Finding Inhibitors of Mutant Superoxide Dismutase-1 for ... · Dismutase-1 for Amyotrophic Lateral Sclerosis Therapy from Traditional Chinese ... method is regard as coulomb ... 1

Research ArticleFinding Inhibitors of Mutant SuperoxideDismutase-1 for Amyotrophic Lateral SclerosisTherapy from Traditional Chinese Medicine

Hung-Jin Huang1 Tung-Ti Chang2 Hsin-Yi Chen3 and Calvin Yu-Chian Chen34

1 Department of Chinese Pharmaceutical Sciences and Chinese Medicine Resources College of Pharmacy China Medical UniversityTaichung 40402 Taiwan

2 School of Chinese Medicine College of Chinese Medicine China Medical University Taichung 40402 Taiwan3Department of Biomedical Informatics Asia University Taichung 41354 Taiwan4 School of Medicine College of Medicine China Medical University Taichung 40402 Taiwan

Correspondence should be addressed to Calvin Yu-Chian Chen ycc929MITedu

Received 10 January 2014 Revised 6 February 2014 Accepted 6 February 2014 Published 18 May 2014

Academic Editor Fuu-Jen Tsai

Copyright copy 2014 Hung-Jin Huang et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Superoxide dismutase type 1 (SOD1) mutations cause protein aggregation and decrease protein stability which are linked toamyotrophic lateral sclerosis (ALS) disease This research utilizes the worldrsquos largest traditional Chinese medicine (TCM) databaseto search novel inhibitors of mutant SOD1 and molecular dynamics (MD) simulations were used to analyze the stability of proteinthat interacted with docked ligands Docking results show that hesperidin and 23541015840-tetrahydroxystilbene-2-O-120573-D-glucoside(THSG) have high affinity to mutant SOD1 and then dopamine For MD simulation analysis hesperidin and THSG displayedsimilar value of RMSD with dopamine and the migration analysis reveals stable fluctuation at the end of MD simulation timeInterestingly distance between the protein and ligand has distinct difference and hesperidin changes the position from initialbinding site to the other place In flexibility of residues analysis the secondary structure among all complexes does not changeindicating that the structure are not affect ligand bindingThe binding poses of hesperidin and THSG are similar to dopamine aftermolecular simulation Our result indicated that hesperidin and THSG might be potential lead compound to design inhibitors ofmutant SOD1 for ALS therapy

1 Introduction

Mutations in CuZn-binding superoxide dismutase type 1(SOD1) could decrease protein stability and increase aggre-gation SOD1 variants are associated with amyotrophic lat-eral sclerosis (ALS) [1ndash4] ALS belongs to motor neurondegenerative disease in the cortex brainstem and spinal cord[5 6] which is similar to Alzheimerrsquos disease Parkinsonrsquosdisease and Huntingtonrsquos disease and the symptoms of ALSinclude degenerative disorder of upperlower motor neuronsand denervation of muscle fibres leading to loss of motorneuron progressive muscular paralysis and muscle atrophy[7 8] resulting in weakness of voluntary muscles till death

because of respiratory failure [9] A recent study indicatedthat mutation of SOD1 gene has genetic linkage in ALSdisease ALS-associated SOD 1 mutations including alanine4 by valine (A4V) [10ndash13] histidine 46 by arginine (H46R)[14ndash16] and I113T mutation [17ndash19] The most popular ALS-causing mutation is A4V mutant type in United States near50 of SOD1-ALS patients are associated with the A4Vmutation [20] H46R has been identified in Japanese about80 Japanese familial ALS in the affectedmembers [21] I113Ttypemutation is another one of the common SOD1mutationsof FALS [22] and many cases of clinical manifestations arelinked to the I113T SOD1 mutation [23] They proposeda toxic gain of function caused by mutant SOD due to

Hindawi Publishing CorporationEvidence-Based Complementary and Alternative MedicineVolume 2014 Article ID 156276 12 pageshttpdxdoiorg1011552014156276

2 Evidence-Based Complementary and Alternative Medicine

the aggregation [24 25] hence designing novel drugs forinhibition of SOD1 aggregation and stabilization have beenused in ALS treatments [26]

The aim of this study is to focus on drug design ofmutant SOD1 The complex of mutant SOD1 and dopaminewas used to investigate the novel inhibitors of mutant SOD1for inhibiting aggregation Computer-aided drug design(CADD) is rapid approach for drug discovery [27ndash30] whichis based on risk factor studies [31ndash36] theory [37] and webserver [38] for developing novel leading compounds CADDhas been wildly used to design new drugs in many casessuch as virus [39 40] inflammation [41] cancer [42ndash45]insomnia [46] weight loss [47] erectile dysfunction [48ndash50] nerve system [51ndash53] and diabetes [54] TraditionalChinese medicine (TCM) has been used over two thousandyears in clinical therapy and many studies utilized TCMto investigate new therapies [55ndash58] In our study smallmolecules from the world TCM database [59] was used toscreen for searching potential compounds with high affinityin mutant SOD1 active site and we further utilizedmoleculardynamics (MD) simulation to verify the stability betweenprotein and ligands for binding assay Synthetic drug oftenhas side effect in clinical treatments and our results providednature product as drug candidate which is safer and reduceadverse reactions

2 Materials and Methods

21 Database Screening The crystal structure of mutantSOD1 was obtained from PDB database (PDB code 4A7V)[60] We employed Prepare Protein module of AccelrysDiscovery Studio 2559350 (DS 25) software [61] to cleanup mistakes of each residue on mutant SOD structure suchas deleting alternate conformations modeling missing loopsand removing water molecules This module also predictstitration site pKs for each amino acid and the pH value of74 was used to protonate all residues PONDR-FIT [62] wasused to predict the orderdisorder in mutant SOD1 structureThe 61000 TCM compounds were downloaded from theTCM DatabaseTaiwan [59] for database screening we alsoemployed TCM compounds from Changrsquo lab for bindingassay [63] and MM2 force field [64] of ChemBioOffice 2010software was carried out to optimize and calculate the 3Dconformation of TCM compounds All compounds generatedifferent conformations by Monte-Carlo techniques underLigandFit module [65] of DS 25 which were docked intomutations SOD1 binding site for protein-ligand interactionanalysis Minimization of all docking poses was based onCHARMm force field [66] and we used Smart minimizeralgorithm as minimization algorithm for ligands minimiza-tion [67 68] which contains steepest descent and conjugategradient The steepest descent performed 1000 steps andfollowed by conjugate gradient minimization

22 Molecular Dynamics (MD) Simulation Protein-ligandstructures were obtained from results of docking studyand the starting conformation of protein-ligand complex

was performed using GROMACS 455 package [69] formolecular dynamic simulation using charmm27 force fieldThe protein structure was placed in cubic box containingTIP3P water molecules The distance between protein andbox was set to 12 nm and the van der Waals cutoff to 14 nmParticle mesh Ewald (PME) method is regard as coulombtype for calculating electrostatic interaction and LINCSalgorithmwas used to restrain the lengths of all bonds amongall simulations For obtaining topology file and parametersof small compounds we employed SwissParam to generatethese data and compatible with the CHARMMall atoms forcefield for GROMACS simulation In system neutralizationwe added Na and Cl ions to randomly replace solventmolecules in simulation systems and the concentration ofNaCl model was set as 0145M The time step was set to0002 ps for MD simulation Steepest descent algorithm wasapplied to energy minimization for 5000 cycle steps Thefollowing procedure is equilibration which was performedunder position restraints for 100 ps to relax solvent in pro-tein structure under constant temperature dynamics (NVT)condition Production simulations perform 5000 ps at finalstep for all simulation systems under constant pressure andtemperature (NPT) dynamics Temperature of all simulationsystems was set to 310 K All MD frames were saved every20 ps for trajectory analysis

23 Analysis of MD Simulation Trajectory analysis of MDconformations was calculated by GROMACS 455 [69]including root mean square deviation (RMSD) root meansquare fluctuation (RMSF) and mean square displacement(MSD) The secondary structures analysis was performed byDSSP program under GROMACS 455 Linkage clusteringalgorithm was used to identify the most populated structuralrepresentations of conformation duringMD simulationsTheRMSD cutoff for cluster analysis was set as 013

3 Results and Discussion

31 Docking Results of Database Screening To analysis dis-order region we employed PONDR-FIT [62] to predict theorderdisorder inmutant SOD1 structureThe sequence num-ber from 21 to 32 and from98 to 100 are binding site ofmutantSOD1 (Figure 1) The disorder disposition values among thisrange are below 05 which indicates that the binding site isfolded orderly and that the protein structure may not affectligand binding [70 71] For docking analysis we based on -PLP1 -PLP2 -PMF and Dock Score to evaluate the dockingpose of traditional Chinese medicine (TCM) compoundsFrom scoring analysis dopamine was regarded as control forcomparing with TCM compounds The score values fromdocking poses of TCM compounds are shown in Table 1 Alldocked ligands are ranked by Dock Score and we foundthat hesperidin and 23541015840-tetrahydroxystilbene-2-O-120573-D-glucoside (THSG) [63] with Dock Score (including scorevalues of -PLP1 -PLP2 and -PMF) are higher than dopamineWe selected hesperidin THSG and dopamine for furtherstudies and chemical scaffolds of these small molecular

Evidence-Based Complementary and Alternative Medicine 3

Table 1 Results of TCM compounds interacted with mutant SOD1 structure by LigandFit docking analysis

Name -PLP1 -PLP2 -PMF DOCK SCOREHesperidin 3372 3961 9525 9191THSG 3736 4161 10123 8816Hyperoside 3216 4158 7996 7985Dopaminelowast 2679 3068 3771 5194Nobiletin 4473 4401 10957 4114Ursolic acid 3661 3650 11459 3469Tangeretin 2924 3245 7320 3328Nobiletin 5941 4654 11714 2982Lupeol 4226 4203 11503 2397Emodin 3029 3213 8407 2202Physcion 3979 3757 9834 1994lowastControl

10

09

08

07

06

05

04

03

02

01

00

0 20 40 60 80 100 120 140 160

Diso

rder

disp

ositi

on

Residue index

Figure 1 Disorder analysis of sequence of mutant SOD1 from resultof PONDR-FIT prediction The value of disorder disposition above05 in disorder disposition is indicated as disorder residues

are shown in Figure 2 Docking poses of dopamine whichdisplayed H bond with Glu100 the surrounding residuesinclude Lys30 Lys23 Glu21 Pro28 and Gln22 (Figure 3(a))For hesperidin there are three amino acids (Glu21 andGlu100) generated H-bond interaction and the surroundingresidues are Trp32 Pro28 and Lys30 (Figure 3(b)) THSGhas two amino acids generated H-bond for ligand bindingwhich are Glu21 Lys30 and Glu100 and amino acids thatinclude Lys23 Pro28 and Trp32 are near the docked ligand(Figure 3(c)) It is worth to know that Glu100 is the commonresidue for each ligand binding and the Lys30 can be foundin all binding residues of mutant SOD1 In further study weutilized molecular dynamics simulation to analyze variationof each ligand in protein structure

32 Stability Analysis of Molecular Dynamics Simulation Todetermine stability of conformations amongMDsimulations

Table 2 Time of middle structure in each cluster among MDsimulation times

Cluster Time of middle frame (ps)Dopamine Hesperidin THSG

1 460 160 3002 940 360 20803 1520 1480 30604 2180 1880 31405 2220 2040 31806 2500 2140 32807 2740 2200 34608 2820 2920 35409 2960 3240 366010 3140 3380 384011 3380 3580 400012 3300 3740 404013 3560 3800 408014 3780 3840 416015 3900 3900 426016 4020 4180 458017 4280 4620 470018 4740 mdash 4860

we utilized root mean square deviation (RMSD) radius ofprotein gyration and total energy to analyze deviation ofall complexes with docked ligand The RMSD values ofprotein structure were used to verify stability among MDsimulations Figure 4 shows all values within the range from016 to 024 nm which indicate that all protein structuresfrom protein-ligand complexes are stable during 5000 pssimulation and the 5000 ps simulation time is enough fordecreasing the fluctuation of all complexes For ligand RMSD


Dopamine Hesperidin THSG

OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

H2N HO

HO

HO

HO

HO

O

O O

O

OO

O

O

O

Figure 2 Chemical scaffold of dopamine hesperidin and THSG

(a) (b)

(c)

Figure 3 Binding poses of docking result (a) dopamine (b) hesperidin and (c) THSG


032

024

016

008

000

032

024

016

008

000

032

024

016

008

000

0 1000 2000 3000 4000 5000

0 1000 2000 3000 4000 5000

0 1000 2000 3000 4000 5000

Com

plex

RM

SD (n

m)

Com

plex

RM

SD (n

m)

Com

plex

RM

SD (n

m)

Time (ps)

Time (ps)

Time (ps)

Dopamine

Hesperidin

THSG

Figure 4 RMSD values of complex structures with docked liganddopamine hesperidin and THSG among 5000 ps simulation

(Figure 5) it is obvious that the conformation of dopaminedisplayed high degree of difference during dynamic simula-tion and the value of ligand RMAD increased to 024 nmfrom 3000 ps to the end Ligand RMSD of TCM candidatesshows slightly deviation and the values of hesperidin andTHSG are in average of 015 and 010 respectively

33 Migration Analysis of Molecules In order to assess thevariation of each ligand after being docked into proteinbinding siteMSD analysis was used tomeasure themigrationof docked ligand during MD simulation MSD value of hes-peridin is the most distinct from the other TCM candidateswhich displayed a rapid increase during initial simulation tothe end of 5000 ps (Figure 6) In further study we measuredthe distance between centers mass of protein and each ligand

025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

1000 2000 3000 4000 5000

Time (ps)

Dopamine

Hesperidin

THSG

0

1000 2000 3000 4000 5000

Time (ps)0

1000 2000 3000 4000 5000

Time (ps)0

Figure 5 RMSD values of three compounds dopamine hesperidinand THSG in protein complex during 5000 ps simulation time

among all simulation times to understand movement ofdocked compounds Interestingly hesperidin shows a longdistance with 26 nm (Figure 7) and turned to 20 nm after2000 ps Indicating that hesperidin moved away from theinitial binding position and transferred to another site of theprotein structure The results suggest that each ligand couldbind with mutant SOD1 during 5000 ps

34 Flexibility of Residues Analysis We calculated root meansquared fluctuation (RMSF) to analyze the flexibility ofresidues on protein structure and the binding region (from21 to 32 and from 98 to 100 residues) shows no significantincrement on structure of mutant SOD1 with all ligands(Figure 8) From DSSP analysis all helices and beta-sheetsof secondary structure for all complexes continued to exist


10

08

06

04

02

000 1000 2000 3000 4000 5000

Time (ps)

DopamineHesperidinTHSG

MSD

(nmS2N

)

Figure 6 MSD values of three compounds dopamine hesperidinand THSG in protein complex during 5000 ps simulation time

28

26

24

22

20

18

160 1000 2000 3000 4000 5000

Time (ps)

Dist

ance

(nm

)


Figure 7 Distance between the centers ofmass ofmutant SOD1 andligands

during 5000 ps simulation times and the number of residuesis not variable among all conformations (Figure 9) Theresults suggest that the protein structure in each complexremained stable after MD simulations

35 Snapshots Analysis In order to identify the most stablestructure during the entireMD simulation for understanding

050

040

030

020

010

0000 20 40 60 80 100 120 140 160

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

Residues

0 20 40 60 80 100 120 140 160

Residues

0 20 40 60 80 100 120 140 160

Residues

Dopamine

Hesperidin

THSG

Figure 8 RMSF values of protein residues with docked liganddopamine hesperidin and THSG among 5000 ps simulation

the interaction of docked ligands all conformations fromMD simulation were clustered into seventeen or eighteengroups (Figure 10) We pick up the middle conformationfrom final groups of clusters as represented structure andeach middle frame is listed in Table 2 In the next study weanalyze protein-ligand interactions of represented structure(Figure 11) Dopamine has H-bond with Glu100 and Glu21and the pi interaction is formed in Lys23 (Figure 11(a)) Hes-peridin generate two H bonds with Lys23 and Glu21 besidesthere are two pi interactions displayed on Lys30 and Trp32respectively (Figure 11(b)) For THSG binding interaction Hbond was found on Glu24 and pi interaction was formed onLys30 (Figure 11(c)) This result shows that hesperidin andTHSG have similar binding residues to dopamine suggestingthat the binding conformations of two candidates are notsignificantly variable after MD simulations


0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140Re

sidue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(a)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(b)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)

CoilB-SheetB-BridgeBend

TurnA-Helix3-Helix

0 1000 2000 3000 4000 5000Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

StructureBendCoilTurn

B-SheetA-HelixB-Bridge3-Helix

(c)

Figure 9 DSSP analysis of complexes with ligands (a) dopamine (b) hesperidin and (c) THSG The ldquoStructurerdquo is summarized by residuenumber of A-Helix B-Sheet B-Bridge and Turn


1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

1715131197531

ClusterTime (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

(a)

1715131197531

Cluster

18171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

60

55

(b)

1715131197531

Cluster

1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

Fram

e num

ber

130

120

110

100

90

80

70

60

50

40

30

20

10

0

(c)

Figure 10 Clustering analyses of all conformations of mutant SOD1 complexes among 5000 ps simulation times


Lys23

Glu100

Lys30

Gln22Pro28Trp32

Glu21Val29

Pi

+

(a)

PiPi

120590

Lys30 Trp32

Ser98

Glu21

Lys23

(b)

Pi

+Lys23

Pro28

Glu24 Gly27

(c)

Figure 11 Binding poses of each representative conformation of mutant SOD1 from cluster analyses in 2D and 3D diagrams Pi interactions(orange) polar residues (purple circle) and van der Waals residues (green circle) are displaced in 2D diagram


4 Conclusion

For docking analysis both Hesperidin and THSG havehigher Dock Score than dopamine and they displayed stablemovements for mutant SOD1 from MSD and center massdistance analysis which was correlated with the low affinityin docking results For RMSF and DSSP assay the secondarystructure of mutant SOD1 did not change significantly duringMD simulation suggesting that the docked ligands are notaffected by protein structure Hesperidin and THSG havehigh affinity with SOD1 and the binding interactions aresimilar to dopamine among all molecular simulations Ourresult indicated that hesperidin and THSGmight be potentiallead compound to design inhibitors of mutant SOD1 for ALStherapy

Conflict of Interests

The authors reaffirm that they have no conflict of interests todeclare

Authorsrsquo Contribution

Hung-Jin Huang Tung-Ti Chang and Hsin-Yi Chen con-tributed equally to this paper

Acknowledgments

The research was supported by grants from the NationalScience Council of Taiwan (NSC102-2325-B039-001NSC102-2221-E-468-027-) Asia University (ASIA100-CMU-2 ASIA101-CMU-2 102-ASIA-07) China Medical University(CMU99-TC-25) and China Medical University Hospital(DMR-103-058 DMR-103-001 and DMR-103-096) Thisstudy is also supported in part by Taiwan Department ofHealth Clinical Trial and Research Center of Excellence(DOH102-TD-B-111-004) Taiwan Department of HealthCancer Research Center of Excellence (MOHW103-TD-B-111-03) and CMU under the AIM for Top University Plan ofthe Ministry of Education Taiwan

References

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[2] L Wang B Popko and R P Roos ldquoAn enhanced inte-grated stress response ameliorates mutant SOD1-induced ALSrdquoHumanMolecular Genetics vol 23 no 10 pp 2629ndash2638 2014

[3] A A Brasil A Belati S C Mannarino A D Panek E C AEleutherio and M D Pereira ldquoThe involvement of GSH in theactivation of human Sod1 linked to FALS in chronologicallyaged yeast cellsrdquo FEMS Yeast Research vol 13 no 5 pp 433ndash440 2013

[4] D R Rosen T Siddique D Patterson et al ldquoMutations inCuZn superoxide dismutase gene are associated with familialamyotrophic lateral sclerosisrdquoNature vol 362 no 6415 pp 59ndash62 1993

[5] R Tandan and W G Bradley ldquoAmyotrophic lateral sclerosispart I Clinical features pathology and ethical issues in man-agementrdquo Annals of Neurology vol 18 no 3 pp 271ndash280 1985

[6] R Tandan and W G Bradley ldquoAmyotrophic lateral sclerosispart 2 Etiopathogenesisrdquo Annals of Neurology vol 18 no 4 pp419ndash431 1985

[7] K A Staats L van Helleputte A R Jones et al ldquoGeneticablation of phospholipase C delta 1 increases survival inSOD1(G93A) micerdquo Neurobiology of Disease vol 60 pp 11ndash172013

[8] S Shinde N Arora and U Bhadra ldquoA complex network ofmicrornas expressed in brain and genes associated with amy-otrophic lateral sclerosisrdquo International Journal of Genomicsvol 2013 Article ID 383024 12 pages 2013

[9] A Al-Chalabi and O Hardiman ldquoThe epidemiology of ALSa conspiracy of genes environment and timerdquo Nature ReviewsNeurology vol 9 no 11 pp 617ndash628 2013

[10] J S Valentine and P J Hart ldquoMisfolded CuZnSOD and amy-otrophic lateral sclerosisrdquo Proceedings of the National Academyof Sciences of the United States of America vol 100 no 7 pp3617ndash3622 2003

[11] P Ip V K Mulligan and A Chakrabartty ldquoALS-causing SOD1mutations promote production of copper-deficient misfoldedspeciesrdquo Journal of Molecular Biology vol 409 no 5 pp 839ndash852 2011

[12] P Shi A-L Strom J Gal and H Zhu ldquoEffects of ALS-relatedSOD1 mutants on dynein- and KIF5-mediated retrograde andanterograde axonal transportrdquo Biochimica et Biophysica ActaMolecular Basis of Disease vol 1802 no 9 pp 707ndash716 2010

[13] L Zinman H N Liu C Sato et al ldquoA mechanism for lowpenetrance in an ALS family with a novel SOD1 deletionrdquoNeurology vol 72 no 13 pp 1153ndash1159 2009

[14] F L Muller Y Liu A Jernigan D Borchelt A Richardsonand H van Remmen ldquoMnSOD deficiency has a differentialeffect ondisease progression in twodifferentALSmutantmousemodelsrdquoMuscle and Nerve vol 38 no 3 pp 1173ndash1183 2008

[15] Y Yoshii A Otomo L Pan M Ohtsuka and S Hadano ldquoLossof glial fibrillary acidic protein marginally accelerates diseaseprogression in a SOD1H46R transgenic mouse model of ALSrdquoNeuroscience Research vol 70 no 3 pp 321ndash329 2011

[16] D D Winkler J P Schuermann X Cao et al ldquoStructuraland biophysical properties of the pathogenic SOD1 variantH46RH48QrdquoBiochemistry vol 48 no 15 pp 3436ndash3447 2009

[17] Y Kokubo S Kuzuhara Y Narita et al ldquoAccumulation of neu-rofilaments and SOD1-immunoreactive products in a patientwith familial amyotrophic lateral sclerosis with I113T SOD1mutationrdquo Archives of Neurology vol 56 no 12 pp 1506ndash15081999

[18] P G Ince P J Shaw J Y Slade C Jones and P HudgsonldquoFamilial amyotrophic lateral sclerosis with a mutation in exon4 of the CuZn superoxide dismutase gene pathological andimmunocytochemical changesrdquo Acta Neuropathologica vol 92no 4 pp 395ndash403 1996

[19] Y Honjo S Kaneko H Ito et al ldquoProtein disulfide isomerase-immunopositive inclusions in patients with amyotrophic lateralsclerosisrdquo Amyotrophic Lateral Sclerosis vol 12 no 6 pp 444ndash450 2011

[20] M E Cudkowicz D McKenna-Yasek P E Sapp et alldquoEpidemiology of mutations in superoxide dismutase in amy-otrophic lateral sclerosisrdquoAnnals of Neurology vol 41 no 2 pp210ndash221 1997


[21] M Aoki M Ogasawara Y Matsubara et al ldquoFamilialamytrophic lateral sclerosis (ALS) in Japan associated withH46Rmutation inCuZn superoxide dismutase gene a possiblenew subtype of familial ALSrdquo Journal of the NeurologicalSciences vol 126 no 1 pp 77ndash83 1994

[22] S Nakamura R Wate S Kaneko et al ldquoAn autopsy case ofsporadic amyotrophic lateral sclerosis associated with the I113TSOD1 mutationrdquoNeuropathology vol 34 no 1 pp 58ndash63 2014

[23] G Lopate R H Baloh M T Al-Lozi et al ldquoFamilial ALSwith extreme phenotypic variability due to the I113T SOD1mutationrdquoAmyotrophic Lateral Sclerosis vol 11 no 1-2 pp 232ndash236 2010

[24] T Schmidlin B K Kennedy and V Daggett ldquoStructuralchanges to monomeric CuZn superoxide dismutase caused bythe familial amyotrophic lateral sclerosis-associated mutationA4Vrdquo Biophysical Journal vol 97 no 6 pp 1709ndash1718 2009

[25] L I Bruijn M K Houseweart S Kato et al ldquoAggregationand motor neuron toxicity of an ALS-linked SOD1 mutantindependent from wild-type SOD1rdquo Science vol 281 no 5384pp 1851ndash1854 1998

[26] J R Auclair K J Boggio G A Petsko D Ringe and J NAgar ldquoStrategies for stabilizing superoxide dismutase (SOD1)the protein destabilized in the most common form of famil-ial amyotrophic lateral sclerosisrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 107 no50 pp 21394ndash21399 2010

[27] S-S Chang H-J Huang and C Y-C Chen ldquoHigh perfor-mance screening structural andmolecular dynamics analysis toidentify H1 inhibitors fromTCMDatabaseTaiwanrdquoMolecularBioSystems vol 7 no 12 pp 3366ndash3374 2011

[28] C-Y Chen Y-H Chang D-T Bau et al ldquoLigand-based dualtarget drug design for H1N1 swine flumdasha preliminary firststudyrdquo Journal of Biomolecular Structure and Dynamics vol 27no 2 pp 171ndash178 2009

[29] T-T Chang M-F Sun H-Y Chen et al ldquoScreening from theworldrsquos largest TCM database against H1N1 virusrdquo Journal ofBiomolecular Structure and Dynamics vol 28 no 5 pp 773ndash786 2011

[30] H-J Huang HW Yu C-Y Chen et al ldquoCurrent developmentsof computer-aided drug designrdquo Journal of the Taiwan Instituteof Chemical Engineers vol 41 no 6 pp 623ndash635 2010

[31] I C Chou W-D Lin C-H Wang et al ldquoMobius syndrome ina male with XXXY mosaicismrdquo BioMedicine vol 3 no 2 pp102ndash104 2013

[32] I C Chou W-D Lin C-H Wang et al ldquoAssociation analysisbetween Tourettersquos syndrome and two dopamine genes (DAT1DBH) in Taiwanese childrenrdquo BioMedicine vol 3 no 2 pp 88ndash91 2013

[33] C-C Lee C-H Tsai L Wan et al ldquoIncreased incidence ofParkinsonism among Chinese with 120573-glucosidase mutation incentral Taiwanrdquo BioMedicine vol 3 no 2 pp 92ndash94 2013

[34] C-H Wang W-D Lin D-T Bau et al ldquoAppearance ofacanthosis nigricansmay precede obesity an involvement of theinsulinIGF receptor signaling pathwayrdquoBioMedicine vol 3 no2 pp 82ndash87 2013

[35] C-H Wang W-D Lin and F-J Tsai ldquoCraniofacial dysmor-phism what is your diagnosisrdquo BioMedicine vol 2 no 2 pp49ndash50 2012

[36] W-Y LinH-P Liu J-S Chang et al ldquoGenetic variationswithinthe PSORS1 region affect Kawasaki disease development andcoronary artery aneurysm formationrdquo BioMedicine vol 3 no2 pp 73ndash81 2013

[37] C Y-C Chen ldquoWeighted equation and rulesmdasha novel conceptfor evaluating protein-ligand interactionrdquo Journal of Biomolec-ular Structure and Dynamics vol 27 no 3 pp 271ndash282 2009

[38] T-Y Tsai K-W Chang and C Y-C Chen ldquoIScreen worldrsquosfirst cloud-computing web server for virtual screening and denovo drug design based on TCM databaseTaiwanrdquo Journal ofComputer-Aided Molecular Design vol 25 no 6 pp 525ndash5312011

[39] H-J Huang Y-R Jian and C-Y Chen ldquoTraditional Chinesemedicine application in HIV an in silico studyrdquo Journal ofBiomolecular Structure and Dynamics vol 32 no 1 pp 1ndash122014

[40] S-S Chang H-J Huang and C Y-C Chen ldquoTwo birdswith one stone Possible dual-targeting H1N1 inhibitors fromtraditional Chinese medicinerdquo PLoS Computational Biologyvol 7 no 12 Article ID e1002315 2011

[41] K-C Chen M-F Sun S-C Yang et al ldquoInvestigationinto potent inflammation inhibitors from traditional Chinesemedicinerdquo Chemical Biology and Drug Design vol 78 no 4 pp679ndash688 2011

[42] S-C Yang S-S Chang H-Y Chen and C Y-C ChenldquoIdentification of potent EGFR inhibitors from TCMDatabaseTaiwanrdquo PLoS Computational Biology vol 7no 10 Article ID e1002189 2011

[43] S-C Yang S-S Chang and C Y-C Chen ldquoIdentifying HER2inhibitors from natural products databaserdquo PLoS ONE vol 6no 12 Article ID e28793 2011

[44] Y A Tsou K C Chen H C Lin S S Chang and C Y ChenldquoUroporphyrinogen decarboxylase as a potential target forspecific components of traditional Chinese medicine a virtualscreening andmolecular dynamics studyrdquo PLoS ONE vol 7 no11 Article ID e50087 2012

[45] C-Y Chen and C Y-C Chen ldquoInsights into designing thedual-targeted HER2HSP90 inhibitorsrdquo Journal of MolecularGraphics and Modelling vol 29 no 1 pp 21ndash31 2010

[46] C Y-C Chen Y-F Chen C-H Wu and H-Y Tsai ldquoWhatis the effective component in suanzaoren decoction for cur-ing insomnia Discovery by virtual screening and moleculardynamic simulationrdquo Journal of Biomolecular Structure andDynamics vol 26 no 1 pp 57ndash64 2008

[47] K C Chen S S Chang H J Huang et al ldquoThree-in-oneagonists for PPAR-alpha PPAR-gamma and PPAR-delta fromtraditional Chinesemedicinerdquo Journal of Biomolecular Structureand Dynamics vol 30 no 6 pp 662ndash683 2012

[48] C Y-C Chen ldquoVirtual screening and drug design for PDE-5receptor from traditional Chinese medicine databaserdquo Journalof Biomolecular Structure and Dynamics vol 27 no 5 pp 627ndash640 2010

[49] C-Y Chen Y-H Chang D-T Bau et al ldquoDiscovery of potentinhibitors for phosphodiesterase 5 by virtual screening andpharmacophore analysisrdquo Acta Pharmacologica Sinica vol 30no 8 pp 1186ndash1194 2009

[50] C Y-C Chen ldquoComputational screening and design of Tradi-tional ChineseMedicine (TCM) to block phosphodiesterase-5rdquoJournal of Molecular Graphics and Modelling vol 28 no 3 pp261ndash269 2009

[51] K-C Chen K-W Chang H-Y Chen and C Y-C ChenldquoTraditional Chinese medicine a solution for reducing dualstroke risk factors at oncerdquo Molecular BioSystems vol 7 no 9pp 2711ndash2719 2011

[52] H Y Chen S S Chang Y C Chan and C Y Chen ldquoDiscoveryof novel insomnia leads from screening traditional Chinese


medicine databaserdquo Journal of Biomolecular Structure andDynamics vol 32 no 5 pp 776ndash791 2014

[53] C Y Chen ldquoMechanism of BAG1 repair on Parkinsonrsquos disease-linked DJ1 mutationrdquo Journal of Biomolecular Structure andDynamics vol 30 no 1 pp 1ndash12 2012

[54] Y-M Chang B K Velmurugan W-W Kuo et al ldquoInhibitoryeffect of alpinate Oxyphyllae fructus extracts on Ang II-induced cardiac pathological remodeling-related pathways inH9c2 cardiomyoblast cellsrdquo BioMedicine vol 3 no 4 pp 148ndash152 2013

[55] P-C Lin P-Y Liu S-Z Lin and H-J Harn ldquoAngelica sinensisa Chinese herb for brain cancer therapyrdquoBioMedicine vol 2 no1 pp 30ndash35 2012

[56] T-Y Ho H-Y Lo C-C Li J-C Chen and C-Y HsiangldquoIn vitro and in vivo bioluminescent imaging to evaluate anti-Escherichia coli activity of Galla Chinensisrdquo BioMedicine vol 3no 4 pp 160ndash166 2013

[57] C-C Li H-Y Lo C-YHsiang andT-YHo ldquoDNAmicroarrayanalysis as a tool to investigate the therapeutic mechanisms anddrug development of Chinese medicinal herbsrdquo BioMedicinevol 2 no 1 pp 10ndash16 2012

[58] S-C Hsu and J-G Chung ldquoAnticancer potential of emodinrdquoBioMedicine vol 2 no 3 pp 108ndash116 2012

[59] C Y-C Chen ldquoTCM DatabaseTaiwan the worldrsquos largesttraditional Chinese medicine database for drug screening insilicordquo PLoS ONE vol 6 no 1 Article ID e15939 2011

[60] G SWright S V Antonyuk NM Kershaw RW Strange andS S Hasnain ldquoLigand binding and aggregation of pathogenicSOD1rdquo Nature Communications vol 4 article 1758 2013

[61] Accelerys Discovery Studio Client V25 Accelrys San DiegoCalif USA 2009

[62] B Xue R L Dunbrack R W Williams A K Dunker andV N Uversky ldquoPONDR-FIT a meta-predictor of intrinsicallydisordered amino acidsrdquoBiochimica et Biophysica Acta Proteinsand Proteomics vol 1804 no 4 pp 996ndash1010 2010

[63] Y Zhao C P Kao Y S Chang andY LHo ldquoQuality assessmenton Polygoni Multiflori Caulis using HPLCUVMS combinedwith principle component analysisrdquo Chemistry Central Journalvol 7 no 1 article 106 2013

[64] N L Allinger ldquoConformational analysis 130 MM2 A hydro-carbon force field utilizing V1 and V2 torsional termsrdquo Journalof the American Chemical Society vol 99 no 25 pp 8127ndash81341977

[65] C M Venkatachalam X Jiang T Oldfield and M Wald-man ldquoLigandFit a novel method for the shape-directed rapiddocking of ligands to protein active sitesrdquo Journal of MolecularGraphics and Modelling vol 21 no 4 pp 289ndash307 2003

[66] B R Brooks R E Bruccoleri B D Olafson D J States SSwaminathan and M Karplus ldquoCHARMM a program formacromolecular energy minimization and dynamics calcula-tionsrdquo Journal of Computational Chemistry vol 4 no 2 pp 187ndash217 1983

[67] R Fletcher and C M Reeves ldquoFunction minimization byconjugate gradientsrdquo The Computer Journal vol 7 no 2 pp149ndash154 1964

[68] WH Press B P Flannery S A TeukolskyW T Vetterling andH Gould ldquoNumerical recipes the art of scientific computingrdquoAmerican Journal of Physics vol 55 no 1 pp 90ndash91 1987

[69] S Pronk S Pall R Schulz et al ldquoGROMACS 45 a high-throughput and highly parallel open source molecular simula-tion toolkitrdquo Bioinformatics vol 29 no 7 pp 845ndash854 2013

[70] W I Tou S S Chang C C Lee and C Y-C Chen ldquoDrugdesign for neuropathic pain regulation from traditional Chinesemedicinerdquo Scientific Reports vol 3 article 844 2013

[71] W I Tou andC Y Chen ldquoMay disordered protein cause seriousdrug side effectrdquo Drug Discovery Today vol 19 no 4 pp 367ndash372 2014


the aggregation [24 25] hence designing novel drugs forinhibition of SOD1 aggregation and stabilization have beenused in ALS treatments [26]

The aim of this study is to focus on drug design ofmutant SOD1 The complex of mutant SOD1 and dopaminewas used to investigate the novel inhibitors of mutant SOD1for inhibiting aggregation Computer-aided drug design(CADD) is rapid approach for drug discovery [27ndash30] whichis based on risk factor studies [31ndash36] theory [37] and webserver [38] for developing novel leading compounds CADDhas been wildly used to design new drugs in many casessuch as virus [39 40] inflammation [41] cancer [42ndash45]insomnia [46] weight loss [47] erectile dysfunction [48ndash50] nerve system [51ndash53] and diabetes [54] TraditionalChinese medicine (TCM) has been used over two thousandyears in clinical therapy and many studies utilized TCMto investigate new therapies [55ndash58] In our study smallmolecules from the world TCM database [59] was used toscreen for searching potential compounds with high affinityin mutant SOD1 active site and we further utilizedmoleculardynamics (MD) simulation to verify the stability betweenprotein and ligands for binding assay Synthetic drug oftenhas side effect in clinical treatments and our results providednature product as drug candidate which is safer and reduceadverse reactions

2 Materials and Methods

21 Database Screening The crystal structure of mutantSOD1 was obtained from PDB database (PDB code 4A7V)[60] We employed Prepare Protein module of AccelrysDiscovery Studio 2559350 (DS 25) software [61] to cleanup mistakes of each residue on mutant SOD structure suchas deleting alternate conformations modeling missing loopsand removing water molecules This module also predictstitration site pKs for each amino acid and the pH value of74 was used to protonate all residues PONDR-FIT [62] wasused to predict the orderdisorder in mutant SOD1 structureThe 61000 TCM compounds were downloaded from theTCM DatabaseTaiwan [59] for database screening we alsoemployed TCM compounds from Changrsquo lab for bindingassay [63] and MM2 force field [64] of ChemBioOffice 2010software was carried out to optimize and calculate the 3Dconformation of TCM compounds All compounds generatedifferent conformations by Monte-Carlo techniques underLigandFit module [65] of DS 25 which were docked intomutations SOD1 binding site for protein-ligand interactionanalysis Minimization of all docking poses was based onCHARMm force field [66] and we used Smart minimizeralgorithm as minimization algorithm for ligands minimiza-tion [67 68] which contains steepest descent and conjugategradient The steepest descent performed 1000 steps andfollowed by conjugate gradient minimization

22 Molecular Dynamics (MD) Simulation Protein-ligandstructures were obtained from results of docking studyand the starting conformation of protein-ligand complex

was performed using GROMACS 455 package [69] formolecular dynamic simulation using charmm27 force fieldThe protein structure was placed in cubic box containingTIP3P water molecules The distance between protein andbox was set to 12 nm and the van der Waals cutoff to 14 nmParticle mesh Ewald (PME) method is regard as coulombtype for calculating electrostatic interaction and LINCSalgorithmwas used to restrain the lengths of all bonds amongall simulations For obtaining topology file and parametersof small compounds we employed SwissParam to generatethese data and compatible with the CHARMMall atoms forcefield for GROMACS simulation In system neutralizationwe added Na and Cl ions to randomly replace solventmolecules in simulation systems and the concentration ofNaCl model was set as 0145M The time step was set to0002 ps for MD simulation Steepest descent algorithm wasapplied to energy minimization for 5000 cycle steps Thefollowing procedure is equilibration which was performedunder position restraints for 100 ps to relax solvent in pro-tein structure under constant temperature dynamics (NVT)condition Production simulations perform 5000 ps at finalstep for all simulation systems under constant pressure andtemperature (NPT) dynamics Temperature of all simulationsystems was set to 310 K All MD frames were saved every20 ps for trajectory analysis

23 Analysis of MD Simulation Trajectory analysis of MDconformations was calculated by GROMACS 455 [69]including root mean square deviation (RMSD) root meansquare fluctuation (RMSF) and mean square displacement(MSD) The secondary structures analysis was performed byDSSP program under GROMACS 455 Linkage clusteringalgorithm was used to identify the most populated structuralrepresentations of conformation duringMD simulationsTheRMSD cutoff for cluster analysis was set as 013

3 Results and Discussion

31 Docking Results of Database Screening To analysis dis-order region we employed PONDR-FIT [62] to predict theorderdisorder inmutant SOD1 structureThe sequence num-ber from 21 to 32 and from98 to 100 are binding site ofmutantSOD1 (Figure 1) The disorder disposition values among thisrange are below 05 which indicates that the binding site isfolded orderly and that the protein structure may not affectligand binding [70 71] For docking analysis we based on -PLP1 -PLP2 -PMF and Dock Score to evaluate the dockingpose of traditional Chinese medicine (TCM) compoundsFrom scoring analysis dopamine was regarded as control forcomparing with TCM compounds The score values fromdocking poses of TCM compounds are shown in Table 1 Alldocked ligands are ranked by Dock Score and we foundthat hesperidin and 23541015840-tetrahydroxystilbene-2-O-120573-D-glucoside (THSG) [63] with Dock Score (including scorevalues of -PLP1 -PLP2 and -PMF) are higher than dopamineWe selected hesperidin THSG and dopamine for furtherstudies and chemical scaffolds of these small molecular




10

09

08

07

06

05

04

03

02

01

00

0 20 40 60 80 100 120 140 160

Diso

rder

disp

ositi

on

Residue index






1 460 160 3002 940 360 20803 1520 1480 30604 2180 1880 31405 2220 2040 31806 2500 2140 32807 2740 2200 34608 2820 2920 35409 2960 3240 366010 3140 3380 384011 3380 3580 400012 3300 3740 404013 3560 3800 408014 3780 3840 416015 3900 3900 426016 4020 4180 458017 4280 4620 470018 4740 mdash 4860




OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

H2N HO

HO

HO

HO

HO

O

O O

O

OO

O

O

O


(a) (b)

(c)



032

024

016

008

000

032

024

016

008

000

032

024

016

008

000

0 1000 2000 3000 4000 5000

0 1000 2000 3000 4000 5000

0 1000 2000 3000 4000 5000

Com

plex

RM

SD (n

m)

Com

plex

RM

SD (n

m)

Com

plex

RM

SD (n

m)

Time (ps)

Time (ps)

Time (ps)

Dopamine

Hesperidin

THSG




025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

1000 2000 3000 4000 5000

Time (ps)

Dopamine

Hesperidin

THSG

0

1000 2000 3000 4000 5000

Time (ps)0

1000 2000 3000 4000 5000

Time (ps)0





10

08

06

04

02

000 1000 2000 3000 4000 5000

Time (ps)


MSD

(nmS2N

)


28

26

24

22

20

18

160 1000 2000 3000 4000 5000

Time (ps)

Dist

ance

(nm

)





050

040

030

020

010

0000 20 40 60 80 100 120 140 160

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

Residues

0 20 40 60 80 100 120 140 160

Residues

0 20 40 60 80 100 120 140 160

Residues

Dopamine

Hesperidin

THSG




0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140Re

sidue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(a)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(b)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)


TurnA-Helix3-Helix

0 1000 2000 3000 4000 5000Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s



(c)



1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

1715131197531

ClusterTime (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

(a)

1715131197531

Cluster

18171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

60

55

(b)

1715131197531

Cluster

1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

Fram

e num

ber

130

120

110

100

90

80

70

60

50

40

30

20

10

0

(c)



Lys23

Glu100

Lys30

Gln22Pro28Trp32

Glu21Val29

Pi

+

(a)

PiPi

120590

Lys30 Trp32

Ser98

Glu21

Lys23

(b)

Pi

+Lys23

Pro28

Glu24 Gly27

(c)



4 Conclusion






Acknowledgments


References














































































10

09

08

07

06

05

04

03

02

01

00

0 20 40 60 80 100 120 140 160

Diso

rder

disp

ositi

on

Residue index






1 460 160 3002 940 360 20803 1520 1480 30604 2180 1880 31405 2220 2040 31806 2500 2140 32807 2740 2200 34608 2820 2920 35409 2960 3240 366010 3140 3380 384011 3380 3580 400012 3300 3740 404013 3560 3800 408014 3780 3840 416015 3900 3900 426016 4020 4180 458017 4280 4620 470018 4740 mdash 4860




OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

H2N HO

HO

HO

HO

HO

O

O O

O

OO

O

O

O


(a) (b)

(c)



032

024

016

008

000

032

024

016

008

000

032

024

016

008

000

0 1000 2000 3000 4000 5000

0 1000 2000 3000 4000 5000

0 1000 2000 3000 4000 5000

Com

plex

RM

SD (n

m)

Com

plex

RM

SD (n

m)

Com

plex

RM

SD (n

m)

Time (ps)

Time (ps)

Time (ps)

Dopamine

Hesperidin

THSG




025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

1000 2000 3000 4000 5000

Time (ps)

Dopamine

Hesperidin

THSG

0

1000 2000 3000 4000 5000

Time (ps)0

1000 2000 3000 4000 5000

Time (ps)0





10

08

06

04

02

000 1000 2000 3000 4000 5000

Time (ps)


MSD

(nmS2N

)


28

26

24

22

20

18

160 1000 2000 3000 4000 5000

Time (ps)

Dist

ance

(nm

)





050

040

030

020

010

0000 20 40 60 80 100 120 140 160

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

Residues

0 20 40 60 80 100 120 140 160

Residues

0 20 40 60 80 100 120 140 160

Residues

Dopamine

Hesperidin

THSG




0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140Re

sidue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(a)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(b)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)


TurnA-Helix3-Helix

0 1000 2000 3000 4000 5000Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s



(c)



1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

1715131197531

ClusterTime (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

(a)

1715131197531

Cluster

18171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

60

55

(b)

1715131197531

Cluster

1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

Fram

e num

ber

130

120

110

100

90

80

70

60

50

40

30

20

10

0

(c)



Lys23

Glu100

Lys30

Gln22Pro28Trp32

Glu21Val29

Pi

+

(a)

PiPi

120590

Lys30 Trp32

Ser98

Glu21

Lys23

(b)

Pi

+Lys23

Pro28

Glu24 Gly27

(c)



4 Conclusion






Acknowledgments


References













































































OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

OH

H2N HO

HO

HO

HO

HO

O

O O

O

OO

O

O

O


(a) (b)

(c)



032

024

016

008

000

032

024

016

008

000

032

024

016

008

000

0 1000 2000 3000 4000 5000

0 1000 2000 3000 4000 5000

0 1000 2000 3000 4000 5000

Com

plex

RM

SD (n

m)

Com

plex

RM

SD (n

m)

Com

plex

RM

SD (n

m)

Time (ps)

Time (ps)

Time (ps)

Dopamine

Hesperidin

THSG




025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

1000 2000 3000 4000 5000

Time (ps)

Dopamine

Hesperidin

THSG

0

1000 2000 3000 4000 5000

Time (ps)0

1000 2000 3000 4000 5000

Time (ps)0





10

08

06

04

02

000 1000 2000 3000 4000 5000

Time (ps)


MSD

(nmS2N

)


28

26

24

22

20

18

160 1000 2000 3000 4000 5000

Time (ps)

Dist

ance

(nm

)





050

040

030

020

010

0000 20 40 60 80 100 120 140 160

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

Residues

0 20 40 60 80 100 120 140 160

Residues

0 20 40 60 80 100 120 140 160

Residues

Dopamine

Hesperidin

THSG




0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140Re

sidue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(a)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(b)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)


TurnA-Helix3-Helix

0 1000 2000 3000 4000 5000Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s



(c)



1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

1715131197531

ClusterTime (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

(a)

1715131197531

Cluster

18171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

60

55

(b)

1715131197531

Cluster

1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

Fram

e num

ber

130

120

110

100

90

80

70

60

50

40

30

20

10

0

(c)



Lys23

Glu100

Lys30

Gln22Pro28Trp32

Glu21Val29

Pi

+

(a)

PiPi

120590

Lys30 Trp32

Ser98

Glu21

Lys23

(b)

Pi

+Lys23

Pro28

Glu24 Gly27

(c)



4 Conclusion






Acknowledgments


References












































































032

024

016

008

000

032

024

016

008

000

032

024

016

008

000

0 1000 2000 3000 4000 5000

0 1000 2000 3000 4000 5000

0 1000 2000 3000 4000 5000

Com

plex

RM

SD (n

m)

Com

plex

RM

SD (n

m)

Com

plex

RM

SD (n

m)

Time (ps)

Time (ps)

Time (ps)

Dopamine

Hesperidin

THSG




025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

025

020

015

010

005

000

Liga

nd R

MSD

(nm

)

1000 2000 3000 4000 5000

Time (ps)

Dopamine

Hesperidin

THSG

0

1000 2000 3000 4000 5000

Time (ps)0

1000 2000 3000 4000 5000

Time (ps)0





10

08

06

04

02

000 1000 2000 3000 4000 5000

Time (ps)


MSD

(nmS2N

)


28

26

24

22

20

18

160 1000 2000 3000 4000 5000

Time (ps)

Dist

ance

(nm

)





050

040

030

020

010

0000 20 40 60 80 100 120 140 160

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

Residues

0 20 40 60 80 100 120 140 160

Residues

0 20 40 60 80 100 120 140 160

Residues

Dopamine

Hesperidin

THSG




0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140Re

sidue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(a)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(b)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)


TurnA-Helix3-Helix

0 1000 2000 3000 4000 5000Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s



(c)



1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

1715131197531

ClusterTime (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

(a)

1715131197531

Cluster

18171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

60

55

(b)

1715131197531

Cluster

1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

Fram

e num

ber

130

120

110

100

90

80

70

60

50

40

30

20

10

0

(c)



Lys23

Glu100

Lys30

Gln22Pro28Trp32

Glu21Val29

Pi

+

(a)

PiPi

120590

Lys30 Trp32

Ser98

Glu21

Lys23

(b)

Pi

+Lys23

Pro28

Glu24 Gly27

(c)



4 Conclusion






Acknowledgments


References












































































10

08

06

04

02

000 1000 2000 3000 4000 5000

Time (ps)


MSD

(nmS2N

)


28

26

24

22

20

18

160 1000 2000 3000 4000 5000

Time (ps)

Dist

ance

(nm

)





050

040

030

020

010

0000 20 40 60 80 100 120 140 160

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

050

040

030

020

010

000

RMSF

(nm

)

Residues

0 20 40 60 80 100 120 140 160

Residues

0 20 40 60 80 100 120 140 160

Residues

Dopamine

Hesperidin

THSG




0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140Re

sidue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(a)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(b)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)


TurnA-Helix3-Helix

0 1000 2000 3000 4000 5000Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s



(c)



1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

1715131197531

ClusterTime (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

(a)

1715131197531

Cluster

18171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

60

55

(b)

1715131197531

Cluster

1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

Fram

e num

ber

130

120

110

100

90

80

70

60

50

40

30

20

10

0

(c)



Lys23

Glu100

Lys30

Gln22Pro28Trp32

Glu21Val29

Pi

+

(a)

PiPi

120590

Lys30 Trp32

Ser98

Glu21

Lys23

(b)

Pi

+Lys23

Pro28

Glu24 Gly27

(c)



4 Conclusion






Acknowledgments


References












































































0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140Re

sidue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(a)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)0 1000 2000 3000 4000 5000

Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s

(b)

0 1000 2000 3000 4000 5000

20

40

60

80

100

120

140

Resid

ue

Time (ps)


TurnA-Helix3-Helix

0 1000 2000 3000 4000 5000Time (ps)

100

80

60

40

20

0

Num

ber o

f res

idue

s



(c)



1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

1715131197531

ClusterTime (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

(a)

1715131197531

Cluster

18171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

60

55

(b)

1715131197531

Cluster

1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

Fram

e num

ber

130

120

110

100

90

80

70

60

50

40

30

20

10

0

(c)



Lys23

Glu100

Lys30

Gln22Pro28Trp32

Glu21Val29

Pi

+

(a)

PiPi

120590

Lys30 Trp32

Ser98

Glu21

Lys23

(b)

Pi

+Lys23

Pro28

Glu24 Gly27

(c)



4 Conclusion






Acknowledgments


References












































































1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

1715131197531

ClusterTime (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

(a)

1715131197531

Cluster

18171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

50

45

40

35

30

25

20

15

10

5

0

Fram

e num

ber

60

55

(b)

1715131197531

Cluster

1918171615141312111098765432100 1000 2000 3000 4000 5000

Clus

ter

Time (ps)

Fram

e num

ber

130

120

110

100

90

80

70

60

50

40

30

20

10

0

(c)



Lys23

Glu100

Lys30

Gln22Pro28Trp32

Glu21Val29

Pi

+

(a)

PiPi

120590

Lys30 Trp32

Ser98

Glu21

Lys23

(b)

Pi

+Lys23

Pro28

Glu24 Gly27

(c)



4 Conclusion






Acknowledgments


References












































































Lys23

Glu100

Lys30

Gln22Pro28Trp32

Glu21Val29

Pi

+

(a)

PiPi

120590

Lys30 Trp32

Ser98

Glu21

Lys23

(b)

Pi

+Lys23

Pro28

Glu24 Gly27

(c)



4 Conclusion






Acknowledgments


References












































































4 Conclusion






Acknowledgments


References






















































































































































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