Int.J.Curr.Microbiol.App.Sci (2015) 4(11): 740-752
ISSN: 2319-7706 Volume 4 Number 11 (2015) pp. 740-752
http://www.ijcmas.com
Original Research Article
Structural Modelling of -subunit of Ring-hydroxylating Dioxygenases
(RHDs) from Microbial Sources
Sumer Singh Meena, Sayan Chatterjee and Kamal Krishan Aggarwal*
University School of Biotechnology, Guru Gobind Singh Indraprastha University,
Sector-16C, Dwarka, New Delhi-110078, India
*Corresponding author
ABSTRACT
Keywords
RHDs, subunit,
Homology
modelling,
SWISSMODEL,
Ramachandran
plot
Polyaromatic hydrocarbons (PAHs) are ubiquitously spread and persistent organic
pollutants responsible for various disease and toxicity. Microbial species have been
found that can degrade PAHs utilizing ring-hydroxylating dioxygenases (RHDs)
also known as Rieske dioxygenases (RDOs) are multi-component catalysts.
Microbes can utilize PAHs as the carbon source, in the absence of simpler form of
carbon, for their survival. The enzyme has catalytic activity against PAHs and other
toxic elements. Thus, it has a very good potential as a bioremediation tool against
PAHs. Thus, study of its structure may reveal better understanding towards the in
situ degradation of PAHs. In the present study structures of -subunit protein
sequences of RHDs belonging to different microbial species were modelled.
National Centre for Biological Information (NCBI) database search retrieved
12,537 -subunit protein sequences of RHDs belonging to 213 microbial species.
As a representative of every species, only one bacterial sequence from every
species was chosen for the homology modelling. The structures of these proteins
were modeled using SWISS-MODEL. Ramachandran plot was used for the models
quality estimation.
Introduction
wide range of substrates. Naphthalene
dioxygenase (NDO) system in Pseudomonas
sp. has been shown to oxidize variety of
substrates (Resnick et al., 1996). There are
reports of toluene dioxygenase (TDOs) from
Pseudomonas putida possessing the ability
to catalyze reactions involving more than
200 different substrates (Boyd et al., 2001).
Martin et al. (2013) used the crystal
structure of oxygenase component of
Sphingomonas CHY-1 RHD as a template to
generate 3D models of the hybrid enzymes.
Ring-hydroxylating dioxygenases (RHDs)
are key enzymes involved in the
bioremediation of PAHs and other toxic
pollutants by catalyzing the initial step in the
degradation process (Peng et al., 2008; Seo
et al., 2009). RHDs are multicomponent
enzymes, consisting of an active site
containing component that is composed of
either n n or n (Kauppi et al., 1998).
Dioxygenases from different microbial
sources have been shown to oxidize the
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Int.J.Curr.Microbiol.App.Sci (2015) 4(11): 740-752
Kauppi et al. (1998) elucidated the detailed
mechanism of action of a hexamer ( 3 3)
naphthalene dioxygenase (NDO) which was
earlier purified and crystallized by Lee et al.
(1997). Kweon et al. (2010) proposed about
the relationship between structure and
function of RHOs (ring-hydroxylating
oxygenases) and suggested structural
characteristics of active sites of NidAB and
NidA3B3 affect substrate specificity in
Mycobacterium vanbaalenii PYR-1.
alignment the most overlapping longest
stretch containing amino acids sequences are
taken as the most representative sequences
from each of these species for further
analysis.
Model by homology modelling
The sequences were modeled by template
based homology modelling through SWISSMODEL
(http://swissmodel.expasy.org/)
online server. The selection of template was
based on sequence homology in microbial
species (Schwede et al., 2003). Three
models from each query sequence were
obtained from the SWISS-MODEL.
Earlier a phylogenetic analysis has revealed
that all RHDs are related on some kinetic
parameter(s)
probably
by
divergent
evolution from same roots and that exhibited
in their classification (Kweon et al., 2010;
Martin et al., 2013). Sequence analysis of
conserved regions of -subunits of ringhydroxylating systems indicated that RHDs
like toluene, benzene and naphthalene
dioxygenases share common ascendants
(Neidle et al., 1991). The structure
modelling of these enzymes may provide indepth understanding about the role and
molecular mechanism of these catalysts in
bioremediation processes that can help to
develop more efficient strategies. In the
present work, we have focused on creating
structure models of -subunits of RHDs
across the microbial species. Created models
were validated using Ramachandran plot.
Model quality evaluation
QMEAN (Benkert et al., 2008), a composite
scoring function for validating and
describing the quality of protein structures
were used to estimate the quality of the
generated models through the SWISS
MODEL server.
Along with QMEAN, Ramachandran plot
(Ramachandran and Sasisekharan, 1968)
was also generated through RAMPAGE
(Lovell et al., 2003) to get information and
feasibility of secondary structures of
proteins through combination of phi ( ) and
psi ( ) angles.
Materials and Methods
Results and Discussion
Retrieval of -subunit protein sequences
of bacterial RHDs from NCBI
Ring-hydroxylating
dioxygenases play
crucial role in PAHs degradation and have
been studied in several bacterial species i.e.
Mycobacterium sp., Pseudomonas sp.,
Rhodococcus sp., Bacillus sp. etc. (Seo et
al., 2009). The RHDs have been shown
earlier in different microbes which catalyze
broad range of substrates (Boyd et al.,
2001). Structural analysis of RHDs in
various species has been described by
Kauppi et al. (1998) which revealed about
The protein sequences of -subunit of
bacterial ring-hydroxylating dioxygenases
were downloaded from NCBI excluding
putative and hypothetical sequences whose
3D structures are not reported in PDB. The
search retrieved 12537 -subunit protein
sequences of RHDs belonging to 213
bacterial species from the NCBI database
(http://www.ncbi.nlm.nih.gov/).
After
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Int.J.Curr.Microbiol.App.Sci (2015) 4(11): 740-752
their compositions. The -subunit also
known as catalytic subunit of RHDs is the
substrate binding site, contains a
mononuclear non-heme iron, and a Rieske
[2Fe 2S] center which determine the
substrate specificity of these enzymes (Peng
et al., 2008).
level than structure. It is possible to identify
the 3D structure by visualizing at a molecule
with some sequence identity (Zvelebil and
Baum, 2007). The modelled protein
structures may give direction towards
analysis of protein functions, interactions,
antigenic behavior, and rational design of
proteins with increased stability (Krieger et
al., 2003; Xiang, 2006).
Earlier evolutionary studies have suggested
that molecules change faster on sequence
Table.1 Details of selected models with lowest outliers in Ramachandran plot* ( 1%)
SL.
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
NCBI ID/
gi no.
357417422
474986669
13094177
334142469
13094177
383081774
383081774
300391845
151091
121582739
121582739
357417422
13094177
407939877
407939877
357596241
414573712
414573712
146275509
146275509
146275509
300391845
402265185
402265185
402265185
151091
151091
121582739
357417422
357596241
384147692
357596241
383081774
47716763
47716763
333743595
407939877
518135638
359820923
431807777
494481824
Model
ID
1
2
3
1
2
1
2
3
2
1
3
2
1
1
2
2
1
2
1
2
3
2
1
2
3
1
3
2
3
1
2
3
3
1
3
1
3
1
2
2
3
GMQE
0.98
0.58
0.99
0.99
0.99
0.96
0.93
0.98
0.97
0.97
0.97
0.98
0.99
0.96
0.93
0.98
0.98
0.96
0.96
0.93
0.93
0.98
0.96
0.92
0.92
0.97
0.97
0.97
0.98
0.99
0.8
0.75
0.9
0.82
0.81
0.77
0.9
0.71
0.76
0.62
0.73
QMEAN4
-1.02
-9.82
0.06
-1.06
0.4
-1.08
-1.41
-1.68
-1.2
-1.02
-1.5
-1.19
-0.74
-1.08
-1.41
-1.14
-1.01
-1.01
-1.73
-2.96
-1.66
-1.67
-1.8
-3.06
-1.77
-1.64
-1.49
-1.41
-2.02
-1.5
-1.52
-3.84
-1.73
-2.79
-3.13
-4.78
-1.73
-5.75
-2.99
-6.67
-3.5
Outliers
in R-plot*
0 (0.0%)
1 (0.1%)
1 (0.3%)
2 (0.4%)
5 (0.4%)
2 (0.5%)
2 (0.5%)
2 (0.5%)
2 (0.5%)
2 (0.5%)
2 (0.5%)
2 (0.5%)
2 (0.5%)
2 (0.5%)
2 (0.5%)
2 (0.5%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
3 (0.7%)
9 (0.8%)
3 (0.8%)
4 (0.9%)
2 (0.9%)
2 (0.9%)
4 (0.9%)
4 (0.9%)
4 (1.0%)
4 (1.0%)
10 (1.0%)
4 (1.0%)
742
% outliers
in R-plot*
0
0.1
0.3
0.4
0.4
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.8
0.8
0.9
0.9
0.9
0.9
0.9
1
1
1
1
Seq.
Identity
87.7
24.93
99.22
94.71
99.22
84.96
80.27
89.31
95.85
90.79
90.57
87.47
99.22
84.96
80.27
90.6
93.11
91.88
79.52
78.85
79.69
89.46
78.63
78.19
78.81
96.07
95.85
90.57
87.47
99.04
64.88
42.86
77.21
58.74
58.74
50.7
77.21
46.44
56.56
19.62
45.97
Seq.
Similarity
0.59
0.32
0.62
0.61
0.62
0.59
0.57
0.59
0.62
0.6
0.6
0.59
0.62
0.59
0.57
0.6
0.61
0.6
0.57
0.56
0.57
0.59
0.57
0.56
0.57
0.62
0.62
0.6
0.59
0.63
0.51
0.42
0.56
0.49
0.49
0.45
0.56
0.43
0.47
0.3
0.44
Coverage
1
0.88
1
1
1
0.99
0.98
1
1
1
1
1
1
0.99
0.98
1
0.96
1
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1
1
1
1
1
0.99
0.96
0.99
1
1
0.95
0.99
0.9
0.9
0.96
0.9
Resolution
2.29Å
2.20Å
1.85Å
1.70Å
1.95Å
1.58Å
2.20Å
1.70Å
2.15Å
2.29Å
2.42Å
2.15Å
1.95Å
1.58Å
2.20Å
1.70Å
2.00Å
2.00Å
1.70Å
1.85Å
1.70Å
1.50Å
1.70Å
1.85Å
1.70Å
2.29Å
2.42Å
2.15Å
2.42Å
1.85Å
2.30Å
1.70Å
2.29Å
1.70Å
1.50Å
1.70Å
2.29Å
2.00Å
2.00Å
2.10Å
2.29Å
Int.J.Curr.Microbiol.App.Sci (2015) 4(11): 740-752
Table.2 The best predicted structures from each of the top qualified organisms
SL.
No.
1
NCBI ID/gi no.
Organisms Names
357417422
2
474986669
3
4
5
6
7
13094177
334142469
383081774
300391845
151091
8
121582739
9
10
407939877
357596241
11
12
414573712
146275509
13
14
15
16
17
18
19
20
402265185
384147692
47716763
333743595
518135638
359820923
431807777
494481824
Pseudoxanthomonas spadix
BD-a59
Streptomyces hygroscopicus sub
sp. jinggangensis TL01
Pseudomonas resinovorans
Novosphingobium sp.PP1Y
Comamonas testosteroni
Pseudomonas stutzeri
Pseudomonas pseudoalcaligenes
KF707
Polaromonas naphthalenivorans
CJ2
Acidovorax sp. KKS102
Novosphingobium
pentaromativorans US6-1
Rhodococcus opacus M213
Novosphingobium
aromaticivorans DSM12444
Sphingomonas sp. LH128
Amycolatopsis mediterranei S699
Stenotrophomonas maltophilia
Delftia sp.Cs1-4
Mycobacterium avium
Mycobacteriumrhodesiae NBB3
Brachyspira pilosicoli P43/6/78
Arthrobacter gangotriensis
Outliers
in R-plot*
Resolution
0 (0.0%)
2.29Å
1 (0.1%)
2.20Å
1 (0.3%)
2 (0.4%)
2 (0.5%)
2 (0.5%)
1.85Å
1.70Å
1.58Å
1.70Å
2 (0.5%)
2.15Å
2 (0.5%)
2.29Å
2 (0.5%)
1.58Å
2 (0.5%)
1.70Å
3 (0.7%)
2.00Å
3 (0.7%)
1.70Å
3 (0.7%)
9 (0.8%)
2 (0.9%)
4 (0.9%)
4 (1.0%)
4 (1.0%)
10 (1.0%)
4 (1.0%)
1.70Å
2.30Å
1.70Å
1.70Å
2.00Å
2.00Å
2.10Å
2.29Å
Figure.1 Bacterial RHD -subunit sequences available at NCBI database
743
Int.J.Curr.Microbiol.App.Sci (2015) 4(11): 740-752
In the present study, we retrieved 12537
sequences of -subunits of RHDs from 213
microbial species, excluding those whose
structures were previously available in
RSCB Protein Data Bank (PDB). One
sequence from each microbial species was
selected to model the structure. A total 639
models were created from 213 species using
SWISS-MODEL. On the basis of percent
outliers in Ramachandran plot, models were
further refined into 41 good quality structure
models (supplementary data), the details of
these models have been summarized in table
1.
Thus, the study presented herein provides
the first account of constructing homology
models for
subunit of microbial RHD
variants. The homology modelling is the
best way to predict and understand protein s
structure in the lack of crystal structures
(Janairo and Janairo, 2012).
These
homology models may serve as tools to
understand the observed kinetic and
catalytic behavior of the RHDs through
comparative docking calculations.
It may also reveal useful information in the
better understanding of factors involved in
enhanced or the reduced activity of the
RHDs and their potential role in the
bioremediation of PAHs. Role of these
structure models can be determined by
analyzing their thermostability and other
related parameters. Interaction of catalytic
subunits can be established by employing
docking on selected models. Docking
studies of these models can create a better
picture of interaction of catalytic subunits
and their role in a particular process e.g.
bioremediation strategies.
Threshold level for percent outliers was set
at 1%, so that good structure can be
classified. In another study, Preenon Bagchi
et al. (2009) have shown allowable
structures at 1.2% outliers. Thus, a more
stringent threshold (1%) was adopted in the
present study. Further analysis of these
structure models indicated that these models
belong to 20 microbial species (Table 2).
The best model that obtained for
Pseudoxanthomonas spadix BD-a59 (gi no.
357417422) has no outliers and can be
considered as good as a crystallography
structure for further analysis (Table 2).
Acknowledgments
SSM acknowledges the University Grant
Commission (UGC), New Delhi, India for
awarding research fellowship.
These models have been shown in
supplementary data corresponding to their
serial number in table 1. Each structure
evaluation by Ramachandran plot has also
been shown in supplementary data for all
these 41 predicted structures.
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Supplementary Data:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
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16
17
18
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22
23
24
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31
32
33
34
35
36
37
38
39
40
41
Figure.S1 Selected structure models of -subunit of bacterial RHDs
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Int.J.Curr.Microbiol.App.Sci (2015) 4(11): 740-752
1
2
3
4
5
6
7
8
9
10
11
12
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15
13
14
16
17
18
19
20
21
22
23
24
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25
26
27
28
29
30
31
32
33
34
35
36
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37
38
40
41
39
Figure.S2 Ramachandran plots of selected structure models of -subunit of bacterial RHDs
752