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International Journal of Biological Macromolecules 108 (2018)
214–224
Contents lists available at ScienceDirect
International Journal of Biological Macromolecules
j ourna l ho me pa g e: www.elsev ier .com/ locate / i jb
iomac
Structure based mimicking of Phthalic acid esters (PAEs)
andinhibition of hACMSD, an important enzyme of the
tryptophankynurenine metabolism pathway
Neha Singh, Vikram Dalal, Pravindra Kumar ∗
Department of Biotechnology, Indian Institute of Technology
Roorkee, 247667, India
a r t i c l e i n f o
Article history:Received 23 October 2017Received in revised form
1 December 2017Accepted 3 December 2017Available online 5 December
2017
Keywords:hACMSDPhthalatesMolecular docking and simulation
a b s t r a c t
Human �-amino-�-carboxymuconate-�-semialdehyde decarboxylase
(hACMSD) is a zinc containingamidohydrolase which is a vital enzyme
of the kynurenine pathway in tryptophan metabolism. It pre-vents
the accumulation of quinolinic acid (QA) and helps in the
maintenance of basal Trp-niacin ratio.To assess the structure based
inhibitory action of PAEs such as DMP, DEP, DBP, DIBP, DEHP and
theirmetabolites, these were docked into the active site cavity of
hACMSD. Docking results show that thebinding affinities of PAEs lie
in the comparable range (−4.9 kca/mol–7.48 kcal/mol) with
Dipicolinic acid(−6.21 kcal/mol), a substrate analogue of hACMSD.
PAEs interact with the key residues such as Arg47and Trp191 and lie
within the 4 Å vicinity of zinc metal at the active site of hACMSD.
Dynamics and sta-bility of the PAEs-hACMSD complexes were
determined by performing molecular dynamics simulationsusing
GROMACS 5.14. Binding free energy calculations of the PAEs-hACMSD
complexes were estimatedby using MMPBSA method. The results
emphasize that PAEs can structurally mimic the binding patternof
tryptophan metabolites to hACMSD, which further leads to inhibition
of its activity and accumulationof the quinolate in the kynurenine
pathway of tryptophan metabolism.
© 2017 Elsevier B.V. All rights reserved.
1. Introduction
In the brain, elevated levels of quinolinic acid (QA) are
oftenassociated with the pathogenesis of different
neurodegenera-tive disorders including Alzheimer’s and Huntington’s
disease[1–3]. In kynurenine pathway of tryptophan metabolism,
�-amino-�-carboxymuconate-�-semialdehyde (ACMS) is metabolized
to�-amino-�-muconate-�-semialdehyde (AMS) via action of impor-tant
enzyme ACMS decarboxylase, further AMS converted to acetylCoA as
shown in Fig. 1 [4,5]. To maintain the basal Trp-niacin ratio,ACMS
is non-enzymatically metabolized to quinolate (QA) whichfurther
leads to the NAD formation [6]. Thus, the presence of keyenzyme
ACMSD prevents the accumulation of quinolate [7]. Vari-ous studies
related to ACMSD show that enzyme is zinc-dependentamidohydrolase
maintaining quinolinic acid (QA) and NAD homeo-stasis [8].
Disturbance in the basal levels of QA is associated withmany
physiological and pathological conditions related to the cen-tral
nervous system (CNS) [9]. Thus, ACMSD act as a checkpoint
andregulates the balance between the relative QA levels.
∗ Corresponding author.E-mail address: [email protected] (P.
Kumar).
Several studies have reported that ACMSD is an critical
enzymefor tryptophan metabolism [10,11]. Phthalic acid esters
(PAEs) arecommonly used for the industrial manufacturing of
lubricants, var-ious adhesives, pest repellents, and plastics
[12,13]. Several PAEsare long-established environmental endocrine
disrupters, perox-isome proliferators and induce reproductive and
developmentaltoxicities [14–18]. It has been reported that PAEs can
imbalancethe Trp-niacin basal ratio in the tryptophan metabolism
pathway.In rats, it has been reported that the conversion ratio of
tryptophanto niacin has increased with increase in the dietary
concentration ofdi-(2-ethylhexyl) phthalate (DEHP) [19]. This study
shows that theincrease in the amount of quinolinic acid with an
increase in DEHPconcentration is associated with the inhibition of
enzymatic activ-ity of ACMSD. Similarly, di-n-butyl phthalate
(DnBP) is reported tobe linked with alteration in trp to niacin
ratio in the weaning rats,which were fed with niacin-free and
tryptophan limited diet [20].
It has been shown that DEHP degrades to Phthalic acid viaan
intermediate mono-(2-ethylhexyl) phthalate (MEHP) [21]. Inanother
report, it has been shown that DEHP and its metaboliteMEHP
increased QA production in the rats [22]. This study revealsthat
the structural similarity of DEHP and MEHP to tryptophanmetabolites
is responsible for the noticeable changes in normal
https://doi.org/10.1016/j.ijbiomac.2017.12.0050141-8130/© 2017
Elsevier B.V. All rights reserved.
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N. Singh et al. / International Journal of Biological
Macromolecules 108 (2018) 214–224 215
Fig. 1. Kynurenine pathway of tryptophan metabolism. Tryptophan
is metabolized to kynurenine and further to 3-Hydroxy anthranilic
acid (3-HAA). Then, ACMSD participatesin a reaction (marked with a
solid arrow) and directs the conversion of
�-amino-�-carboxymuconate-�-semialdehyde (ACMS) to
�-amino-�-muconate-�-semialdehyde(AMS), which is further
metabolized to Acetyl-CoA. ACMS is also non-enzymatically converted
to quinolate (marked with a dotted arrow), which further leads to
nicotinamideadenine dinucleotide (NAD) biosynthesis.
tryptophan metabolism and caused the inhibition of ACMSD
activ-ity.
Although, numerous studies have shown that phthalatesare
involved in disturbing the basal trp to niacin ratio, butthe
elucidation of the binding mode and important inter-actions of PAEs
with hACMSD have not been reported yet.This study highlights the
important interactions of phtha-lates with human
�-amino-�-carboxymuconate-�-semialdehydedecarboxylase (hACMSD)
which eventually inhibit the hACMSDactivity and leads to the
accumulation of quinolate. The crystalstructure of the hACMSD along
with substrate analogue Dipicol-inic acid, PDC (PDB ID: 4IH3) is
available [8]. PDC binds in the zinccontaining active site of
hACMSD and shows the interaction withArg47 and Trp191. In this
study, five commonly used PAEs andtheir corresponding
monophthalates used for the docking stud-ies with hACMSD are:
dimethyl phthalate (DMP), diethyl phthalate(DEP), di-n-butyl
phthalate (DnBP), di-isobutyl phthalate (DIBP), di-(2-ethylhexyl)
phthalate (DEHP), monomethyl phthalate (MMP),monoethyl phthalate
(MEP), mono-n-butyl phthalate (MBP),mono-iso butyl phthalate
(MIBP), and mono-(2-ethylhexyl) phtha-late (MEHP),
mono-(2-ethyl-5-hydroxyhexyl) phthalate
(MEHHP),mono-(2-ethyl-5-oxyhexyl) phthalate (MEOHP). Molecular
dock-ing and simulation studies were used to investigate the
bindingmode and stability of these PAEs to human ACMSD. The
resultsconclude that these phthalates can efficiently bind and can
inhibitthe activity of hACMSD. Hence, the binding of PAEs with
hACMSDaffect the basal trp to niacin ratio which further
accumulates thequinolate in the tryptophan metabolism pathway.
2. Material and methods
2.1. Protein and ligand preparation
Computational docking and simulation studies were performedin
order to assess the interaction of the phthalates with hACMSD.The
coordinates of hACMSD were retrieved from the crystal struc-
ture of hACMSD bound to a substrate analogue Dipicolinic
acid(PDC) (PDB ID: 4IH3) from the RCSB database [8]. Geometric
opti-mization of the protein was done by using the Clean
Geometrymodule of Discovery Studio (DS) 4.0 suite by Accelerys (San
Diego,CA, USA). Side-chain torsion angles varying more than 30◦
fromthe ideal values were corrected. The conformational quality
waschecked using the Minimize and refine protein module of DS
suite,in which water molecules were eliminated and the
CHARMM27force field was used for the protein receptor [23]. The
structurewas minimized for 1000 steps by utilizing the
smart-minimizeralgorithm with 0.1 RMS gradient cut-off to remove
the steric over-laps.
2.2. Molecular docking of ligands
AutoDock 4.2.6 was used to perform the docking of PAEs
withhACMSD [24]. Autodock utilizes a semiempirical free energy
forcefield to calculate the binding free energy of a small
moleculeto a macromolecule. Receptor molecule was prepared by
addingexplicit hydrogen molecules and associated Kollman
charges(16.0) by utilizing the AutoDock Tools 1.5.6 and saved
in.pdbqtfile format. Five commonly used diphthalates and their
cor-responding monophthalates used for the docking studies
withhACMSD are: dimethyl phthalate (DMP), diethyl phthalate
(DEP),di-n-butyl phthalate (DnBP), di-isobutyl phthalate (DIBP),
di-(2-ethylhexyl) phthalate (DEHP), monomethyl phthalate
(MMP),monoethyl phthalate (MEP), mono-n-butyl phthalate
(MBP),mono-iso butyl phthalate (MIBP), mono-(2-ethylhexyl)
phthalate(MEHP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP),
andmono-(2-ethyl-5-oxyhexyl) phthalate (MEOHP). As a positive
con-trol, substrate analogue of hACMSD, dipicolinic acid (PDC)
wasdocked and compared with binding affinity scores of PAEs. The3D
structures of all the phthalates were drawn using Marvin suite17.13
[http://www. chemaxon.com/marvin/sketch/index.jsp] andminimized
using DS suite. The properties of phthalate compoundsand their
structures are shown in Table 1. The ligands were pre-
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chemaxon.com/marvin/sketch/index.jsp
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216 N. Singh et al. / International Journal of Biological
Macromolecules 108 (2018) 214–224
Table 1Commonly used Phthalates and their usage. The details of
Phthalic acid esters (PAEs) such as DMP, DEP, DBP, DIBP, DEHP and
their metabolites along with their differentuses.
S.no. Phthalate category Phthalates Molecular weight (g/mol)
Structure Usage
1 Low Molecular weight DMP 194.18 Used as plasticizer, also used
in insectrepellents
MMP 180.15
DEP 222.4 Solvent in personal care products, celluloseacetate
plasticized films for food packaging
MEP 194.18
DBP 278.34 Enteric coating of medications and foodsupplements
and nitrocellulose–coatedregenerated cellulose film (RCF) used
inplasticized coatings.MBP 222.24
DIBP 278.34 Used as a substitute for DBP due to thesimilarity in
their application properties
MIBP 222.24
2 High molecular weight DEHP 390.56 Production of polyvinyl
chloride (PVC)plastics, Used in PVC based, medicalproducts, retail
packed food items
MEHP 278.34
MEHHP 294.34
MEOHP 292.33
pared by adding hydrogen atoms and Gasteiger charges and
thensaved in.pdbqt format. Ligand flexibility was used to specify
thetorsional degrees of freedom in ligand molecule. The atomic
poten-tial grid map was generated with a spacing of 0.375 Å by
usingAutoGrid 4. The docking receptor grid was created by
choosingthe catalytic key residues which show interaction with
substrateanalogue, PDC i.e. Arg47 and Trp191 and His174, present in
theproximity of zinc metal. The dimensions of the grid box were as
fol-lows 70 Å × 70 Å × 70 Å and the center point coordinates were
set asX = 0.56, Y = −12.94 and Z = 2.77. For docking purpose,
Lamarckiangenetic algorithm and grid supported energy evaluation
methodwere adopted. The number of total GA runs was increased
from10 to 100. Other docking parameters were used as default.
Thepose with the maximum binding affinity score and the
correspond-ing interactions was selected and further visually
inspected andanalyzed in PyMol 1.3 [25].
2.3. Molecular dynamics simulation
The associated structural and dynamic changes occurring at
theatomistic level in hACMSD on the binding of PAEs were analyzedby
molecular dynamics simulation. The simulation study was per-formed
with Gromacs 5.1.4 suite with GROMOS96 43a1 force fieldon LINUX
based workstation [26,27]. PDC and phthalates topol-ogy files were
generated using Automated Topology Builder (ATB)[28,29]. The
protein complexes were solvated in a cubic box withsimple point
charge (SPC) waters and counter ions were addedfor the overall
electrostatic neutrality of the system [30]. Energyminimization was
performed to minimize the steric clashes byusing Steepest descent
algorithm for 50,000 iteration steps andcut-off up to 1000 kJmol−1.
Then the system was equilibrated intwo different phases for 50,000
steps. The first phase of equilibra-
tion was done with a constant number of particles, volume,
andtemperature (NVT), each step 2 fs. The second phase of
equilibra-tion was performed with a constant number of particles,
pressure,and temperature (NPT), the ensemble at 300 K. LINCS
algorithm wasutilized for covalent bond constraints in the
equilibration steps. Forthe calculation of Lennard-Jones and
Coulomb interactions, 1.4 nmradius cut-off was used. Long range
electrostatics were calculatedby using Particle Mesh Ewald (PME)
method with Fourier grid spac-ing of 1.6 Å. The temperature inside
the box was regulated by usingV-rescale, a modified Berendsen
temperature coupling method.Parrinello-Rahman pressure coupling
method was utilized in NPTequilibration.
The final production step of molecular dynamics simulation
wascarried out for 20 ns, each step of 2 fs. Trajectories were
saved andresults were analyzed using XMGRACE. Root mean square
devia-tion (RMSD) variation in protein backbone was calculated by
usingg rms tool which utilizes the least-square fitting method.
Overallroot mean square fluctuation (RMSF) in the atomic positions
of pro-tein C� backbone was calculated by using the g rmsf tool. A
roughmeasure of compactness factor of protein during the course of
thesimulation was estimated by using the g gyrate tool of
GROMACS.gmx sasa was used for computation of the total solvent
accessiblesurface area (SASA). Hydrogen bonds were calculated with
3.5 Ådistance cut-off by using g hbond and the distribution of
inter-molecular hydrogen bond lengths throughout the simulation
werealso analyzed.
2.4. MMPBSA binding free energy calculation
The binding free energy of the interaction betweenligand–protein
complexes were obtained by utilizing the Molecu-lar
Mechanic/Poisson-Boltzmann Surface Area (MMPBSA) method
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N. Singh et al. / International Journal of Biological
Macromolecules 108 (2018) 214–224 217
Table 2Details of molecular docking results. The summary of
binding affinities (kcal/mol)and the polar interactions of the
PAEs-hACMSD complexes
S.no Ligand Binding affinity(kcal/mol)
Interactions
1. Dipicolinic acid(PDC)
−6.21 Arg47 (NH1)–O3 (2.8 Å)Arg47 (NH2)–O3 (3.4 Å)Arg47 (NH2)–O3
(3.1 Å)Trp191 (NE1)–O4 (3.2 Å)Trp191 (NE1)–N1 (3.3 Å)Asp291
(OD2)–O2 (3.1 Å)
2. DEHP −5.77 Arg47 (NH2)–O4 (2.4 Å)Arg47 (NH2)–O1 (2.7 Å)Arg47
(NE)–O3 (3.4 Å)Trp191 (NE1)–O4 (2.4 Å)Trp191 (NE1)–O4 (2.7 Å)Trp191
(NE1)–O4 (3.5 Å)
3. MEHP −7.48 His8 (NE2)–O3 (2.7 Å)Arg47 (NH2)–O1 (3.5 Å)His174
(NE2)–O3 (2.5 Å)Trp191 (NE1)–O1 (3.8 Å)His224 (NE2)–O3 (2.7
Å)Asp291 (OD1)–O3 (2.6 Å)Asp291 (OD2)–O4 (3.2 Å)
4. MEHHP −6.94 Arg47 (NH1)–O4 (3.0 Å)Arg47 (NH1)–O5 (3.0 Å)Arg47
(NH2)–O4 (3.2 Å)Val78 (N)–O5 (3.6 Å)Trp191 (NE1)–O3 (2.9 Å)Leu296
(N)–O3 (3.5 Å)
5. MEOHP −7.42 His8 (NE2)–O3 (3.4 Å)Arg47 (NH1)–O1 (3.4 Å)Arg47
(NH2)–O1 (2.8 Å)His174 (NE2)–O3 (3.2 Å)Asp177(N)–O3 (2.7 Å)Trp191
(NE1)–O1 (3.6 Å)His224 (NE2)–O3 (2.7 Å)Asp291 (OD1)–O3 (3.5
Å)Asp291 (OD2)–O4 (3.1 Å)
6. DMP −4.96 Arg47 (NH1)–O2 (2.7 Å)Arg47 (NH2)–O2 (3.3 Å)Arg47
(NH2)–O3 (2.8 Å)Trp191 (NE1)–O4 (3.1 Å)Trp191 (NE1)–N1 (2.9 Å)
7. DEP −4.90 Arg47 (NH1)–O4 (2.9 Å)Arg47 (NH1)–O3 (3.1 Å)Arg47
(NH2)–O2 (2.8 Å)Arg47 (NH2)–O3 (3.0 Å)Arg47 (NH2)–O4 (3.5 Å)Trp191
(NE1)–O2 (3.3 Å)
8. DBP −5.65 Arg47 (NH1)–O3 (3.0 Å)Arg47 (NH2)–O3 (2.9 Å)Val78
(N)–O5 (2.5 Å)Trp191 (NE1)–O2 (3.2 Å)
9. DIBP −5.13 Arg47 (NH1)–O4 (2.9 Å)Arg47 (NH1)–O2 (3.2 Å)Arg47
(NH2)–O1 (2.9 Å)Val78 (N)–O3 (3.7 Å)Trp191 (NE1)–O1 (3.2 Å)
10. MMP −5.10 His8 (NE2)–O11 (2.7 Å)Arg47 (NH2)–O1 (3.1 Å)His174
(NE2)–O11 (2.6 Å)Trp191 (NE1)–O1 (3.3 Å)His224 (NE2)–O12 (3.0
Å)Asp291 (OD1)–O11 (2.5 Å)Asp291 (OD2)–O12 (3.4 Å)
11. MEP −5.03 Arg47 (NH1)–O4 (2.8 Å)Arg47 (NH2)–O4 (3.3 Å)Arg47
(NH2)–O1 (3.0 Å)Val78 (N)–O2 (3.4 Å)His174 (NE2)–O3 (3.5 Å)Trp191
(NE1)–O1 (2.8 Å)
Table 2 (Continued)
S.no Ligand Binding affinity(kcal/mol)
Interactions
12. MBP −5.83 Arg47 (NH1)–O3 (3.2 Å)Arg47 (NH2)–O4 (3.2 Å)Arg47
(NH2)–O3 (3.0 Å)Val78 (N)–O4 (3.6 Å)Trp191 (NE1)–O2 (2.8 Å)
13. MIBP −5.25 Arg47 (NH1)–O4 (2.9 Å)Arg47 (NH2)–O3 (3.0 Å)Arg47
(NH2)–O2 (2.6 Å)Trp191 (NE1)–O2 (2.7 Å)
which employs ensembles derived from molecular dynamics(MD)
simulation [31]. In the GROMACS module, the g mmpbsaapplication is
used for the calculation of different components ofthe binding free
energy of PDC-hACMSD and PAEs-hACSMD com-plexes. Here, the binding
energy is an average of three energeticterms, i.e. potential energy
in the vacuum, polar-solvation energy,and non-polar solvation
energy, respectively. In the presentstudy, the snapshots at each 10
ps between 15 and 20 ns werecollected and MMPBSA was performed to
predict the bindingenergy.
3. Results and discussion
Due to the structural similarity of the benzene ring of PAEs
withnatural substrate analogue, PDC, they are expected to mimic
thebinding mode at the active site of hACSMD. Side chains of
PAEsenabled them to make analogous interactions with the
importantresidues such as Arg47 and Trp191 and they occupy the
activesite of hACMSD within the 4 Å vicinity of zinc metal.
Therefore,this study illustrates the binding efficiency of commonly
used PAEscomparable to that of PDC, a substrate analogue of
hACMSD.
3.1. Molecular docking of ligands
Molecular docking is an extensively used computationalapproach
to validate the binding of the suitable orientation ofsmall
molecule with the receptor protein. In order to character-ize the
molecular interactions, molecular docking of
co-crystallizedsubstrate analogue, Dipicolinic acid (PDC) along
with PAEs was per-formed within the binding pocket of hACMSD using
AutoDock 4.2.6.All the generated binding poses were ranked and
clustered accord-ing to their root mean- standard deviation (RMSD)
value, bindingaffinity score, and the vicinity of the zinc metal.
The AutoDockresults show that the diphthalates and their
monophthalates havea binding affinity in the range of −4.9 to −7.48
kcal/mol which iscomparable to a substrate analogue, PDC (−6.21
kcal/mol) as shownin Table 2.
All phthalates have shown interaction with the key amino
acidresidues such as Arg47 and Trp191, similar to substrate
analogue,PDC as shown in Figs. 2–4, , . Moreover, the
monophthalates ofDEHP such as MEHP, MEHHP, and MEOHP have shown
maximumbinding affinities with hACMSD. PAEs are present within the
rangeof 4 Å from the zinc metal and occupied the active site cavity
com-prising of residues such as His8, Val76, His174, Asp291,
Phe294,and Leu296, in the same manner as that of PDC. MEHP and
MEOHPalso have shown interaction with other active site residues
suchas His8, His224, His174, Asp177, and Asp291. The crystal
struc-ture of hACMSD in complex with 1,3-dihydroxyacetone
phosphate(DHAP) (PDB ID: 2WM1), a glycolytic intermediate which is
a potentinhibitor of the enzyme also shows interaction with key
residuessuch as Arg47, Asp291, and Trp191(Garavaglia et al., 2009).
Theseresults suggest that all the studied PAEs can efficiently bind
in the
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218 N. Singh et al. / International Journal of Biological
Macromolecules 108 (2018) 214–224
Fig. 2. The ribbon diagram of hACMSD binding active site cavity
with PDC, DEHP and its metabolites. A) DEHP (green color), B) MEHP
(magenta color), C) MEHHP (pink color),D) MEOHP (pale yellow color)
and E) PDC (grey color). All the PAEs occupy the active site cavity
within the 4 Å vicinity of zinc metal. Residues of the hACMSD are
shown instick form, zinc metal is shown as a sphere in metallic
blue color and the red dotted line represents intermolecular
hydrogen bond interactions. (For interpretation of thereferences to
colour in this figure legend, the reader is referred to the web
version of this article.)
active site of hACMSD. Moreover, DEHP along with its
monophtha-lates seems to be a potent inhibitor for hACMSD. Hence,
inhibitionof hACMSD by PAEs can disbalance the basal ratio of
Trp-niacin inthe kynurenine pathway.
3.2. Molecular simulation results
Molecular dynamics simulation studies provide suitable meansto
understand the changes occurring at the atomistic level in
theprotein-ligand system and emphasizes on the stability of the
com-
plex. Therefore, a simulation study was performed in order
tounderstand the dynamics involved during binding of phthalates
tohACMSD. In the present study, different parameters such as
RMSD,RMSF, radius of gyration (Rg), solvent accessible surface area
(SASA)and the hydrogen bond formation and length distribution
duringthe course of the simulation have been studied.
3.2.1. Root-mean-square deviation (RMSD)It represents the
dynamic stability of the protein and predicts
the conformation changes occurring in the protein backbone
dur-
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N. Singh et al. / International Journal of Biological
Macromolecules 108 (2018) 214–224 219
Fig. 3. The cartoon representation of hACMSD binding with low
molecular weight diphthalates. A) DMP (purple color), B) DEP (white
color), C) DBP (orange color) and D)DIBP (yellow color). hACMSD
residues are shown in stick form, zinc metal is shown as a sphere
in metallic blue color and the red dotted line represents
intermolecularhydrogen bond interactions of low molecular
phthalates with hACMSD. (For interpretation of the references to
colour in this figure legend, the reader is referred to the
webversion of this article.)
Fig. 4. The binding interaction of low molecular weight
Monophthalates with hACMSD. A) MMP (pale green color), B) MEP
(forest green color), C) MBP (light red color) andD) MIBP (light
blue color). The interacting residues of hACMSD residues are
represented in stick form, zinc metal is shown as a sphere in
metallic blue color and the red dottedline shows the intermolecular
hydrogen bonds. (For interpretation of the references to colour in
this figure legend, the reader is referred to the web version of
this article.)
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220 N. Singh et al. / International Journal of Biological
Macromolecules 108 (2018) 214–224
Fig. 5. RMSD graph of hACMSD with PDC and PAEs. RMSD profile of
the C� backbone of hACMSD during the 20 ns of simulation at 300 K:
A) PDC, DEHP and its metabolites,B) PDC and low molecular weight
diphthalates such as DMP, DEP, DBP and DIBP, and C) PDC and low
molecular weight monophthalates such as MMP, MEP, MBP and MIBP.
Table 3Average values of RMSD and RMSF of hACMSD −PAEs
complexes. Average RMSDand RMSF values corresponding to variation
in hACMSD backbone on the binding ofPAEs and PDC.
S.No Compound Average RMSD (Å) Average RMSF (Å)
1. PDC 0.252 0.1122. DEHP 0.261 0.1263. MEHP 0.307 0.1154. MEHHP
0.257 0.1255. MEOHP 0.221 0.1256. DMP 0.278 0.1277. DEP 0.275
0.1178. DBP 0.255 0.1189. DIBP 0.295 0.111
10. MMP 0.231 0.10211. MEP 0.262 0.13612. MBP 0.223 0.11013.
MIBP 0.279 0.132
ing the simulation. Here, in the present study, RMSD values
ofPDC-hACMSD and PAEs-hACMSD complexes were analyzed. RMSDplots
show that most of the system acquires equilibrium within10–12 ns
during the course of the simulation and were stable up to20 ns as
shown in Fig. 5. The average RMSD values of the PAEs-hACMSD
complexes were compared with the reference ligandPDC-hACSMD complex
as shown in Table 3. The high fluctuationof the RMSD values for all
the complexes lies within in the rangeof 2 Å to 3 Å. Complexes of
lower molecular weight monophtha-lates with hACMSD showed less
average RMSD as compared totheir corresponding diphthalates.
Complexes of hACMSD with DEPand MEP shows initial fluctuations and
attained equilibrium after7 ns of simulation. RMSD results suggest
that there is no majorchange in the backbone RMSD patterns of
PAEs-hACMSD com-plexes as compared to PDC-hACMSD complex. Moreover,
resultsreveal that lower molecular weight monophthalates have a
lessaverage root mean square deviation and are more stabilized
ascompared to their corresponding diphthalates. The RMSD
resultsanalysis implies that the binding of phthalates at the
catalytic site
of hACMSD is stable and does not vary the protein backbone
sta-bility.
3.2.2. Root-mean-square fluctuation (RMSF)RMSF determines the
flexibility of the polypeptide chain after
fitting it to a reference frame. It is the fluctuation of C�
atomcoordinates from their average position during the
simulation.Generally, in proteins loosely organized loops are
characterizedby high RMSF values while secondary structural
elements showless flexibility. In the present context, residue
mobility was cal-culated for each of the PAEs-hACMSD complexes and
was plottedagainst the residue number based on the trajectory of MD
simu-lation. Results show a higher RMSF peak in the residues range
of180–193, a loop region, which facilitates the entry of ligands
intothe active site of hACMSD as shown in Fig. 6. The partial
helicalregion from the residue 232–250, present towards the C
terminal ofhACMSD also shows the higher RMSF fluctuation. These
results sug-gest that active site residues were not considerably
perturbed uponbinding of the ligands. Results illustrate that the
RMSF fluctuationprofiles of PAEs-hACMSD complexes were almost
similar to PDC-hACMSD complex. RMSFs results imply that the atomic
mobilityof PAEs-hACMSD complexes is in consent with PDC-hACMSD
com-plex. Thus, the PAEs form stable complexes with hACMSD and
caninhibit this important enzyme of tryptophan metabolism
pathway.
3.2.3. Radius of gyration (Rg)Radius of gyration (Rg) factor is
associated with the compactness
of protein during the molecular simulation. It is simply a
measure ofthe distance between the center of mass of the protein
atoms andits terminal in a given time step. In general, a stably
folded pro-tein tends to maintain a relatively less variation in Rg
value whichdetermines its dynamic stability. In the present study,
variationoccurring in Rg value is plotted against time as shown in
Fig. 7. Theradius of gyration results shows that compactness of
phthalate-hACMSD complexes is comparable to the PDC-hACMSD
complex.Rg results reveal that secondary structures are compactly
packed
-
N. Singh et al. / International Journal of Biological
Macromolecules 108 (2018) 214–224 221
Fig. 6. RMSF molecular dynamics simulation results of hACMSD for
20 ns. A) PDC, DEHP and its metabolites with hACMSD, B) PDC and low
molecular weight diphthalatessuch as DMP, DEP, DBP and DIBP with
hACMSD, and C) PDC and low molecular weight monophthalates such as
MMP, MEP, MBP and MIBP.
Fig. 7. Radius of gyration (Rg) plots of PDC-hACMSD and
PAEs-hACSMD. The radius of gyration results associated with the
compactness of the hACMSD protein for thesimulation of 20 ns: A)
PDC, DEHP and its metabolites, B) PDC and low molecular weight
diphthalates such as DMP, DEP, DBP and DIBP, and C) PDC, low
molecular weightmonophthalates such as MMP, MEP, MBP and MIBP.
in the case of diphthalates and their metabolites to form
stablecomplexes with hACMSD.
3.2.4. Solvent accessible surface area (SASA)Solvation free
energy of each atom in a protein is contributed
by its polar and non polar residue interactions. Solvent
accessiblesurface area (SASA) is the surface area monitored by the
probe ofthe solvent molecule when it traces the Van der Waals
surface ofthe receptor molecule. Mostly structural alterations are
monitoredin the residues forming the loop region in the vicinity of
the active
site cavity. In general, hydrophobic residues mostly contribute
tothe rise of SASA value. This is also apparent by the raised value
of thesolvent accessible surface area (SASA) in that region. SASA
resultsof PAEs-hACMSD complexes are similar to PDC-hACMSD complexas
shown in Fig. 8.
3.2.5. Hydrogen bond analysisHydrogen bond number and
distribution in the PAEs-hACMSD
complexes were studied to determine the stability of the
systemduring the 20 ns simulation period. The g hbond utility of
GRO-
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222 N. Singh et al. / International Journal of Biological
Macromolecules 108 (2018) 214–224
Fig. 8. Solvent accessible surface area profile of hACMSD with
PDC and PAEs. SASA results of hACMSD-PDC and hACMSD-PAEs complexes
during the simulation of 20 ns: A)PDC, DEHP and its metabolites, B)
PDC and low molecular weight diphthalates such as DMP, DEP, DBP and
DIBP, and C) PDC and low molecular weight monophthalates suchas
MMP, MEP, MBP and MIBP.
Fig. 9. Hydrogen bond number results of PDC-hACMSD and
PAEs-hACMSD complexes during the 20 ns of simulation. A) PDC, DEHP
and its metabolites, B) PDC and lowmolecular weight diphthalates
such as DMP, DEP, DBP and DIBP, and C) PDC and low molecular weight
monophthalates such as MMP, MEP, MBP and MIBP.
MACS was employed to compute the hydrogen bond numbers
anddistribution profiles of the complexes. Hydrogen bond
numbersresults show that the most of the PAEs-hACMSD complexes
havemaintained a minimum of two hydrogen bonds throughout thecourse
of simulation as shown in Fig. 9. Moreover, monophtha-late
metabolites of DEHP such as MEHP, MEHHP, and MEOHP forma maximum
number of hydrogen bonds with the hACMSD dur-ing the course of 20
ns simulation. The average number of h bondsduring the MD phase
shows their continuous contribution in pro-viding stability to the
complex. The results of the distribution ofhydrogen bond lengths
also indicate that the PAEs-hACMSD com-
plexes have form high to low affinity hydrogen bonds, which isin
consent with PDC-hACMSD complex as shown in Fig. 10. Thehydrogen
bond results help in understanding the functionality andability of
these harmful phthalate compounds to efficiently hinderthe activity
hACMSD in the kynurenine pathway.
3.3. MMPBSA binding free energy calculation
The free energy calculation analysis is useful in assessing
thebinding potential of ligands as it provides a quantitative
estimationof the binding free energy. MMPBSA, a utility within
GROMACS was
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N. Singh et al. / International Journal of Biological
Macromolecules 108 (2018) 214–224 223
Fig. 10. Bar representation of the hydrogen bond numbers
distribution of hACMSD with PDC and PAEs. Hydrogen bond
distribution profile of PDC-hACMSD and PAEs-hACMSDcomplexes for the
course of 20 ns simulation at 300 K, A) PDC, DEHP and its
metabolites, B) PDC and low molecular weight diphthalates such as
DMP, DEP, DBP and DIBP, andC) PDC and low molecular weight
monophthalates such as MMP, MEP, MBP and MIBP.
Table 4Binding energies of hACMSD-PAEs complexes by MMPBSA. Van
der Waals energy, electostatic energy, polar solvation energy and
SASA energy components contributing tothe total binding free energy
of hACMSD-PAEs and hACMSD-PDC complexes.
S.No Compound Van der Waalsenergy (kJ/mol)
Electostaticenergy (kJ/mol)
Polar solvationenergy (kJ/mol)
SASA energy(kJ/mol)
Binding energy(kJ/mol)
1 PDC −139.405 +/− 0.329 −9.747 +/− 0.331 67.745 +/− 0.423
−11.127 +/− 0.027 −92.546 +/− 0.5672 DEHP −213.316 +/− 0.602
−12.924 +/− 0.346 157.375+/− 1.362 −20.392 +/− 0.056 −89.257 +/−
1.5153 MEHP −182.956 +/− 0.609 −35.861 +/− 0.383 104.625+/− 0.742
−15.679 +/− 0.045 −129.888 +/− 0.6864 MEHHP −201.045 +/− 0.928
−10.651 +/− 0.404 78.052 +/− 0.629 −15.373 +/− 0.059 −149.068+/−
0.8065 MEOHP −228.297 +/− 0.522 −22.370 +/− 0.309 118.271+/− 0.579
−17.246 +/− 0.043 −149.646+/− 0.8476 DMP −158.565 +/− 0.343 −25.771
+/− 0.241 122.446+/− 0.451 −12.060 +/− 0.029 −73.961 +/− 0.5077 DEP
−142.667 +/− 0.468 −42.500 +/− 0.454 135.593+/− 1.449 −12.310 +/−
0.040 −61.884 +/− 1.3478 DBP −167.730 +/− 0.465 −27.721 +/− 0.459
124.417+/− 0.716 −14.253 +/− 0.042 −85.287 +/− 0.8109 DIBP −183.035
+/− 0.477 −9.031 +/− 0.340 125.265+/− 0.891 −17.092 +/− 0.041
−83.893 +/− 0.91010 MMP −149.449 +/− 0.410 −65.586 +/− 0.430
139.931+/− 0.756 −10.851 +/− 0.026 −85.955 +/− 0.99011 MEP −145.130
+/− 0.379 −23.799 +/− 0.557 103.311+/− 1.398 −12.793 +/− 0.039
−78.411 +/− 1.38512 MBP −182.239 +/− 1.320 −20.841 +/− 0.394
108.814+/− 0.860 −13.533 +/− 0.098 −107.794+/− 1.40213 MIBP
−196.513 +/− 0.367 −9.160 +/− 0.191 122.351+/− 0.323 −13.708 +/−
0.029 −97.022 +/− 0.489
utilized to calculate the binding free energy of PAEs-hACMSD
com-plexes. The trajectories of the last 5 ns of PAEs-hACMSD
complexeswere generated and the MMPBSA was utilized for the
prediction ofthe binding energy of the complexes. MMPBSA results
show thatthe binding free energies of the PAEs are comparable to
the PDCwhile the monophthalates metabolites of DEHP have higher
bind-ing free energy than other PAEs as shown in Table 4. Binding
freeenergy results of MMPBSA indicate that PAEs-hACMSD complexesare
stable. MMPBSA results confirm that these phthalates can
effi-ciently bind in the active site of hACMSD and inhibit
enzymaticactivity. Highest binding free energies of DEHP and its
metabo-lites reveal that they are can bind with high affinity to
hACSMDas compared to PDC.
4. Conclusion
In kynurenine pathway, human
�-amino-�-carboxymuconate-�-semialdehyde decarboxylase (hACMSD)
controls the level ofquinolinic acid. The inhibition of hACMSD
activity may lead tothe elevation of quinolate levels which is
often associated with
several neurological disorders. PAEs are the ubiquitous
environ-mental pollutants which have endocrine disruption and
teratogenicproperties. The in-silico techniques such as molecular
docking andsimulation studies were done to analyze the interactions
of com-monly used PAEs with hACMSD. This computational study
showsthat the PAEs-hACMSD complexes are stable and have
bindingaffinities similar to natural substrate analogue complex
i.e. PDC-hACMSD. Hence, PAEs and their metabolites can efficiently
bindto hACMSD and inhibit its activity in the kynurenine pathway
oftryptophan metabolism. Our study emphasizes on the
inhibitoryeffects of phthalates on hACMSD activity and
simultaneously pro-vides a regulatory link between disturbances in
trp to niacin ratioon the administration of PAEs in the diet.
Further studies shouldfocus on the in-vitro assessment of phthalate
toxicity and correla-tion needs to establish between the elevations
of quinolinic acidlevel in urine along with corresponding phthalate
exposure.
Disclosure statement
No conflict of interest is reported by the authors
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224 N. Singh et al. / International Journal of Biological
Macromolecules 108 (2018) 214–224
Acknowledgement
PK would like to thank DBT, Govt. Of India, Ministry of Sci-ence
and Technology for providing the financial support throughNational
Bioscience Award and grant no. BT/HRD/NBA/37/01/2015(VII). NS
thanks, UGC and VD thanks DBT for financial support.
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