50%
H-bond 2
O10
Arg118:HE
8.90%
H-bond-3
O10
Arg156:HH12
57.33%
H-bond_4
H25
Tyr402:OH
32.30%
doi:10.1371/journal.pcbi.1002315.t003
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Figure 7. H-bond distance profile between N1 and TCM candidates or TamifluH. (A) Xylopine_2, (B) Rosmaricine_14, (C) Rosmaricine_15,
and (D) TamifluH.
doi:10.1371/journal.pcbi.1002315.g007
fluctuations and higher total energy observed for Xylopine_2 may
be attributed to its spatial structure and docking characteristics.
Xylopine_2 consists of a bulky xylopine and a 2-aminopyridinium
residue linked through 1 in a way similar to that of cis
conformations (Figure 6). It is well established that cisconformations are less stable than their trans- counterparts, and
thus may explain the higher total energy levels for Xylopine_2. In
addition, Xylopine_2 binds to N1 though the aminopyridinium
residue, allowing the xylopine structure more freedom to rotate
and thereby increasing ligand RMSDs and total energy levels.
H-Bond network during MD simulation. The H-bond
occupancy of each compound in N1 is summarized in Table 3.
Xylopine_2 can easily form H-bond at Glu228, and the occupancy
rate can reach as high as 98.23%. Rosmaricine_14 forms H-bonds
at Glu119 and Glu228, with high occupancy rates of 98.63% and
96.05%. Rosmaricine_15 forms H-bonds with Arg293 and
Asn344 (99.80% and 78.10%, respectively). TamifluH primarily
forms H-bonds at Arg368 (82.50%) and Arg156 (57.33%). High
H-bond occupancy rates indicate that the compound can maintain
stable binding during the 20 ns MD. Since occupancy is calculated
from the designated cut-off distance of 2.5 Å, distance fluctuations
exceeding the critical value can lead to underestimation of H-bond
formations and skewed conclusions of candidate stability. Distance
profiles of ligand atoms capable of forming H-bonds during MD
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are illustrated in Figure 7. The distance between Xylopine_2 and
Glu228 was maintained between 2–3 Å (Figure 7A). The distance
of Rosmaricine_14 and Glu119 and Glu 228 also remained
between 2–3 Å (Figure 7B). From Table 3, Rosmarcine_15 and
TamifluH did not form high occupancy H-bonds with Tyr402.
Intriguingly, bond distance profiles indicate that Tyr402 was one
of the key amino acids for H-bond formation (Figure 7C, 7D).
Tyr402 bond distance generally exceeded 2.5 Å in
Rosmaricine_15, thus explaining the low occupancy rate in
Table 3. For TamifluH, the distance in the first ns was between
3–4 Å and then decreased to 2–3 Å from 1–20 ns, thus accounting
for the low occupancy rate as well. Despite the low occupancy
rates, the bond distance profiles suggest that Tyr402 is an
important N1 binding site for Rosmaricine_15 and TamifluH.
Possible mechanism for protein-ligand interaction. Insights
to how ligand stabilization occurs within the protein binding site
can be discerned from MD simulation. The H-bond formations
with Leu224 at 2 and Glu228 at 3 and 4 ‘‘sandwich’’ the
aminopyridinium and anchors Xylopine_2 (Figure 6A). However,
the xylopine moiety remained unattached, causing strain to the
compound and possibly contributing to large H-bond distance
fluctuations (Figure 7A). At approximately 15 ns, attraction
between Glu228 and 3 causes the terminal amine residue to
torque towards Glu228 increasing the distance from Leu224
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H1N1 Inhibitors from Traditional Chinese Medicine
Figure 8. Torsion angles of TCM candidates and Tamiflu in H1 and N1. (A) Xylopine_2, (B) Rosmaricine_14, (C) Rosmaricine_15 and (D)
TamifluH. Lower case letters specify the bonds on which the torsion angle were monitored.
doi:10.1371/journal.pcbi.1002315.g008
Figure 7B starting at 3 ns was due to Arg293 intermolecular Hbond formation between the O atom and NH2 residue.
Nonetheless, all H-bonds were maintained throughout the MD
simulation, suggesting good stability of Rosmaricine_14. The
mechanism for stability of Rosmaricine_15 is similar to that of
Rosmaricine_14. In addition to H-bonds at 9 with Glu228 and
10 with Arg293 which are identical to Rosmaricine_14, H-bonds
are formed at 11 with Glu228, 12 with Thr226, 13 with Asn262,
and 14 with Tyr402 (Figure 6C). These additional anchor points
stabilized residues that were available for rotation in
Rosmaricine_14, further lowering the total energy of the
compound (Figure 5C). Within the anchored ligand, twisting of
15 contributes to H-bond fluctuations at Glu228 such as
(Figure 6A). As a result, the stable H-bond with Leu224 was lost,
and an additional H-bond with Glu228 was formed from 2. At
the end of MD simulation, the primary binding force for
Xylopine_2 was H-bonds formed with Glu228. In contrast to
Xylopine_2, multiple binding sites secured Rosmaricine_14 and
15 within the binding site as reflected by the small H-bond
distance fluctuations compared to Xylopine_2 (Figure 7B, 7C).
Rosmaricine_14 bound to Glu119, Glu228, and Arg293 through
6, 7, and 8 respectively (Figure 6B). The multiple attachment
points anchor both the aminopyridine moiety and the
rosmaricine moiety, reducing strain on the molecular structure
as reflected by the low total energy and bond distance
fluctuations. The increase in bond distance from Arg293 in
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Figure 9. Protein-ligand interactions of TCM candidates and TamifluH in H1. H1-ligand interactions following MD are illustrated for (A)
Xylopine_2, (B) Rosmaricine_14, (C) Rosmaricine_15, and (D) TamifluH. Hydrogen bonds are indicated by green dashed lines between the atoms
involved. Amino acids forming H-bonds with the ligand are shown in red. Hydrophobic contacts are represented by arcs with spokes radiating
towards the ligand atoms they contact. The contacted atoms are shown with spokes radiating back.
doi:10.1371/journal.pcbi.1002315.g009
amine groups l and o are visualized by the recorded angle
trajectories. NAG (Figure 8D) and Tamiflu (Figure 8E) both have
relatively stable intermolecular torsion changes. This indicates that
the lower stability of NAG and Tamiflu in H1 and N1,
respectively, are not due to instability of their ligand structures,
but may be attributed to weaker or unstable ligand-protein
affinities.
Hydrophobic interactions. Hydrophobic interactions also
played a role in stabilizing ligands within H1 (Figure 9) and N1
(Figure 10) binding sites during MD. Due to differences in ligand
structure and binding conformation, amino acids with which
hydrophobic interactions were formed differed. In H1, amino
acids involved in hydrophobic interactions included Pro82,
Asp103, Asn104, Cys107, Cys153, and Pro154. More stabilizing
interactions including H-bonds and hydrophobic interactions were
observed in the N1 binding site (Figure 10). For the TCM
candidates and Tamiflu, the spatial distribution of H-bonds
coupled and hydrophobic interactions limits the free movement of
ligands within N1, thus increasing stability of the N1-ligand
complex.
observed at 14 ns (Figure 7C). As a result of the torque at 15, 11
flips toward 12 and the stable H-bond with Glu228 is replaced by
that of Thr226. For Tamiflu, H-bonds formed with Arg156 at 16,
Arg368 at 17, and Tyr402 at 18 anchored the molecule in the
binding site and limited structural fluctuations caused by the
intermolecular H-bond between 17 and 19, and the ‘‘antler-like’’
structure 20 (Figure 6D). The stability of Tamiflu as a result of
these binding anchors is reflected in the low total energy profile
(Figure 5) and small H-bond distance fluctuations (Figure 7D).
The ability of the de novo derivatives to form stable H1-ligand
complexes has also been assessed [92]. All de novo derivatives were
capable of forming H-bonds at Glu83 and Asp103, the key
binding sites on H1.
The torsion angles of flexible bonds in each candidate when in
complex with H1 and N1 are summarized in Figure 8. In
Xylopine_2, all monitored bonds were stable in H1 except for e
(Figure 8A). The fluctuations could be attributed to the attraction
between the amine group H atoms and Asp 103. When bound to
N1, b was the primary location for torque changes in Xylopine_2.
The recorded torsion angle changes at b support our previous
speculation that the unattached xylopine moiety is a key source of
instability for Xylopine_2. Torsion angle fluctuations of Rosmaricine_14 in both H1 and N1 were mainly due to rotations at g and
j (Figure 8B). Such changes are expected as the H atoms on the
amine group continuously rotate to form H-bonds with key amino
acids. Bonds in Rosmaricine_15 (Figure 8C) exhibited similar
characteristics to those in Rosmaricine_14. Rapid rotations at the
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CoMFA and CoMSIA Analysis
To further investigate docking features, CoMFA and CoMSIA
models were built and validated using 27 neuraminidase inhibitors
listed in Table S2. The PLS analyses results for CoMFA and
CoMSIA models are shown in Table 4. The CoMFA model was
generated using both steric and electrostatic fields and yielded a
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Figure 10. Protein-ligand interactions of TCM candidates and TamifluH in N1. H1-ligand interactions following MD are illustrated for (A)
Xylopine_2, (B) Rosmaricine_14, (C) Rosmaricine_15, and (D) TamifluH. Hydrogen bonds are indicated by green dashed lines between the atoms
involved. Amino acids forming H-bonds with the ligand are shown in red. Hydrophobic contacts are represented by arcs with spokes radiating
towards the ligand atoms they contact. The contacted atoms are shown with spokes radiating back.
doi:10.1371/journal.pcbi.1002315.g010
Table 4. CoMFA and CoMSIA analysis for N1 using PLS.
Cross
validation
2
Non-cross validation
r2
SEE
F
0.524
0.924
0.372
63.567
0.426
0.936
0.369
41.649
5
0.255
0.871
0.626
12.652
H
5
0.337
0.979
0.212
132.092
D
5
0.220
0.737
0.750
7.940
A
5
0.221
0.798
0.658
11.186
S+E
5
0.200
0.925
0.401
34.912
S+H
6
0.565
0.977
0.223
119.225
S+H+D
5
0.570
0.906
0.435
34.000
H+D+A
6
0.528
0.957
0.304
62.855
S+H+D+A*
5
0.673
0.937
0.357
53.360
S+E+H+D+A
5
0.561
0.946
0.331
62.823
Model
Fraction
ONC
q
CoMFA
-
5
CoMSIA
S
5
E
cv
non-cross validated r2 value of 0.924 and a cross validated q2 value
of 0.524 with an optimal number of components as 5. The optimal
CoMSIA model (r2 = 0.937, q2 = 0.673, ONC = 5) consisted of
steric and hydrophobic fields, H-bond acceptors and donors.
When compared against actual observed activities [102], both
CoMFA and CoMSIA models had good predictability, predicting
pIC50 values that differed only marginally from the actual pIC50
values of 24 compounds (Table 5).
The validated CoMFA and CoMSIA maps were used to assess
ligand bioactivity. Contour of the de novo compounds at 20 ns MD
simulation to the relative spatial positions of CoMFA and
CoMSIA feature maps are shown in Figure 11. In Xylopine_2,
Rosmaricine_14 and Rosmaricine_15, the H-bond between the
2-aminopyridinium group and Glu228 matched the electropositive group feature of the CoMFA model (Figure 11A,11C,11E)
and the H-bond donor feature in CoMSIA model
(Figure 11B,11D,11F). The hydrophobic benzene structures of
Xylopine_2 matched the steric favoring region of the CoMFA
map and the hydrophobic feature of the CoMSIA map. The
carbonyl groups in Rosmaricine_14 and Rosmaricine_15 which
formed H-bonds with Tyr402 satisfied the H-bond acceptor
feature in the CoMSIA model. TamifluH also contours to both
CoMFA and CoMSIA models. The 3-methoxypentane group
close to Arg293 and Asn344 matched the steric favoring region of
CoMFA (Figure 11G) and the hydrophobic feature of CoMSIA
(Figure 11H). This residue has similar characteristics to the 2aminopyridinium group in the de novo derivatives. In addition, the
N-methylacetamide group in TamifluH, which forms H-bond
ONC: Optimal number of components.
SEE: Standard error of estimate.
F: F-test value.
S: Steric.
E: Electrostatic.
H: Hydrophobic.
D: Hydrogen bond donor.
A: Hydrogen bond acceptor.
*: optimum CoMSIA model.
doi:10.1371/journal.pcbi.1002315.t004
PLoS Computational Biology | www.ploscompbiol.org
9
December 2011 | Volume 7 | Issue 12 | e1002315
H1N1 Inhibitors from Traditional Chinese Medicine
Table 5. Observed and predicted activities of 27 neuraminidase inhibitors by CoMFA and CoMSIA models.
CoMFA
pIC50a
CoMSIA
Compound
Observed
Predicted pIC50
Residual
Predicted pIC50
1
5.20
5.21
0.01
4.97
Residual
20.23
2
5.70
6.00
0.30
5.85
0.15
3
6.74
6.69
20.05
6.40
20.34
4
6.52
6.44
20.08
6.42
20.10
5
6.70
6.75
0.04
6.67
20.03
6
8.00
8.36
0.36
7.82
20.18
7
9.00
8.75
20.25
8.31
20.69
8
7.80
7.82
0.02
8.62
0.82
9
6.82
6.93
0.11
7.00
0.18
10
9.00
8.94
20.06
8.93
20.07
11
5.70
5.45
20.25
5.57
20.13
12
6.65
6.54
20.11
6.48
20.17
13
5.66
5.81
0.15
5.81
0.15
14
7.66
7.88
0.22
7.59
20.07
15
7.22
7.34
0.12
7.16
20.06
16
6.28
6.23
20.05
6.68
0.40
17
5.82
5.76
20.06
6.08
0.25
18
7.00
6.58
20.42
6.76
20.24
19
4.60
4.79
0.19
4.84
0.24
20
8.74
8.58
20.16
8.80
0.06
21
8.34
8.40
0.06
8.30
20.04
22
5.51
5.55
0.04
5.38
20.13
23
5.47
5.49
0.02
5.39
20.08
24
5.64
5.49
20.15
5.96
0.32
21.02
25*
3.70
4.03
20.33
4.72
26*
5.43
5.23
0.20
5.56
20.13
27*
6.85
7.97
21.12
7.74
20.89
a
: Adapted from [102].
*: test set.
doi:10.1371/journal.pcbi.1002315.t005
and N1, but that biological activity was also predicted in both
binding sites, thus it is possible to develop dual-targeting drugs
from the selected de novo derivatives.
Important features for potential H1 and N1 inhibitors are
summarized in Figure 13. For H1, a salt bridge with Glu83 and
H-bond donor and/or electrostatic interactions with Asp103 are
important characteristics that should be met. Potential inhibitors
for N1 should have salt bridge and/or H-bond formation at
Glu228 and interactions with Asp293. These features can be
used to identify or design novel drugs for H1 and/or N1. In the
case of the TCM de novo derivatives from this study, each
compound could structurally fulfill the requirements of both H1
(Figure 13A,13B,13C) and N1 (Figure 13D,13E,13F) binding
sites, thus supporting their potential as dual-targeting compounds.
In this research, we identified Xylopine_2, Rosmaricine_14,
and Rosmaricine_15 as the top three de novo derivatives exhibiting
binding affinity to H1 and N1. Addition of a pyridinum residue to
the native structures of xylopine and rosmaricine contributes to
bond formation at key residues in both H1 (Glu83, Asp103) and
N1 (Glu228, Arg292). The de novo derivatives were predicted as
with Tyr402, is located near the H-bond donor feature in
CoMSIA.
Though all compounds contoured to the N1 inhibitor features
identified by CoMFA and CoMSIA, a critical difference was
observed between TamifluH and the TCM de novo derivatives. All
compounds except TamifluH formed H-bonds at Glu228. As
Glu228 is a primary binding site of N1 [99], ability of the TCM de
novo derivatives to maintain stable binding with Glu228 during
MD simulation supports the potential of these compounds as drug
alternatives to TamifluH.
Due to the lack of reported H1 ligand bioactivities in the
literature, direct assessment of bioactivity through construction of
CoMFA and CoMSIA models was not possible. Alternatively,
indirect support was provided by assessing the ability of de novo
derivatives to maintain contour to the N1 CoMFA/CoMSIA
maps while forming interactions at key residues in H1, Glu83 and
Asp103 [92]. As illustrated in Figure 12 the TCM de novo
derivatives docked into the H1 binding site and formed critical
interactions at Glu83 and Asp103 without losing contour to the
CoMFA and CoMSIA maps. These results suggest that not only
were the TCM de novo derivatives capable of docking into both H1
PLoS Computational Biology | www.ploscompbiol.org
10
December 2011 | Volume 7 | Issue 12 | e1002315
H1N1 Inhibitors from Traditional Chinese Medicine
Figure 12. CoMFA (left) and CoMSIA (right) models of the latest
N1 conformation with TCM candidates and TamifluH at 20 ns
simulation time within the H1 protein binding site. Respective
CoMFA and CoMSIA models of Xylopine_2 (A, B), Rosmaricine_14 (C, D),
and Rosmaricine_15 (E, F). Contours in CoMFA represent the following
features: favors bulky substituent (green), disfavors bulky substituent
(yellow), favors electropositive groups (blue), and disfavors electropositive groups (red). Features in CoMSIA model include: hydrophobic
(cyan), hydrophilic (yellow), favorable H-bond acceptor (magenta),
unfavorable H-bond acceptor (green), H-bond donor (orange), disfavor
H-bond donors (purple).
doi:10.1371/journal.pcbi.1002315.g012
Figure 11. CoMFA (left) and CoMSIA (right) models of the N1
conformation with TCM candidates and TamifluH at 20 ns
simulation time. Respective CoMFA and CoMSIA models of Xylopine_2 (A, B), Rosmaricine_14 (C, D), Rosmaricine_15 (E, F) and Tamiflu
(G, H) are given. Contours in CoMFA represent the following features:
favors bulky substituent (green), disfavors bulky substituent (yellow),
favors electropositive groups (blue), and disfavors electropositive
groups (red). Features in CoMSIA model include: hydrophobic (cyan),
hydrophilic (yellow), favorable H-bond acceptor (magenta), unfavorable
H-bond acceptor (green), H-bond donor (orange), disfavor H-bond
donors (purple).
doi:10.1371/journal.pcbi.1002315.g011
Key binding locations of the de novo derivatives include Glu83
and Asp103 for H1, and Glu228 and Arg292 for N1. Mutations
currently attributed to oseltamivir resistance are located at H275
and N295S of the NA [103]. Since the key binding locations of the
TCM derivatives do not overlap with those causing oseltamivir
resistance, derivatives will be able to bind to viruses that are
currently resistant to TamifluH. In addition, the de novo derivatives
do not bind to amino acids in H1 or N1 that are prone to mutation
(Table 6, Table 7) [40,104], thus would likely be able to exert
activity across a range of mutant H1N1 viruses. Last but not the
least, multiple bond formations observed in MD provide
additional insurance against possible mutations at key binding
residues. In the case of a single point mutation, the de novo
compounds will remain bound to the H1 and N1 sites through
another key residue, therefore resisting the development of drug
resistance in the virus. Based on the results and observations of this
study, the TCM de novo derivatives may be attractive compounds
for designing novel dual-target inhibitors for H1 and N1.
active by the SVM and MLR models, and contoured well to the
3D-QSAR models. The TCM de novo derivatives were able to
maintain contour while forming key binding interactions in H1,
thus providing indirect support for bioactivity in H1. The results
of this study indicate that the TCM de novo derivatives not only
can bind to, but can also exhibit biological activities in both H1
and N1.
PLoS Computational Biology | www.ploscompbiol.org
11
December 2011 | Volume 7 | Issue 12 | e1002315
H1N1 Inhibitors from Traditional Chinese Medicine
TamifluH was used as the control for N1, and its N1 docking score
was set as the minimum requirement. The top TCM compounds that
passed the filtering were selected for de novo evolution.
De Novo Evolution and Lipinski’s Rule of Five
In de novo evolution, TCM compounds were placed into the H1
and N1 protein binding sites described previously, and Ludifragments were attached to the native structure. The new
derivatives were generated in full evolution mode. Derivatives
from de novo evolution were subjected to additional screening
through Lipinski’s rule [106] to rule out orally unstable or
pharmacologically inapplicable compounds. As de novo products
generated for H1 and N1 proteins differed, all de novo products
were re-docked to H1 and N1 proteins to assess binding affinity.
De novo products that docked into both H1 and N1 proteins were
selected and ranked by the sum of their respective H1 and N1
DockScore. The top ten compounds with the highest DockScore
were selected for further structure-based analysis.
Bioactivity Prediction by SVM and MLR
The 27 neuraminidase inhibitors used, including 24 training set
compounds and 3 test set compounds, were adapted from Zhang’s
study [102]. Compounds were drawn using ChemBioOffice 2008
(PerkinElmer Inc., Cambridge, MA) and modified to physiological
ionization using the Prepare Ligand function in DS 2.5. Bioactivity
values (IC50) were also obtained from Zhang’s study though the
original sources were not clarified, and converted to pIC50 (log(1/
IC50)). Molecular descriptors of the compounds were calculated using
Calculate Molecular Properties in DS 2.5 and the GFA was used to
select the best representative molecular descriptors [107]. Utilizing
the best representative molecular descriptors identified through
GFA, MLR and SVM models were constructed using MATLAB
(The Mathworks Inc., Natick, MA) and LibSVM [108], respectively,
and used to predict the bioactivity of TCM de novo compounds.
Figure 13. Key features of TCM candidates for stable binding in
H1 and N1. (A) Xylopine_2 in H1, (B) Rosmaricine_14 in H1, (C)
Rosmaricine_15 in H1, (D) Xylopine_2 in N1, (E) Rosmaricine_14 in N1,
and (F) Rosmaricine_15 in N1.
doi:10.1371/journal.pcbi.1002315.g013
MD Simulation
The MD simulation was performed using the Molecular
Dynamics package of DS 2.5. The complexes were created with
a 10 Å solvation shell of TIP3 water around the protein. Sodium
cations were added to each system for neutralization. Minimization using Steepest Descent and Conjugate Gradient were
performed at 500 cycles each. Each protein-ligand complex was
gradually heated from 0K to 310K over 50 ps, followed by a
200 ps equilibration phase. The production stage was performed
for 20 ns using NVT canonical ensemble and trajectory frames
were saved every 20 ps. SHAKE algorithm was applied to
immobilize all bonds involving hydrogen atoms throughout the
MD simulation. Long-range electrostatics were treated with PME
method. Time step was set to 2 fs for all MD stages. The
temperature coupling decay time for the Berendsen thermal
coupling method was 0.4 ps. Post processing of the trajectory was
performed using Analyze Trajectory module. Torsion angles of
each bond were also monitored through DS 2.5. LIGPLOT [109]
was used to generate schematic diagrams of protein-ligand
interactions for each candidate and control in H1 and N1.
Materials and Methods
Software
Virtual screening, de novo derivative generation, and molecular
dynamics (MD) simulation were performed using Discovery Studio
Client v2.5.0.9164 (DS2.5; Accelrys Inc., San Diego, CA). The
two-dimensional and three-dimensional structures of TCM
compounds were generated using ChemBioOffice 2008 (PerkinElmer Inc., Cambridge, MA). Comparative molecular field
analysis (CoMFA) and comparative molecular similarities indices
analysis (CoMSIA) models were constructed using SYBYLß 8.3
package (Tripos Inc., St. Louis, MO).
Docking Analysis
Compounds from the TCM Database@Taiwan were docked to
H1 and N1 protein active sites reported in our previous study [91].
All procedures were completed under the forcefield of Chemistry
at HARvard Molecular Mechanics (CHARMm) [105]. The
virtual screening process was performed using LigandFit. The
conformational search method was based on the Monte Carlo
algorithm. Rigid body minimization following initial ligand
placement was completed using Smart Minimizer. Scoring
functions used by LigandFit were DockScore.
TCM compounds that docked into both H1 and N1 proteins were
selected and then ranked by the sum of their H1 and N1 DockScore.
PLoS Computational Biology | www.ploscompbiol.org
CoMFA and CoMSIA Models
CoMFA and CoMSIA models were constructed through the
partial least square (PLS) analysis using previously described
neuraminidase inhibitors [102]. The optimal number of components was obtained from leave-one-out method to yield the highest
r2 and q2 values in non-cross validation and cross-validation,
respectively. Biological activities of the TCM de novo compounds
were evaluated based on contour to the generated 3D-QSAR map.
12
December 2011 | Volume 7 | Issue 12 | e1002315
PLoS Computational Biology | www.ploscompbiol.org
13
-
A/Taiwan/0045/2006 (Clade 2)
A/Taiwan/6566/2006 (Clade 2C-1)
A/Taiwan/2832/2008 (Clade 2C-2)
-
-
-
-
-
-
-
D
D
E
E
E
E
E
144
-
-
-
-
-
-
-
S
S
T
T
T
T
T
145
-
-
-
-
-
-
-
N
N
T
T
T
T
T
146
-
A/Taiwan/2823/2008 (Clade 2A)
A/Taiwan/10092/2007 (Clade 2B-1)
A/Taiwan/9042/2008 (Clade 2B-2)
A/Taiwan/0045/2006 (Clade 2)
A/Taiwan/6566/2006 (Clade 2C-1)
A/Taiwan/2832/2008 (Clade 2C-2)
237
-
-
-
-
-
-
-
V
V
I
V
V
V
V
239
-
-
-
-
-
-
-
D
D
G
D
D
G
D
241
-
-
-
-
-
-
-
E
E
A
A
A
A
A
-
-
-
-
-
-
-
V
V
L
L
L
L
L
251
-
-
-
-
-
-
-
P
P
S
S
S
S
S
154
Data compiled from Yang et al. (2011) [40] and Morlighem et al. (2011) [104].
-: Information not provided in original research.
doi:10.1371/journal.pcbi.1002315.t006
I
-
I
Japan_HA_Samples ‘‘I’’
A/Taiwan/5515/2005 (Clade 1)
A
Japan_HA_Samples ‘‘II’’
A
A/Brevig_Mission/1/18 (H1N1)
A/London/1/1919(H1N1)
A
A
A/New_York/1/18(H1N1)
A/London/1/1918(H1N1)
A
A/South_Carolina/1/18(H1N1)
233
Amino acid location
-
A/Taiwan/9042/2008 (Clade 2B-2)
R
Japan_HA_Samples ‘‘II’’
-
R
Japan_HA_Samples ‘‘I’’
A/Taiwan/10092/2007 (Clade 2B-1)
K
A/London/1/1919(H1N1)
-
K
A/London/1/1918(H1N1)
-
K
A/Brevig_Mission/1/18 (H1N1)
A/Taiwan/2823/2008 (Clade 2A)
K
A/New_York/1/18(H1N1)
A/Taiwan/5515/2005 (Clade 1)
K
A/South_Carolina/1/18(H1N1)
130
Amino acid location
-
-
-
-
-
-
-
K
K
-
-
T
T
T
256
-
-
-
-
-
-
-
H
H
Y
Y
Y
Y
Y
155
-
-
-
-
-
-
-
V
V
-
-
I
I
I
266
S
S
S
N
S
S
S
A
A
A
A
A
A
A
158
-
-
-
-
-
-
-
V
V
-
-
A
A
A
267
-
-
-
-
-
-
-
K
K
S
S
S
S
S
159
Table 6. HA mutation points between the 1918 H1N1 and H1N1/09 viruses.
-
-
-
-
-
-
-
R
R
-
-
W
W
W
269
-
-
-
-
-
-
-
K
K
R
R
R
R
R
163
-
-
-
-
-
-
-
M
M
-
-
L
L
L
274
-
-
-
-
-
-
-
I
I
L
L
L
L
L
166
-
-
-
-
-
-
-
E
E
-
-
N
N
N
275
-
-
-
-
-
-
-
V
V
T
T
T
T
T
169
-
-
-
-
-
-
-
N
N
-
-
G
G
G
277
-
-
-
-
-
-
-
N
N
S
S
S
S
S
173
-
-
-
-
-
-
-
A
A
-
-
S
S
S
278
-
-
-
-
-
-
-
I
I
V
V
V
V
V
183
-
-
-
-
-
-
-
I
I
-
-
T
T
T
284
-
-
-
-
-
-
-
D
D
V
V
V
V
V
185
-
-
-
-
-
-
-
T
T
-
-
A
A
A
287
-
-
-
-
-
-
-
I
I
V
V
V
V
V
196
-
-
-
-
-
-
-
T
T
-
-
K
K
K
295
N
N
N
N
N
N
N
S
S
P
P
P
P
P
200
-
-
-
-
-
-
-
K
K
-
-
H
H
H
300
G
G
G
A
G
G
G
G
G
G
S
G
G
G
202
-
-
-
-
-
-
-
T
T
-
-
S
S
S
305
-
-
-
-
-
-
-
A
A
Y
Y
Y
Y
Y
203
-
-
-
-
-
-
-
I
I
-
-
V
V
V
315
T
T
A
T
A
A
A
Q
Q
Q
Q
Q
Q
Q
206
-
-
-
-
-
-
-
K
K
-
-
E
E
E
319
-
-
-
-
-
-
-
F
F
S
S
S
S
S
217
-
-
-
-
-
-
-
K
K
-
-
R
R
R
325
-
-
-
-
-
-
-
T
S
S
S
S
S
S
220
-
-
-
-
-
-
-
L
L
-
-
M
M
M
331
-
-
-
-
-
-
-
R
R
K
K
K
K
K
222
-
-
-
-
-
-
-
V
V
-
-
I
I
I
338
-
-
-
-
-
-
-
S
S
N
N
N
N
N
224
-
-
-
-
-
-
-
V
V
-
-
I
I
I
362
-
-
-
-
-
-
-
K
K
R
R
R
R
R
225
-
-
-
-
-
-
-
L
L
-
-
Q
Q
Q
382
-
-
-
-
-
-
-
K
K
R
R
R
R
R
226
-
-
-
-
-
-
-
E
E
-
-
G
G
G
391
-
-
-
-
-
-
-
K
K
T
T
T
T
T
228
H1N1 Inhibitors from Traditional Chinese Medicine
December 2011 | Volume 7 | Issue 12 | e1002315
PLoS Computational Biology | www.ploscompbiol.org
V
V
-
Japan_HA_Samples ‘‘I’’
Japan_HA_Samples ‘‘II’’
A/Taiwan/5515/2005 (Clade 1)
A/Taiwan/2823/2008 (Clade 2A)
A/Taiwan/10092/2007 (Clade 2B-1)
A/Taiwan/9042/2008 (Clade 2B-2)
A/Taiwan/0045/2006 (Clade 2)
A/Taiwan/6566/2006 (Calde 2C-1)
A/Taiwan/2832/2008 (Clade 2C-2)
16
-
-
-
-
-
-
-
T
T
V
17
-
-
-
-
-
-
-
I
I
V
19
-
-
-
-
-
-
-
M
M
I
14
-
A/Taiwan/10092/2007 (Clade 2B-1)
A/Taiwan/9042/2008 (Clade 2B-2)
A/Taiwan/0045/2006 (Clade 2)
A/Taiwan/6566/2006 (Clade 2C-1)
A/Taiwan/2832/2008 (Clade 2C-2)
-
-
-
-
-
-
-
I
I
M
188
-
-
-
-
-
-
-
N
N
G
189
G
G
G
G
G
G
G
G
G
G
194
20
Q
Q
Q
Q
Q
R
R
N
N
N
222
-
-
-
-
-
-
-
A
A
I
21
M
M
M
M
M
M
V
V
V
V
234
-
-
-
-
-
-
-
N
N
S
Data compiled from Yang et al. (2011) [40] and Morlighem et al. (2011) [104].
-: Information not provided in original research.
Amino acid mutation contributing to oseltamivir-resistance (H275Y) are located at 275 and shown in bold.
doi:10.1371/journal.pcbi.1002315.t007
-
A/Taiwan/2823/2008 (Clade 2A)
V
Japan_HA_Samples ‘‘II’’
-
V
Japan_HA_Samples ‘‘I’’
A/Taiwan/5515/2005 (Clade 1)
A
A/Brevig_Mission/1/18 (H1N1)
166
Amino acid location
I
A/Brevig_Mission/1/18 (H1N1)
13
Amino acid location
Table 7. NA mutation points between 1918 H1N1 and H1N1/09 viruses.
34
-
-
-
-
-
-
-
V
V
I
241
-
-
-
-
-
-
-
I
I
V
40
-
-
-
-
-
-
-
D
N
N
248
-
-
-
-
-
-
-
L
L
T
45
-
-
-
-
-
-
-
F
F
L
256
-
-
-
-
-
-
-
Q
Q
H
46
-
-
-
-
-
-
-
R
R
K
257
-
-
-
-
-
-
-
I
I
P
53
-
-
-
-
-
-
-
I
I
V
263
-
-
-
-
-
-
-
V
V
I
74
-
-
-
-
-
-
-
V
V
T
264
-
-
-
-
-
-
-
F
F
V
75
-
-
-
-
-
-
-
V
V
I
267
-
-
-
-
-
-
-
A
A
V
79
-
-
-
-
-
-
-
M
M
L
269
-
-
-
-
-
-
-
S
S
D
80
81
-
H
Y
Y
H
Y
S
H
H
S
H
-
T
H
H
285
-
-
-
-
-
-
-
V
V
T
275*
-
-
-
-
-
-
-
V
V
A
84
-
-
-
-
-
-
-
S
S
G
286
-
-
-
-
-
-
-
K
K
I
86
-
-
-
-
-
-
-
E
E
K
287
-
-
-
-
-
-
-
A
A
T
94
-
-
-
-
-
-
-
I
I
V
288
-
-
-
-
-
-
-
V
V
I
105
-
-
-
-
-
-
-
T
T
M
289
-
-
-
-
-
-
-
S
S
G
126
-
-
-
-
-
-
-
N
N
D
307
-
-
-
-
-
-
-
P
P
H
149
-
-
-
-
-
-
-
E
E
D
311
-
-
-
-
-
-
-
I
I
V
163
G
G
G
G
D
G
G
D
D
D
354
-
-
-
-
-
-
-
I
I
V
H1N1 Inhibitors from Traditional Chinese Medicine
December 2011 | Volume 7 | Issue 12 | e1002315
H1N1 Inhibitors from Traditional Chinese Medicine
Supporting Information
Acknowledgments
Table S1 TCM database screening results of H1 and N1.
We are grateful to the Asia University cloud-computing facilities.
(DOC)
Author Contributions
Molecular structures of training and test set compounds for N1 CoMFA and CoMSIA modeling.
(DOC)
Table S2
Conceived and designed the experiments: CYCC. Performed the
experiments: HJH. Analyzed the data: HJH, SSC. Contributed reagents/materials/analysis tools: CYCC. Wrote the paper: CYCC SSC.
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