structural modelling of -subunit of ring-hydroxylating ... singh meena, et al.pdfring-hydroxylating...

Int.J.Curr.Microbiol.App.Sci (2015) 4(11): 740-752

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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

A B S T R A C T

Introduction

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

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.

ISSN: 2319-7706 Volume 4 Number 11 (2015) pp. 740-752 http://www.ijcmas.com

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.

K e y w o r d s

RHDs, -subunit, Homology modelling, SWISS-MODEL, Ramachandran plot

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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.

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 ring-hydroxylating 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 in-depth 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.

Materials and Methods

Retrieval of -subunit protein sequences of bacterial RHDs from NCBI

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

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 SWISS-MODEL (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.

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.

Results and Discussion

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

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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).

Earlier evolutionary studies have suggested that molecules change faster on sequence

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).

Table.1 Details of selected models with lowest outliers in Ramachandran plot* ( 1%)

SL. No.

NCBI ID/ gi no.

Model ID GMQE QMEAN4

Outliers in R-plot*

% outliers in R-plot*

Seq. Identity

Seq. Similarity Coverage Resolution

1 357417422 1 0.98 -1.02 0 (0.0%) 0 87.7 0.59 1 2.29Å 2 474986669 2 0.58 -9.82 1 (0.1%) 0.1 24.93 0.32 0.88 2.20Å 3 13094177 3 0.99 0.06 1 (0.3%) 0.3 99.22 0.62 1 1.85Å 4 334142469 1 0.99 -1.06 2 (0.4%) 0.4 94.71 0.61 1 1.70Å 5 13094177 2 0.99 0.4 5 (0.4%) 0.4 99.22 0.62 1 1.95Å 6 383081774 1 0.96 -1.08 2 (0.5%) 0.5 84.96 0.59 0.99 1.58Å 7 383081774 2 0.93 -1.41 2 (0.5%) 0.5 80.27 0.57 0.98 2.20Å 8 300391845 3 0.98 -1.68 2 (0.5%) 0.5 89.31 0.59 1 1.70Å 9 151091 2 0.97 -1.2 2 (0.5%) 0.5 95.85 0.62 1 2.15Å 10 121582739 1 0.97 -1.02 2 (0.5%) 0.5 90.79 0.6 1 2.29Å 11 121582739 3 0.97 -1.5 2 (0.5%) 0.5 90.57 0.6 1 2.42Å 12 357417422 2 0.98 -1.19 2 (0.5%) 0.5 87.47 0.59 1 2.15Å 13 13094177 1 0.99 -0.74 2 (0.5%) 0.5 99.22 0.62 1 1.95Å 14 407939877 1 0.96 -1.08 2 (0.5%) 0.5 84.96 0.59 0.99 1.58Å 15 407939877 2 0.93 -1.41 2 (0.5%) 0.5 80.27 0.57 0.98 2.20Å 16 357596241 2 0.98 -1.14 2 (0.5%) 0.5 90.6 0.6 1 1.70Å 17 414573712 1 0.98 -1.01 3 (0.7%) 0.7 93.11 0.61 0.96 2.00Å 18 414573712 2 0.96 -1.01 3 (0.7%) 0.7 91.88 0.6 1 2.00Å 19 146275509 1 0.96 -1.73 3 (0.7%) 0.7 79.52 0.57 0.99 1.70Å 20 146275509 2 0.93 -2.96 3 (0.7%) 0.7 78.85 0.56 0.99 1.85Å 21 146275509 3 0.93 -1.66 3 (0.7%) 0.7 79.69 0.57 0.99 1.70Å 22 300391845 2 0.98 -1.67 3 (0.7%) 0.7 89.46 0.59 0.99 1.50Å 23 402265185 1 0.96 -1.8 3 (0.7%) 0.7 78.63 0.57 0.99 1.70Å 24 402265185 2 0.92 -3.06 3 (0.7%) 0.7 78.19 0.56 0.99 1.85Å 25 402265185 3 0.92 -1.77 3 (0.7%) 0.7 78.81 0.57 0.99 1.70Å 26 151091 1 0.97 -1.64 3 (0.7%) 0.7 96.07 0.62 1 2.29Å 27 151091 3 0.97 -1.49 3 (0.7%) 0.7 95.85 0.62 1 2.42Å 28 121582739 2 0.97 -1.41 3 (0.7%) 0.7 90.57 0.6 1 2.15Å 29 357417422 3 0.98 -2.02 3 (0.7%) 0.7 87.47 0.59 1 2.42Å 30 357596241 1 0.99 -1.5 3 (0.7%) 0.7 99.04 0.63 1 1.85Å 31 384147692 2 0.8 -1.52 9 (0.8%) 0.8 64.88 0.51 0.99 2.30Å 32 357596241 3 0.75 -3.84 3 (0.8%) 0.8 42.86 0.42 0.96 1.70Å 33 383081774 3 0.9 -1.73 4 (0.9%) 0.9 77.21 0.56 0.99 2.29Å 34 47716763 1 0.82 -2.79 2 (0.9%) 0.9 58.74 0.49 1 1.70Å 35 47716763 3 0.81 -3.13 2 (0.9%) 0.9 58.74 0.49 1 1.50Å 36 333743595 1 0.77 -4.78 4 (0.9%) 0.9 50.7 0.45 0.95 1.70Å 37 407939877 3 0.9 -1.73 4 (0.9%) 0.9 77.21 0.56 0.99 2.29Å 38 518135638 1 0.71 -5.75 4 (1.0%) 1 46.44 0.43 0.9 2.00Å 39 359820923 2 0.76 -2.99 4 (1.0%) 1 56.56 0.47 0.9 2.00Å 40 431807777 2 0.62 -6.67 10 (1.0%) 1 19.62 0.3 0.96 2.10Å 41 494481824 3 0.73 -3.5 4 (1.0%) 1 45.97 0.44 0.9 2.29Å


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Table.2 The best predicted structures from each of the top qualified organisms

Figure.1

Bacterial RHD -subunit sequences available at NCBI database

SL. No.

NCBI ID/gi no. Organisms Names Outliers in R-plot*

Resolution

1 357417422 Pseudoxanthomonas spadix BD-a59 0 (0.0%) 2.29Å

2 474986669 Streptomyces hygroscopicus sub sp. jinggangensis TL01 1 (0.1%) 2.20Å

3 13094177 Pseudomonas resinovorans 1 (0.3%) 1.85Å 4 334142469 Novosphingobium sp.PP1Y 2 (0.4%) 1.70Å 5 383081774 Comamonas testosteroni 2 (0.5%) 1.58Å 6 300391845 Pseudomonas stutzeri 2 (0.5%) 1.70Å 7 151091 Pseudomonas pseudoalcaligenes

KF707 2 (0.5%) 2.15Å

8 121582739 Polaromonas naphthalenivorans CJ2 2 (0.5%) 2.29Å

9 407939877 Acidovorax sp. KKS102 2 (0.5%) 1.58Å 10 357596241 Novosphingobium

pentaromativorans US6-1 2 (0.5%) 1.70Å

11 414573712 Rhodococcus opacus M213 3 (0.7%) 2.00Å 12 146275509 Novosphingobium

aromaticivorans DSM12444 3 (0.7%) 1.70Å

13 402265185 Sphingomonas sp. LH128 3 (0.7%) 1.70Å 14 384147692 Amycolatopsis mediterranei S699 9 (0.8%) 2.30Å 15 47716763 Stenotrophomonas maltophilia 2 (0.9%) 1.70Å 16 333743595 Delftia sp.Cs1-4 4 (0.9%) 1.70Å 17 518135638 Mycobacterium avium 4 (1.0%) 2.00Å 18 359820923 Mycobacteriumrhodesiae NBB3 4 (1.0%) 2.00Å 19 431807777 Brachyspira pilosicoli P43/6/78 10 (1.0%) 2.10Å 20 494481824 Arthrobacter gangotriensis 4 (1.0%) 2.29Å


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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.

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).

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.

The distribution of amino acid sequences of the alpha subunit of RHDs reported in bacteria as available in the NCBI database shows that the major share has been that of the Mycobacterium, Burkholderia, E. coli, Acinetobacter, Pseudomonas, Sphingomonas, Rhodococcus, Klebsiella, and more than one fourth belongs to uncultured & other unclassified species (Figure 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.

Acknowledgments

SSM acknowledges the University Grant Commission (UGC), New Delhi, India for awarding research fellowship.

Reference

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Figure.S2 Ramachandran plots of selected structure models of -subunit of bacterial RHDs

structural modelling of -subunit of ring-hydroxylating ... singh meena, et al.pdfring-hydroxylating...

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