Relationship between the binding free energy and PCBs’ migration, persistence, toxicity and bioaccumulation using a combination of the molecular docking method and 3D-QSAR
Zhao et al. Chemistry Central Journal
Relationship between the binding free energy and PCBs' migration, persistence, toxicity and bioaccumulation using a combination of the molecular docking method and 3D-QSAR
Xiao‑Hui Zhao 0 1
Xiao‑Lei Wang 0 1
Yu Li 0 1
0 College of Environmental Science and Engineering, North China Electric Power University , No. 2, Beinong Road, Beijing 102206 , China
1 The Moe Key Laboratory of Resources and Evironmental Systems Optimization, North China Electric Power University , Beijing 102206 , China
The molecular docking method was used to calculate the binding free energies between biphenyl dioxygenase and 209 polychlorinated biphenyl (PCB) congeners. The relationships between the calculated binding free energies and migration (octanol-air partition coefficients, KOA), persistence (half‑ life, t1/2), toxicity (half maximal inhibitory concentration, IC50), and bioaccumulation (bioconcentration factor, BCF) values for the PCBs were used to gain insight into the degradation of PCBs in the presence of biphenyl dioxygenase. The relationships between the calculated binding free energies and the molecular weights, KOA, BCF, and t1/2 values for the PCBs were statistically significant (P < 0.01), whereas the relationship between the calculated binding free energies and the IC50 for the PCBs was not statistically significant (P > 0.05). The electrostatic field, derived from three‑ dimensional quantitative structure-activity relationship studies, was a primary factor governing the binding free energy, which agreed with literature findings for KOA, t1/2, and BCF. Comparative molecular field analysis and comparative molecular similarity indices analysis contour maps showed that the binding free energies, KOA, t1/2, and BCF values for the PCBs decreased simultaneously when substituents with electropositive groups at the 3‑ position or electronegative groups at the 3′‑ position were introduced. This indicated the binding free energy was correlated with the persistent organic pollutant characteristics of PCBs. Furthermore, low binding free energies improved the degradation of the PCBs and simultaneously decreased the KOA, t1/2, and BCF values, thereby reducing the persistent organic pollutant characteristics of PCBs in the environment. These results are expected to be beneficial in providing a theoretical foundation for further elucidation of the degradation and molecular modification of PCBs.
Polychlorinated biphenyl; Molecular docking; Biphenyl dioxygenase; Pearson correlation; Three‑ dimensional quantitative structure-activity relationship
Introduction
Polychlorinated biphenyls (PCBs) are a typical persistent
organic pollutant with an aromatic biphenyl skeleton
containing one to ten chlorine atoms that can
theoretically yield 209 different congeners [
1
]. PCBS are
chemically and thermally stable and have accumulated in soil,
sediments, and the atmosphere where they can harm
human health and the environment because of their
toxicity.
From when they were first produced in the 1930s to
when they were banned in the 1990s, about 1.3 million
tons of PCBs were produced, and tens of thousands of
tons are known to have been released into the
environment causing widespread pollution [
2
]. PCBs are of great
concern because of their persistence, high lipophilicity,
and adverse effects on organisms. Consequently,
numerous studies have investigated the levels of PCBs in various
environmental samples, such as sediments [
3, 4
], indoor
air [5], residential carpet dust [
6
], chicken egg yolks [
7
],
fish and meat [
8
], and human breast milk [
9, 10
], blood
[11], and adipose tissue [
12
]. Because PCBs tend to
accumulate in the food chain, they present a high risk to
human and animal health [
13
], and complete degradation
of PCBs in the environment is essential to avoid this.
Biodegradation of PCBs by microbes has been
studied extensively. This process mainly occurs via oxidation
by enzymatic catalysis. Since 1973, many aerobic
bacteria capable of degrading PCB congeners have been
isolated, including Burkholderia LB400 [
14
], Rhodococcus
sp. strain RHA1 [
15
], Alcaligenes eutrophus H850 [
16
],
and Enterobacter sp. LY402 [
17
]. Biphenyl dioxygenase
(BphA) plays a major role in the degradation of PCBs
[
18
]. BphA uses O2 and electrons to catalyze the
dihydroxylation of an aromatic ring as the initial PCB
degradation step and determine the substrate specificity for the
overall degradation pathway. Cao et al. [
19
] analyzed the
PCB degradation abilities of BphA1 from Enterobacter
sp. LY402 experimentally and with molecular simulation.
They found that the binding free energies of the PCBs
were well matched with the degradation rate constants
(k) for PCBs with different numbers of chlorine
substituents. In other words, the binding free energies of the
PCBs decrease as k increases. Wu et al. [
20
] compared
the binding free energies of pollutants and receptors, and
found that a lower binding free energy indicated higher
affinity. Therefore, the binding free energy can be used to
evaluate the degradation abilities and analyze the
biodegradation of PCBs. Liu et al. [
21
] studied the relationship
between the molecular characteristics and degradation
rates of substrates degraded by Enterobacter sp. LY402.
They determined that the dipole moment of a substrate
was positively correlated with its degradation rate, and
the stretch-bend energy was negatively correlated with
the degradation rates. However, the effectiveness of
microbial degradation of highly chlorinated PCBs and
specific substituted PCBs is limited [
22
]. To gain insight
into the molecular basis of degradation, the key enzymes
involved in the PCB biodegradation process, specifically
BphA, have been studied intensively [
23–25
].
Three-dimensional quantitative structure–activity
relationship (3D-QSAR) studies can be used to evaluate
the structure–activity relationships of a set of molecules
using comparative molecular field analysis (CoMFA) and
comparative molecular similarity indices analysis
(CoMSIA). These two methods can avoid the insufficiency of
the traditional two-dimensional model that is used to
characterize the relationships between properties and
structures, and have clear physical significance and
provide rich information about the molecular field energy
[
26
].
The objective of this study was to reveal the
relationships between the binding free energies of BphA with
PCBs and the PCBs’ migration, persistence, toxicity,
and bioaccumulation. First, molecular docking was used
to explore the interactions between BphA and 209 PCB
congeners. Next, Pearson correlation analysis was
performed to study the relationships between the binding
free energies and the molecular weights, migration
abilities (octanol–air partition coefficients, KOA),
bioaccumulation (bioconcentration factors, BCF), environmental
persistence (half-life, t1/2), and toxicities (half maximal
inhibitory concentration, IC50) of the PCBs. Finally,
QSAR CoMFA and CoMSIA were used to investigate the
relationships between the binding free energies and the
structures of the PCBs. These results will provide a
theoretical foundation for further elucidation of the
degradation and molecular modification of PCBs.
Materials and methods
Protein structures
Crystal structures of nine types of BphA (PDB IDs: 1ULJ
[
27
], 1WQL [
28
], 2YFJ [
29
], 2YFL [
30
], 2GBX [
31
], 2XSH
[
32
], 2E4P [
33
], 3GZX [
34
], and 3GZY [
34
]) used in
Surflex-Dock were obtained from the Protein Data Bank
(http://www.rcsb.org/pdb/). The catalytic activities these
BphA were different. The active sites were determined
by the amino acid residues in the region where catalytic
activity occurred. As different types of BphA may contain
multiple active sites, all active sites were docked into the
209 PCBs to ensure the molecular docking was accurate.
Molecular docking
The molecular docking was performed using the Sybyl-x
2.0 molecular modeling package (Tripos Inc., St. Louis,
MO) running on a Windows 7 32-bit workstation. The
Surflex-Dock method in the Sybyl package was used
to carry out molecular docking simulations to dock the
ligands into a receptor’s ligand binding site and
represent the interaction strength. 209 PCBs were minimized
under the Tripos force field with MMFF94 [
35
] atomic
partial charges by the Powell method, with a maximum
iteration of 10,000 to reach a convergence gradient value
of 0.001 kcal mol−1 Å. Before docking, the natural ligand
and structural water molecules were removed from the
crystal structure. Using the Biopolymer module, polar
hydrogen atoms were added into the standard
geometry. Kollman all-atom charges were assigned to protein
atoms [
36
]. In this process, ligands were automatically
docked into the binding site of the protein using a
ProtoMol-based approach [
37
] with a patented search engine
and an empirical scoring function. We applied the
automatic docking method. Based on the software protocol
of Surflex-Dock, ProtoMol was produced. “Proto Bloat”
and “Proto Thresh” are two important factors that greatly
affect the size and extent of the ProtoMol. “Proto Bloat”
determines how far ProtoMol extends outside of the
concavity, while “Proto Thresh” represents how far ProtoMol
extends into the concavity of the target site [
38
]. In this
process, for all ProtoMols produced, “Proto Thresh” was
set to 0.5 and “Proto Bloat” was set to 1. All other
parameters were at the default settings. Then, a binding pocket
was generated. Successful molecular docking is indicated
by a root mean square deviation of less than 2 Å [
39
].
According to the total docking scores, the first twenty
conformations of every ligand were saved. The best
conformations of the ligands were analyzed for their
binding interactions [
40
]. In the molecular docking process,
the receptor protein was considered to be rigid, and the
ligand compounds were regarded as being flexible [
36
].
Surflex-Dock’s scoring function, which contains
hydrophobic, polar, repulsive, entropic, and solvation terms,
was trained to estimate the dissociation constant (Kd)
expressed as − log(Kd) unit [
41
]. The docking results were
ranked in a molecular spreadsheet. The conformer with
the highest score was taken as the docking result [
20
]. For
a better comparison between the binding free energies of
the PCBs and BphA, the binding scores were converted
to binding free energies (kcal/mol). The binding free
energy was calculated as follows: where RT = 0.59 kcal/
mol [
38
]:
Free energy of binding = RT × ln10−pKd
(1)
RT = 0.59 kcal/mol.
3D‑QSAR analysis
The calculated binding free energies for 209 PCBs and
BphA were used for modeling. CoMFA and
CoMSIA methods were applied to build 3D-QSAR models
using the structural parameters as independent
variables and the binding free energy as the dependent
variable to obtain the relationship between the binding free
energy and molecular structure. In construction of the
3D-QSAR model, molecular alignment is an important
step [
42
]. In this approach, decachlorobiphenyl, which
had the largest binding free energy, was used as the
template to align the remaining PCBs. The 209 PCBs were
well aligned.
CoMFA and CoMSIA analysis
The CoMFA model includes steric and electrostatic
descriptor fields. A 3D cubic lattice with a grid spacing
of 2 Å and a sp3 carbon probe atom was created to
calculate the CoMFA descriptor fields. The distance
dependent dielectric constant was set at 1.0. The default energy
cutoff value was set at 30 kcal mol−1. The CoMSIA model
was based on the CoMFA model, which was composed of
hydrophobic, hydrogen bond donor, and hydrogen bond
acceptor fields. A sp3 carbon carrying a charge of + 1.0
was used as the probe atom to generate the CoMSIA
descriptor fields. The default value of 0.3 was used as the
attenuation factor α [
43
]. CoMSIA uses a Gaussian-type
distance dependence to measure the relative attenuation
of the field position of each atom in the lattice, and leads
to much smoother sampling of the fields around the
molecules than CoMFA. Compared with the CoMFA model,
CoMSIA can avoid inherent defects, but the results are
not necessarily good [
44
]. Hence, both methods (CoMFA
and CoMSIA) can be used to verify and complement
each other to obtain a reliable prediction model.
Partial least squares regression
Partial least squares regression analysis was applied to
establish linear correlations between the binding free
energies and the CoMFA and CoMSIA descriptors.
Cross-validation with the leave-one-out method was
carried out to yield the square of the cross-validation
coefficient (q2) and the optimum number of components (N).
Then, non-cross-validation analysis was performed with
N and a column filter of 2.0 kcal mol−1 to produce
regression models for CoMFA and CoMSIA and accelerate the
analysis and decrease the noise. The 3D colored contour
maps represent the relationship between the binding free
energies and each molecular field.
Pearson correlation analysis
Pearson correlation analysis was performed using SPSS
software to study the relationship between the binding
free energies and the molecular weights, KOA, BCF, t1/2,
and IC50 values of the PCBs. The KOA [
45
], BCF [
46
], t1/2
[
47
], and IC50 [
48
] values were obtained directly from the
literature. The binding free energies were significantly
correlated to the molecular weight, KOA, BCF, t1/2, and
IC50 values when the P value was less than 0.01. The
binding free energy was significantly correlated to the
molecular weight, KOA, BCF, t1/2, and IC50 when P was more
than 0.01 but less than 0.05. The binding free energy was
not significantly correlated to the molecular weight, KOA,
BCF, t1/2, or IC50 when P was more than 0.05.
Results and discussion
Comparative analysis of affinities between PCBs and BphA
The root mean square deviation range was 0.0154–
0.9753, with all values less than 2 Å, indicating that
Surflex-Dock was reliable in causing reappearance of
the binding pattern of the ligand, and the parameter
setting for molecular docking was appropriate. The
docking results are shown in Fig. 1. The binding free energy
was calculated using Eq. (1) and was used to evaluate the
affinity between the ligand and receptor. Low binding free
energies indicate high affinity or catalytic activity [
20
].
The degradation rate constants of the PCBs decreased
as binding free energy increased (i.e., the binding
affinity between the ligand and receptor decreased) [
19
]. As
different types of BphA may contain multiple active sites,
we selected a group from every BphA that had the
highest comprehensive scoring function (Fig. 2).
The binding free energies of the PCBs were well
matched with the degradation rate constants (k) for the
different numbers of chlorine substituents. A lower
binding free energy represents stronger binding
affinity or catalytic activity [
20
]. Both 1ULJ and 1WQL could
degrade the isomers from chlorobiphenyl to
tetrachlorobiphenyl (Fig. 2). The PCB degradation rates of 1ULJ
were higher than those of 1WQL for all isomers except
tetrachlorobiphenyl, which had a higher degradation
rate with 1WQL than 1ULJ. Both 3GZX and 2YFL could
degrade the isomers from chlorobiphenyl to
hexachlorobiphenyl. The PCB degradation rate of 3GZX was higher
than that of 2YFL. 2GBX, 2XSH, and 2YFJ degraded the
isomers from chlorobiphenyl to pentachlorobiphenyl
with PCB degradation rates decreasing in the following
order: 2GBX > 2YFJ > 2XSH. For hexachlorobiphenyl and
heptachlorobiphenyl, the degradation rates with 2XSH
were higher than those with 2GBX and 2YFJ. 2E4P was
able to degrade the isomers from chlorobiphenyl to
octachlorobiphenyl, and 3GZY degraded the isomers from
chlorobiphenyl to nonachlorobiphenyl.
Although all of the types of BphA could degrade the
less-chlorinated PCBs (one to four Cl atoms), the
degradation rates were different. The PCB degradation rates
of the different types of BphA increased in the following
order: 2E4P < 3GZY < 1WQL < 1ULJ < 2XSH < 2YFL < 2
YFJ < 3GZX < 2GBX. 3GZY was able to degrade highly
chlorinated PCBs (five to nine Cl atoms), but the
degradation rates were not high. For pentachlorobiphenyl and
hexachlorobiphenyl, the degradation rates of seven types
of BphA were in the following order: 2GBX > 2XSH > 2Y
FJ > 2YFL > 3GZX > 3GZY > 2E4P. For
heptachlorobiphenyl, the degradation rates of five types of BphA increased
in the following order: 2YFJ < 2GBX < 2XSH < 2E4P < 3
GZY. The degradation rate for octachlorobiphenyl was
higher with 3GZY than with 2E4P. Our results correspond
with those from previous studies that found that low
binding free energies were an indicator of high affinity [
20
].
Chen et al. [
45
] studied the application of PCB
industrial products in practical production. Their results
showed that isomers from trichlorobiphenyl to
heptachlorobiphenyl accounted for the majority of the
commercial mixtures of PCBs, and chlorobiphenyl and
dichlorobiphenyl also contributed a substantial
proportion [
45
]. In the environment, the isomers from
trichlorobiphenyl to heptachlorobiphenyl are the primary PCBs
that undergo degradation. In this study, 2GBX, 2XSH,
2YFJ, 2E4P, and 3GZY had the ability to degrade the
isomers from trichlorodiphenyl to
heptachlorobiphenyl, and the PCB degradation rates were in the following
order: 2GBX > 2YFJ > 2XSH > 3GZY > 2E4P. For single
PCBs, 2GBX had the highest degradation rates for the
isomers from chlorobiphenyl to pentachlorobiphenyl,
2XSH had the best ability to degrade hexachlorobiphenyl,
and 3GZY had the highest degradation rates for isomers
from heptachlorobiphenyl to nonachlorobiphenyl. In the
microbial degradation system for PCBs, the
biodegradation ratios of the PCBs were maximized with addition of
2GBX, 2XSH, and 3GZY.
Correlation analysis of the relationships between the binding free energies and the persistent organic pollutant characteristics of the PCBs
In this study, Pearson correlation analysis was used to
study the relationships between the binding free energies
and the molecular weight, KOA, BCF, t1/2, and IC50 values
for the PCBs (Table 1).
The Pearson correlation analysis revealed that the
binding free energies were significantly correlated
to the molecular weight, KOA, BCF, and t1/2 values
(P = 0 < 0.01), but not significantly correlated to the IC50
values (P < 0.05).
The binding free energies between the PCBs and
BphA gradually increased as the number of Cl atoms
increased (Fig. 2), that is, the binding free energy
gradually increased with increasing molecular weight. This
indicted a positive correlation between the binding free
energy and molecular weight. Large KOA values for the
PCBs imply they have stronger migration abilities [
45
].
Strong migration abilities may suggest that PCBs are not
easily degraded in the environment because the
atmospheric conditions are not suitable for biodegradation.
Because the binding free energy was negatively
correlated with the degradation ability, low degradation
ability implied the binding free energy was strong. Therefore,
the binding free energy was positively correlated with
the KOA values of the PCBs. Large BCF values implied
that the PCBs strongly bioaccumulated in the body [
44
].
With gradual accumulation in the food chain, the levels
of PCBs in the body will gradually increase, and this will
be accompanied by a decrease in the amount of PCBs
in the environment. This is not beneficial for PCB
degradation in the environment by microorganisms. Hence,
PCBs are more difficult to degrade following an increase
in the binding free energy, which shows that the binding
free energy is positively correlated with the BCF. Large
t1/2 values for the PCBs imply that they are persistent in
“r” represents “Pearson correlation coefficient, the bigger the r, the stronger the correlation”; “sig.” means “significant, if sig < 0.05, indicating the significant correlation”;
“N” represents “sample size”;“**” means “the coefficient statistically significant was at P = 0.01” (n = 209)
N, optimum number of components; r2, correlation coefficient; q2, cross-validated val; SEE, standard error of the estimate; F, Fischer’s test value; S, steric; E,
electrostatic; H, hydrophobic; D, hydrogen bond donor; A, hydrogen bond acceptor
the environment [
45
]. Hence, PCBs are more difficult to
degrade following an increase in the binding free energy,
which agrees with earlier studies.
CoMFA and CoMSIA contour analysis
The q2 from the CoMFA models had a range of 0.585–
0.908 (> 0.5), the r2 range was 0.717–0.948 (> 0.6) [
49
],
the SEE range was 0.357–1.380, and the F range was
32.668–269.003 (Table 2). Therefore, the CoMFA model
was robust. For the CoMSIA models, the q2 range was
0.521–0.897 (> 0.5), the r2 range was 0.630–0.930 (> 0.6)
[
49
], the SEE range was 0.394–1.750, and the F range
was 20.393–197.637. Therefore, the CoMFA model was
robust. The contribution rates of the electrostatic fields
to the CoMFA and CoMSIA models were in the range
of 0.609–0.848, which showed that the electrostatic field
was important in each of the two models. Chen et al.
[
45
] found that the electrostatic field played an
important role in determining the logKOA values of PCBs, Liu
et al. [
44
] determined that the electrostatic descriptor
was a primary factor governing the logBCF, and Xu et al.
[
45
] found that electrostatic interaction was crucial in
determining the t1/2 values. These results are consistent
with the positive correlations between the binding free
energies and the KOA, BCF, and t1/2 values for the PCBs,
which can be indirectly explained by the correlation
between the binding free energies and the KOA, BCF, and
t1/2 values for the PCBs from the molecular field
perspective. The binding free energies between the nine types of
BphA and 209 PCBs are shown in Table S1 (shown in the
Additional file 1 available to the readers).
The most interesting feature of CoMFA and
CoMSIA models is the visualization of the results as 3D
contour plots. We can observe the contour of the 3D space
around the molecule, in which changes in the
physicochemical properties are predicted to increase or decrease
the effectiveness of the contour [
50
]. The results for the
CoMFA and CoMSIA models are illustrated in field
contribution graphs using the standard deviation times
coefficient field. The contour maps of the two models (Figs. 3,
4) show the electrostatic fields with decachlorobiphenyl
as a template. These contour maps showed 80 and 20%
contributions for favorable and unfavorable regions,
respectively. The electrostatic field is represented by
blue and red contours, with blue regions indicating the
electropositive groups close to these regions are
favorable to the binding free energy, and the red regions
indicate the electronegative groups near these regions can
enhance the binding free energy. By contrast, the binding
free energy decreased when electropositive groups were
introduced in the red region or electronegative groups in
the blue region.
Although the electrostatic field was identified as a
major factor affecting the binding free energies between
BphA and the PCBs, there were some differences in the
electrostatic contour maps (Figs. 3, 4, Table 3).
The binding free energies between the PCBs and
the nine types of BphA increased simultaneously with
substituents possessing electropositive groups at the
3′-position of the B ring or electronegative groups at
the 2- and 3-positions of the A ring. In the electrostatic
fields of 1WQL, 2YFJ, 2YFL, 2XSH, and 2E4P,
electropositive groups were desirable at the 3′- and 5-positions
to increase the binding free energies. In the electrostatic
fields of 2GBX and 3GZX, the electropositive groups
close to the regions of the 2- and 3′-positions increased
the binding free energies between the PCBs and BphA.
Introducing electropositive groups at the 2-, 3′-, and
5-positions increased the binding free energies between
the PCBs and 1ULJ. The binding free energies between
the PCBs and 3GZY increased with substituents
possessing electropositive groups at the 3′-position of the B ring.
In the electrostatic fields of 1ULJ and 3GZX,
introducing electronegative groups to the 2-, 3-, and
6-positions of the A ring increased the binding free energies.
The binding free energies between PCB-24 and PCB-5
could be deciphered using this contour. In the
electrostatic fields of 2YFJ and 2GBX, electronegative groups
near the 2-, 3-, 3′-, 4-, 6-, and 6′-positions were favorable
for enhancing the binding free energies, as shown by the
higher binding free energy for PCB-109 than for PCB-55
or PCB-59. The binding free energies between the PCBs
and 1WQL were enhanced with electronegative groups at
the 2-, 3-, and 5-positions of the A ring. For example, the
binding free energy of PCB-23 was higher than those of
PCB-6 and PCB-9. Electronegative groups were desirable
at the 2-, 3-, and 4-positions of the A ring and increased
the binding free energies between the PCBs and 3GZY,
as shown by the higher binding free energy for PCB-21
than for PCB-7 or PCB-12. Introducing electronegative
groups to the 2- and 3-positions of the A ring increased
the binding free energies between the PCBs and 2XSH.
The same applied for the binding free energies between
PCB-5 and PCB-2. The binding free energies between
the PCBs and 2E4P increased on introduction of
substituents with electronegative groups at the 2-, 3-, 6-, and
6′-positions. An example of this was the high binding
free energy of PCB-5 compared with those of PCB-1 and
PCB-2. Electronegative groups close to the regions of the
2-, 4-, 5-, 6-, and 6′-positions increased the binding free
energies between the PCBs and 2YFJ, as shown by the
higher binding free energy of PCB-30 than for PCB-7 or
PCB-10.
The logKOA increased on introduction of substituents
with electropositive groups at the 3′- and 6-positions or
electronegative groups at the 2-, 3-, 3′- and 5-positions
[
45
]. The logKOA and the binding free energies between
the PCBs and the nine types of BphA increased
simultaneously on introduction of substituents with
electropositive groups at the 3′-position of the B ring or
electronegative groups at the 2-position of the A ring.
In the electrostatic fields of 1ULJ, 2XSH, 2E4P, 3GZX,
3GZY, 1WQL, 2YFL, 2GBX, and KOA, introducing
electronegative groups at the 3-position of the A ring
increased the binding free energies and the KOA
simultaneously. In the electrostatic fields of 2YFJ, 1WQL, and
KOA, electronegative groups near the 5-position were
favorable for enhancing the binding free energies and the
KOA simultaneously. Therefore, using molecular
modeling, we proved that the binding free energies were
positively correlated with the migration abilities of the PCBs.
The BCF values increased on introduction of
substituents with electropositive groups at the 2-, 3′-, 5-,
and 6-positions or electronegative groups at the 3-, 4-,
and 5-positions of the A ring [
44
]. The BCF values of
the PCBs and the binding free energies between the
PCBs and BphA increased simultaneously on
introduction of substituents with electropositive groups at the
3′-position of the B ring or electronegative groups at
the 3-position of the A ring. In the electrostatic fields
of 1ULJ, 3GZX, 3GZY, 2GBX, and BCF, electropositive
groups were desirable at the 3′-positions of the B ring for
increases in the binding free energies and the BCF values.
In the electrostatic fields of 1WQL, 2YFJ, 2YFL, 2XSH,
2E4P and BCF, introducing electropositive groups to the
3′- and 5-positions increased the binding free energies
and BCF values simultaneously. In the electrostatic fields
of 1ULJ, 3GZX, 2XSH, 2E4P, and BCF, electronegative
groups close to the regions of the 3-position of the A ring
increased the binding free energies and the BCF values.
In the electrostatic fields of 2YFJ, 2GBX, 3GZY, and BCF,
electronegative groups near the 3- and 4-positions of
the A ring were favorable for enhancing the binding free
energies and the BCF values simultaneously. The
introduction of electronegative groups to the 3- and
5-positions of the A ring increased the BCF values and the
binding free energies between PCBs and 1WQL.
Electronegative groups were desirable at the 4- and
5-positions of the A ring to increase the BCF values of the PCBs
and the binding free energies between the PCBs and
2YFJ. Therefore, the binding free energies were positively
correlated with the bioaccumulation of PCBs, which was
proven by molecular modeling.
It was determined that electronegative groups close to
the regions of the 3-, 3′-, 5-, 6-, and 6′-positions increased
the t1/2 values of the PCBs [
45
]. The t1/2 values of the
PCBs and the binding free energies between the PCBs
and the nine types of BphA increased simultaneously on
introduction of substituents with electronegative groups
at the 3-position of the A ring. In the electrostatic fields
of 2YFL, 2GBX, and t1/2, electronegative groups were
desirable at 3-, 3′-, 6-, 6′-positions to increase the
binding free energies and t1/2 values. Electronegative groups
close to the regions of the 3- and 5-positions of the A
ring increased the t1/2 values and binding free energies
between the PCBs and 1WQL. Electronegative groups
near the 3-, 5-, 6-, and 6′-positions were favorable for
simultaneously enhancing the t1/2 values and the binding
free energies between PCBs and 2YFJ. Introducing
electronegative groups at the 3- and 6′-positions increased
the t1/2 values and the binding free energies between the
PCBs and 2E4P. Electronegative groups were desirable
at the 3-, 6-, and 6′-positions to increase the t1/2 values
of the PCBs and the binding free energies between the
PCBs and 3GZX. Molecular modeling showed that the
binding free energies were positively correlated with the
persistence of the PCBs.
Conclusions
The main research conclusions are as follows:
1. Among the nine types of BphA, 2GBX gives the
highest PCB degradation rate.
2. The binding free energy is highly significantly
correlated with the molecular weight, KOA, BCF, and t1/2
values, but is not significantly correlated with the
IC50 values.
3. The electrostatic descriptors play a more significant
role than steric descriptors and hydrophobic
descriptors. This is consistent with literature findings of the
electrostatic field as a major factor governing the
KOA, t1/2, and BCF values. Therefore, that the
influence of molecular structure on the binding free
energies, KOA, t1/2, and BCF values is consistent.
4. In an electrostatic field, introducing electropositive
groups in the 3-position or electronegative groups
in the 3′-position of a PCB significantly reduces the
binding free energies, molecular weights, KOA, BCF,
and t1/2 values. Therefore, the binding free energies
are correlated with the molecular weights, KOA, BCF,
and t1/2 values of the PCBs because of structural
features of the molecules.
Additional file
Additional file 1: Table S1. The binding free energies between nine
types of BphA and 209 PCBs.
Authors’ contributions
All authors read and approved the final manuscript.
Acknowledgements
Not applicable.
Competing interests
Not applicable.
Availability of data and materials
Not applicable.
Ethics approval and consent to participate
Not applicable.
Funding
Fundamental Research Funds for the Central Universities in 2013 (JB2013146)
and the Key Projects in the National Science & Technology Pillar Program in
the Eleventh 5‑ Year Plan Period (2008BAC43B01).
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in pub‑
lished maps and institutional affiliations.
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