Molecular interaction of the triazole fungicide propiconazole with homology modelled superoxide dismutase and catalase

  • Praveen SataputeEmail author
  • Rajeshwari D. Sanakal
  • Sikandar I. Mulla
  • Basappa Kaliwal
Original Article


To understand the plasmid mediated biodegradation of propiconazole and enzymatic antioxidant activity, the plasmid cured PS-4C strain was utilized against the propiconazole for its dissolution in the liquid medium, further, the molecular docking studies against the Pseudomonas aeruginosa superoxide dismutase (SOD) as well as catalase (CAT) was undertaken. An acridine orange based LD50 concentration of the propiconazole was found at 50 µg ml−1 and hence the plasmid of PS-4C strain was cured at this dose. Homology modeling using Swiss modeler was applied to generate 3D structure of both SOD and CAT. Active sites were predicted using CastP server and molecular docking was performed by AutodockVina program and thereby calculated binding free energy. Ligand docked against the SOD and CAT enzymes was found to bind with strong hydrophobic interaction. Propiconazole showed strong binding affinity with CAT compared to SOD. Thus, propiconazole resistant plasmid degenerated bacterium PS-4C strain can be a potent candidate for the safer remediation of pollutants and the conformation of propiconazole exploits the interactive geometry along with the molecule size sufficient for spanning the two enzymes to which they will bind making it a good starting point for designing library of antioxidants.


Propiconazole Catalase Superoxide dismutase Homology modeling Docking 


Propiconazole is a triazole foliar antimycotic agent, employed in the agriculture fields. The annual intake of this antimycotic agent is 7373 g a.i./ha. It’s amongst the leading triazole antimycotic agent within the agriculture field (Satapute and Kaliwal 2016a; Satapute et al. 2019a). On the other hand, microorganisms are the potent biological candidates which are easily available and were used for the bio-transformation/degradation of pesticides (Mulla et al. 2017). Soil borne microorganisms are most promising biological agents; they themselves adapt to adverse stress conditions and gain the capability of survivability under many contaminated sites (Satapute and Kaliwal 2018; Satapute et al. 2019b). The application of pesticides can provide the sufficient amount of carbon sources for the growth of adapted and resistant soil microorganisms (Araya and Lakhi 2004), and thus establishing the healthy way for the degradation of these harmful chemicals (Satapute and Kaliwal 2016a). Generally, during degradation process, the microorganisms and their enzymes can play a vital role in the breakdown of environmental contaminants (toxic/hazardous) into non-toxic and simple molecules (Mulla et al. 2016a, b, 2018, 2019; Singh et al. 2016).

Most of organism(s) do not possess the capability of dissolution of toxic compounds (including pesticides). However, some microorganisms can initially adopt themselve and thereby start to utilize such chemicals (Javaid et al. 2016). Additionally, the catabolic genes have the ability to reduce substrate specificity or inducer specificity of prevailing genes or microbes can gain the specific enzyme(s) or gene(s) by exchange of genetic information (Deng et al. 2018; van der Meer 1994). Eventually, in the existing population of microorganisms, it is predicted that there are adequate beneficial modifications in the prevailing genes, either situated on plasmids or identical elements to allow them to utilize the toxic compounds/pesticides (Chen and Alexander 1989; Munita and Arias 2016). Moreover, such modification will also give the discriminatory benefit to microbes. Hence, magnifying the new traits becomes the part of the genome in a population of microbes and this condition will favor the adaptation of pre-existing genes to transform toxic compounds into non-toxic forms or simpler products (Aislabie and Lloyd-Jones 1995).

Various types of genes which are involved in the metabolism of toxic compounds have been identified (Talwar et al. 2014). Additionally, cytochrome P450 monooxygenase which constitutes a huge family of enzymes (heme thiolates) capable of degradation of a wide range of toxic compounds (pesticides and heavy metals) are extremely well characterized in microorganisms (especially bacteria) (Degtyarenko 1995; Satapute and Kaliwal 2016b). Superoxide dismutase (SOD) gene catalyzes the separation of superoxide into oxygen and hydrogen peroxide. Thus, they are important antioxidants, protecting the cells in presence of oxygen (Fukai and Ushio-Fukai 2011). Catalases endorse the disproportionation of H2O2, cover heme as a prosthetic group and are homotetramers, while peroxidases use H2O2 to oxidize a large number of toxic compounds. The combination catalase-peroxidases carry out varying actions (Milgrom 2016). The bacterial catalase (CAT)-peroxidases typically contains 726–753 amino acids per subunit involving two exceedingly homologous halves, both of the bond have important amino acids, identical with eukaryotic monomeric peroxidases, including cytochrome peroxidase (Kamachi et al. 2014; Zou et al. 1999).

Molecular docking is a technique used to forecast the favored location of one molecule to another when bound to each other to form a steady complex. Information of the favored alignment may be used to forecast the strong point of binding affinity among two molecules using scoring functions (Latha and Saddala 2017; Lengauer and Rarey 1996). The newer and less expensive molecules that objectify the dynamic destinations of the target ought to be outlined with a specific end goal to expand the moderateness of molecule (Kurjogi et al. 2018). The molecular docking study of enzyme-ligand interaction was determined in the present study. Sequence of both SOD and CAT were selected for the homology modeling to predict the three dimensional structure of proteins (SOD and CAT) and propiconazole checked for the binding affinity with modeled protein structure. The toxicity assessment isn’t clearly studied and propiconazole was found to be toxic compound which has the ability to persist in the soil up to 96–575 days (Technical Information Bulletin for Propiconazole Fungicide). Hence, the present study was undertaken to isolate and identify the SOD and CAT genes from the Pseudomonas aeruginosa PS-4 strain and molecular docking was performed to predict the possible binding affinity of SOD and CAT proteins with ligand (propiconazole).

Materials and methods

Chemical, media and microorganism

A purity of 94% propiconazole was obtained from Nagarjuna Agrichem Co., Ltd. Srikakullam, Andra Pradesh, India. Remaining chemicals being used in the experimental study were of higher analytical and HPLC grade. The Seubert’s mineral salts medium (MSM) (Hoskeri et al. 2014; Mulla et al. 2017) containing propiconazole (10 µg l−1) as a sole carbon source was used. The bacterium, P. aeruginosa strain (PS-4) was previously isolated in our laboratory and was selected based on its capability to degrade propiconazole (Satapute and Kaliwal 2016b).

LD 50 value determination

A loopful of P. aeruginosa PS-4 strain was inoculated into Erlenmeyer flask (250 ml) containing 50 ml of sterile nutrient broth (NB) and incubated at 37 °C under shaking conditions at 120 rpm for 18 h and from this 100 µl of freshly grown culture was transferred into sterilized glass tubes (25 ml) containing 5 ml of NB with various concentrations of acridine orange dye (5–50 µg ml−1). The absence of acridine orange dye in a test tube served as a control. Further, all test tubes were incubated for 24 h at 37 °C (optimum temperature) and the growth of bacterial cells was determined at 660 nm. The obtained cell density values were compared with treated samples and control. Later, the final concentration of LD50 value was measured.

Plasmid curing

Plasmid curing is a method used to identify the plasmid in 5–6 generations of bacterial culture by adding LD50 concentration. The sterile NB (25 ml) was utilized to grow the test organism at 37 °C for 18 h. LD50 concentrations of acridine orange dye were prepared in five tubes containing NB and 18 h old test organism was inoculated into tube no. 1. The control (absence of acridine orange dye) was also monitored and incubated at 37 °C for 24 h. After the incubation period 1% of culture medium from test tube no. 1 was aseptically transferred to test tube no. 2 and again incubated for 24 h. Similarly, this repeated inoculation of test organism was done till the plasmid curing was undergone. Finally, the observation of plasmid (presence/absence) was investigated with the help of agarose gel electrophoresis by extracting the plasmid with utilization of miniprep plasmid isolation kit at all the tested generations.

Biodegradation of propiconazole by plasmid cured strain

The MSM amended with propiconazole (10 µg l−1) as a sole substrate and energy source was used to analyze the degradation ability of plasmid cured PS-4C strain. Extraction and HPLC analysis of propiconazole was done according to previously published method (Satapute and Kaliwal 2016b).

Superoxide dismutase (SOD) and catalase (CAT) genes identification in P. aeruginosa PS-4 strain

The genomic DNA of P. aeruginosa PS-4 strain was isolated and amplified with SOD specific gene by using suitable primers F: 5′-GCTGCCTTACGAAAAGAACG-3′ and R: 5′-TTCCTGAGGGTAGACGATGG-3′ and this primer was produced from the locus of the SOD gene from P. aeruginosa (Accession number CP015117) with the molecular length of 750 bp. A 25 µl of PCR reaction mixture consisted of 2 × PCR mix (22.5 µl), 0.5 µl each lagging and leading primer (final conc. 0.5 µM) and 1.5 µl of desired template DNA to be amplified. PCR reaction took place in following order. Initial denaturation for 2 min followed by the denaturation at 94 °C for 15 s, followed by 25 cycles (94 °C for 15 s, 61 °C for 30 s and 72 °C for 40 s) and final extension at 72 °C for 7 min. Similarly, The DNA of PS-4 strain mixed with the following primers F: 5′-CAACCAGAACTCGCAGACC-3′ and R: 5′-ACATCTGGGTATCGGCGTAG-3′ to identify the CAT gene in the strain and the primer was disigned from the locus of the catalase gene of P. aeruginosa (Accession number AP012280) with approximate size of 1100 bp. A PCR blend comprises of 25 µl with 2 × PCR blend (22.5 µl), 0.5 µl each forward and reverse primers (last conc. 0.5 µM) and 1.5 µl of test DNA to be intensified. PCR reaction occurs in following. Beginning denaturation of 2 min took after by the denaturation at 94 °C for 15 s, trailed by 20 cycles (94 °C for 15 s, 61 °C for 30 s and 72 °C for 1 min) and last expansion at 72 °C for 7 min. The final amplified products of SOD and CAT were obtained on 1% agarose gel using gel documentation and calculated using a 3 kb ladder and the sequencing was carried out using applied biosystem 3010XL capillary sequencer. The ABI’s Big Dye® Terminator v3.1 sequencing chemistry was used. In order to carry out Pairwise/Multiple Sequence Alignment—The ClustalW2 Tool ( was used. Further, the phylogeny of both the genes was drawn utilizing MEGA 7.0 software with the neighbor joining method using pairwise/multiple sequence alignment (Tamura et al. 2013).


The resulting sequences of SOD and CAT (P. aeruginosa) were used for further in silico analysis. The sequences were submitted to SBASE server ( for domain prediction. The predicted domain was searched to find out the related protein structure to be used as a template by the BLAST (Basic Local Alignment Search Tool) program against Protein Data Bank PDB. A sequence that shows maximum identity with high score and less E-value was aligned and used as a template to build a 3D model for CAT and SOD.

Generation of 3-D structure using homology modeling

The modeling of the 3D structure of protein was performed using SWISS-MODEL (Arnold et al. 2006) (, the built model was visualized under molecular visualization software. Structural validation of protein was done using RAMPAGE (Lovell et al. 2003) (, phi–psi torsion angles for all the residues in structure were plotted in the Ramachandran Plot at RAMPAGE.

Active site identification

Active site of CAT and SOD were identified using CASTP server (Dundas et al. 2006). CAST identifies and measures pockets and pocket mouth openings, as well as cavities.

Protein–ligand interaction

Structure of the ligand propiconazole was retrieved from PubChem (CID: 43234) in the sdf format and was converted into pdb file using the Open Babel software.

The docking site for ligand to homology modelled structure of SOD and CAT were defined using PyRX0.8 interface. A grid box was created with 40 × 53 × 40 points with grid centre 41.106, 22.871, 49.954 for SOD. To study the interaction of ligand with CAT, the grid box of 61 × 59 × 47, grid centre 12.661, − 17.839, 54.590 was selected. The AutodockVina was set with exhaustiveness of 8 for both the proteins. Only the best pose (the one with the lowest binding energy) was considered for the ligand. Visualization of the results was made with the help of the autodocktools software suite (ADT) (Morris et al. 2009) and PyMOL (DeLano 2002).


LD 50 concentration and plasmid curing of P. aeruginosa PS-4

The acridine orange dye based LD50 concentration of P. aeruginosa PS-4 strain was investigated in the growth media. The concentration of LD50 value of the PS-4 strain of 50 µg ml−1 dose exhibits the growth of 0.217 at 660 nm which was half of the cell growth of the control (0.496). Further, LD50 concentration was opted for the plasmid curing of PS-4 strain up to fifth generation. It was noticed that, up to third generation of plasmid curing process four plasmids were found at approximately 23.13 kb, 6.55 kb, 4.36 kb and 3.41 kb, respectively. The plasmid hampering was observed after the third generation and later in fifth generation it was confirmed that the plasmid was completely cured and disappeared (Fig. 1).
Fig. 1

The effect of curing agent acridine orange on P. aeruginosa PS4 strain; lane 1: marker, lane 2: control, lane 3 and 4: showing the presence of plasmids in the first and second generation, lane 5 and 6: plasmid curing with hampered plasmid at the third and fourth generation, lane 7: complete curing of plasmid at fifth generation

Biodegradation propiconazole by plasmid cured strain of P. aeruginosa PS-4C

The plasmid cured PS-4C strain exhibits luxurious growth in the MSM medium and it also utilized 7.47 µg l−1 with the initial concentration of 10 µg l−1 of propiconazole (Fig. 2). The cell free culture filtrate of PS-4C strain showed various peaks during HPLC analysis which indicates the formation of intermediates of propiconazole. Additionally, propiconazole peak was significantly reduced in the area percentage, representing the reduction in the concentration (Fig. S1, Supplementary Information).
Fig. 2

Bacterial growth and degradation of propiconazole by the plasmid cured strain of P. aeruginosa PS-4 in mineral salt medium supplemented with propiconazole (10 µg l−1) as sole carbon source. All the data are mean of three independent replicates ± standard deviation (SD)

SOD gene identification in P. aeruginosa PS-4

The identified SOD gene (PS-4 strain) is approximately 700 bp (Fig. S2). BLAST analysis shows similar homology nucleotide sequence in other strains of P. aeruginosa and also phylogenetic tree of gene segment shows relatively similar to P. aeruginosa strain F9670 and ATCC 27853 complete genome (Fig. 3).
Fig. 3

Phylogenetic tree of superoxide dismutase gene isolated from the P. aeruginosa (PS-4) and related sequences assembled through neighbour joining method by using MEGA 7.0 software

CAT gene identification in P. aeruginosa PS-4

The CAT gene in P. aeruginosa PS-4 was confirmed with product size of approximately 1050 bp (Fig. S2). BLAST analysis shows the sequence of amplified gene has high homology with CAT gene identified in other strains of P. aeruginosa that were available in the NCBI server and the resulting phylogenetic evidence showed that the gene segment (amplicon) was most closely related to P. aeruginosa strain NCGM 1984, complete genome (Fig. 4). The sequence (CAT) was deposited to the NCBI with accession number of MN194197.
Fig. 4

Phylogeny of catalase gene isolated from P. aeruginosa PS-4 constructed by MEGA 7.0 with bootstrap values at nodes

Homology modeling of SOD and CAT gene

The BLAST search for SOD against structural database has high level of sequence identity (of 93%) with 1DTO (Chain A, Crystal Structure of recombinant SOD) which was used as template for structure prediction in homology modeling. For CAT, 1M7S (Chain A, Crystal Structure of catalase from Pseudomonas) has 88% sequence identity at the structurally conserved regions (SCRs) and was used as template for homology modeling by applying superimposition of the two structures. Coordinates from the templates (1DTO A) for SOD and (1M7S A) for CAT to the SCRs, structurally variable regions (SVRs), N-termini and C-termini were assigned to the target sequence based on the satisfaction of spatial restraints. From the final stable 3D structures of SOD and CAT obtained with the help of Rasmol, it is evident that SOD has 11 helices and 15 strands (Fig. 5a), and CAT has 11 helices and 26 strands (Fig. 5b).
Fig. 5

Final refined homologue 3D structures of SOD (a) and CAT (b)

Validation of domain

Manganese and iron superoxide dismutase was found to be the domain of SOD gene and catalase, N-terminal was found as domain for the CAT gene. After the refinement process, validation of the model was carried out using Ramachandran plot calculations computed with the PROCHECK program. The ϕ and ψ distributions of the Ramachandran plots of non-glycine, non-proline residues are summarized. There are 96.8% of the residues of SOD in favored and 3.2% in allowed regions (Fig. 6a). On the other hand, 96.7% of the residues of catalase were in favored, 2.9% allowed regions and Ala269 was located in outlier region (Fig. 6b).
Fig. 6

Ramachandran plot of SOD (a) and CAT (b) genes

After building the final model, the possible binding sites of SOD and CAT were searched using CASTp server. It was identified that residues Asp8, Gln9, Ser10, Leu15, Leu24, His28, Trp78, Leu81, Ser82 and Pro83were in active site for SOD (Fig. S3A). Active site residues for CAT were identified as Arg2, Val3, Val4, His5, Arg41, Ser43, Val45, Val46, His47, Arg56, Asp57, Pro58, Gly60, Phe61, Ala62, Val75, Gly76, Asn77, Phe82, Phe83, Ile84, Arg85, Asp86, Ala87, Phe90, Met93, Val94, Phe97, Lys98, Arg111, Leu128, Ser145, Val146, His147, Ala148, Tyr149, Lys162, Ser225, Leu226, Asp227, Ala228, Val259, Met261, Pro263, Leu266, Ser272, Pro273 and Cys274 (Fig. S3B).

The in silico interaction of propiconazole with homology modeled structures showed the least binding energy of − 6.7 kcal mol−1 with CAT enzyme than SOD (of − 5.9 kcal mol−1). The conformation with least binding energy and most stability based on cluster analysis was taken for docking analysis. In the auto dock predicted interaction; it shows the interaction as hydrophobic (Figs. 7 and 8) for SOD and CAT, respectively.
Fig. 7

Molecular docking of SOD with propiconazole

Fig. 8

Molecular docking of CAT with propiconazole


The presence of particular genes and their related enzymes actively participate in the degradation of toxic molecules. Hence, in cell, genes play a major role in converting toxic molecule into non-toxic molecules. Current experiment shows PS-4 strain containing SOD and CAT genes having estimated size of 700 bp and 1050 bp, respectively. The structure of SOD and CAT gene is unavailable in the PDB database for the specific sequences; both of these enzymes are created using homology modeling. The modeled structure is used to study the docking effect on ligand propiconazole. Under the in vitro conditions it was proved that propiconazole was successfully degraded by P. aeruginosa PS-4C strain and it also shows propiconazole substrate helps for its growth. To know the degradation mechanism of propiconazole, SOD and CAT genes were studied in molecular level through in silico approach.

Plasmid cured strain (PS-4C) was well distinguished for having the ability to degrade propiconazole in the current study; it indicates that chromosomal genes are directly responsible for degrading the propiconazole by PS-4 strain and only a few bacteria were isolated which carry an extra chromosomal DNA. Contradictorily, Deshpande et al. (2001) summarized that, the organophosphate insecticide dimethoate degrading P. aeruginosa MCMB- 427 was found unable to utilize the dimethoate after the plasmid curing process at given conditions, additionally, the study concludes that, the plasmid pDMD427 was found active biological candidate in the biodegradation process of dimethoate. Interestingly, plasmid removed P. aeruginosa sp. was found to be an excellent agent to utilize cadmium, chromium and lead (Raja and Selvam 2009). Likewise, the biotransformation of fenitrothion by Burkholderia sp. strain NF100 was initiated and successfully achieved with the expression of plasmids (pNF1 and pNF2) (Hayatsu et al. 2000). Moreover, the biodegradation of imidacloprid was hindered by the absence of plasmid in the Brevundimonas sp. Interestingly, the dissolution of imidacloprid was observed up to 50% lesser than the uncured bacterial strain, indicating that biodegradation of imidacloprid was achieved by both chromosomal and extra chromosomal genes (Shetti and Kaliwal 2012). Thus, the use of P. aeruginosa PS-4 strain for biodegradation of hazardous chemicals to make toxic free environment is possible.

SOD is a unique contender in the degradation process of propiconazole dissolution in the liquid medium. Previously, numbers of researcher have identified the SOD gene from P. aeruginosa. For instance, Norman et al. (Norman et al. 2004) have investigated the impact of pyocyanin on crude oil degradation. They reported that the SOD activity was found to be major cause for the crude oil concentration decrease and it was also stated that P. aeruginosa is a potent candidate and it was isolated from different contaminated sites. Moreover, P. aeruginosa BCH strain isolated from the dye contaminated soil expressed the oxidative stress activity during the process of textile dye degradation and it was observed that the SOD activity was found higher when the strain was exposed to the dyes and degradation was also achieved (Phugare et al. 2012). In contrast, Choi et al. (2007) investigated the hydrogen peroxide resistance of P. aeruginosa genes and it was seen that the oxidative stress enzyme (SOD) activity was significantly reduced and less activity was observed, this state of expression of SOD under stress condition was not disturbed and the hydrogen peroxide remains neutral. This crucial role of SOD has the decisive effect on P. aeruginosa for gaining the resistance over hydrogen peroxide. Although, as mentioned above SOD can be a potent oxidative stress agent in the mechanism of many environmental contaminant cleaning processes. However, it also fails to express under certain conditions and this state of SOD will not affect any environmental processes. In our study, it was deliberated that SOD was identified in propiconazole resistant strain P. aeruginosa PS-4 and we conclude that the propiconazole degradation was partially monitored by SOD oxidative stress effect.

The antecedent literatures give an idea about how the CAT can be able to show its activity under the stress conditions. For instance, Hassett et al. (2000) have shown that the CAT activity was found greater in the P. aeruginosa oxyR mutant strain but not in the non-mutant strain. This concludes that the bacteria may fail themselves to adapt to the toxic conditions, however, this kind of typical behavior of bacteria will have the chances of CAT activity enhancement to overcome the toxic effects. It was also reported that the various strains of Pseudomonas have outermost membrane receptor protein that involves in the transportation of iron complex to the siderophores/pyoverdines. In the bacterial cells iron is mainly used for the metabolic processes where oxygen is the electron acceptor (Loper and Henkels 1999). It is well known that the oxygen serves as good electron acceptor. The oxygen by-products including superoxide and hydrogen peroxide, however, are lethal to the bacteria. In response, Pseudomonas sps. produce catalase to protect the cell from the reactive properties of the by-products (Miller et al. 1997), this clarifies that catalase will be expressed when the bacteria will be exposed to any toxic compounds. The sign of catalase gene in our study indicates protective defense of P. aeruginosa PS-4 strain from the propiconazole. Moreover, the CAT activity was induced to avoid the lethal effects of latter and this induction of CAT activity was achieved through the aromatic amines in growing cells of E. coli (Cooper et al. 1985; Diaz et al. 2001). Hence, CAT gene is the major precursor in any of the degradation study because of the oxidative stress defense.

The in silico docking study of propiconazole with both SOD and CAT shows that it has the highest affinity to bind at the iron binding region. The iron binding residues in SOD are His26, His73, Asp156 and His160. The ligand propiconazole also showed the interaction with the nearby residues like Trp72, Thr75 and Pro152. The binding complex provides the way to understand the mode of action of propiconazole towards the activity of iron cation to every sub unit. SOD destroys superoxide anion radicals which are normally produced within the cells and which are toxic to biological systems. Additionally, the interaction of propiconazole with catalase has lowest binding energy of − 6.7 kcal/mol and has hydrophobic binding with His5, Ala62, Val75, Gly76, Asn77, Phe82, Val146, Met261 and Pro263. The active site residues predicted with CastP server are matching closely with CAT binding interacting residues. The catalase enzyme decomposes hydrogen peroxide into water and oxygen and serves to protect cells from the toxic effects of hydrogen peroxide. The binding activity of propiconazole at the HEM binding region provides an insight to understand the mode of inhibition of 1M7S.

Presentation of a couple of mistakes in the homology displaying is a typical occurrence and more consideration is required towards improvement and approval of the structure built (Misura and Baker 2005). A few strategies are accessible for an extensive assessment of structural arrangement in a model, which incorporates superposition of model into the native structure with the structure alignment program, estimation of the normal separation between the backbone particles of superimposed proteins by the RAMPAGE and generation of the Z-score (Gerstein and Levitt 1998). Similarly, Kurjogi et al. (2018) showed the approval of the SEA and SEB 3-D models were done utilizing Ramachandran plot, and the comparing counts were figured with the PROCHECK program and Ramachandran plot showed 98.8% and 95.5% of residues of the SEA and SEB, respectively. Previously, Morris et al. (2009) have detailed that, in the allowed region of modeled enzyme the phi–psi distribution for residues is more than 90% containing the core alpha, core beta and core left handed alpha. Likewise, in our study, in the favored region the phi–psi distribution for the residues was more than 99% and 98% for SOD and CAT, respectively. Additionally, molecular docking process is considered as a promising tool to understand ligand behavior against active sites of the molecule, Mangasuli et al. (2018) reported how a surflex-docking was employed for the interaction of chemical synthesized compounds to cytochrome P450 14 alpha-sterol demethylase. Likewise, coumarin–carprofen hybrids which are condensed with coumarin exhibits the excellent activity against the enzyme PDB ID:4FDO, thus, proves the importance of molecular docking interaction (Pattanashetty et al. 2017). In the molecular docking studies, the predication of the appropriate active binding sites on the genes was done and it was also suggested that the docking energy is always lowest and expressed in kcal/mol (Azam and Abbasi 2013). Moreover, in the present in silico analysis, the docking was authenticated with lower docking energy and free binding energy. Therefore, the strain P. aeruginosa PS-4 can play a vital role in maintaining environmental sustainability through decontamination of dangerous contaminants.


The plasmid cured P. aeruginosa PS-4 strain was found to be potent agent for the degradation of pollutants. Moreover, SOD and CAT genes isolated from the P. aeruginosa PS-4 strain were involved in the degradation of propiconazole. The molecular docking of propiconazole with SOD and catalase provided an insight towards the understanding of mechanism of action. Based on the binding energy, propiconazole showed highest binding affinity with CAT compared to SOD. Further, propiconazole structure was docked with the structures of SOD and CAT, and it was concluded that the toxicity was suppressed by the SOD and CAT. It is observed from the present study that plasmid cured P. aeruginosa PS-4 strain have the ability to degrade propiconazole and the degradation was enhanced by the plasmid cured strain. Further, conventional wet laboratory practise would provide more experimental confidence to use the bacteria or its enzymes in in situ degradation of propiconazole.



The authors are thankful to the Department of Biotechnology (DBT), Ministry of Science and Technology, Government of India, Delhi, for providing the instrumentation (BT/PR/4555/INF/22/126/2010 dated 30-09-2010) and bioinformatics infrastructure facility. Authors also thankful to the UGC-UPE fellowships and Post Graduate Department of Studies in Microbiology and Biotechnology, Karnatak University, Dharwad for providing the laboratory facilities.

Compliance with ethical standards

Conflict of interest

The authors do not have any conflict of interest connected to the manuscript.

Supplementary material

42398_2019_83_MOESM1_ESM.doc (250 kb)
Supplementary material 1 (DOC 250 kb)


  1. Aislabie J, Lloyd-Jones G (1995) A review of bacterial-degradation of pesticides. Aust J Soil Res 33:925–942Google Scholar
  2. Araya M, Lakhi A (2004) Response to consecutive nematicide applications using the same product in mussaAAAcv. Grande naine originated from in vitro propagative material and cultivated in virgin soil. Nematol Bras 28:55–61Google Scholar
  3. Arnold K, Bordoli L, Kopp J, Schwede T (2006) The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics 22(2):195–201. Google Scholar
  4. Azam SS, Abbasi SW (2013) Molecular docking studies for the identification of novel melatoninergic inhibitors for acetylserotonin-O-methyltransferase using different docking routines. Theor Biol Med Model 10:63. Google Scholar
  5. Chen S, Alexander M (1989) Reasons for the acclimation for 2,4-D biodegradation in lake water. J Environ Qual 8:153–156Google Scholar
  6. Choi YS, Shin DH, Chung IY, Kim SH, Heo YJ, Cho YH (2007) Identification of Pseudomonas aeruginosa genes crucial for hydrogen peroxide resistance. J Microbiol Biotechnol 17(8):1344–1352Google Scholar
  7. Cooper RA, Jones DC, Parrott S (1985) Isolation and mapping of Escherichia coli K12 mutants defective in phenylacetate degradation. J Gen Microbiol 131(10):2753–2757. Google Scholar
  8. Degtyarenko KN (1995) Structural domains of P450-containing monooxygenase systems. Protein Eng 8(8):737–747Google Scholar
  9. DeLano WL (2002) Pymol: an open-source molecular graphics tool.
  10. Deng D, Li F, Li MX (2018) A novel propane monooxygenase initiating degradation of 1,4-dioxane by Mycobacterium dioxanotrophicus PH-06. Environ Sci Technol Lett 5:86–91Google Scholar
  11. Deshpande NM, Dhakephalkar PK, Kanekar PP (2001) Plasmid-mediated dimethoate degradation in Pseudomonas aeruginosa MCMB-427. Lett Appl Microbiol 33(4):275–279Google Scholar
  12. Diaz E, Ferrandez A, Prieto MA, Garcia JL (2001) Biodegradation of aromatic compounds by Escherichia coli. Microbiol Mol Biol Rev MMBR 65(4):523–569. Google Scholar
  13. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 34(Web Server Issue):W116–W118. Google Scholar
  14. Fukai T, Ushio-Fukai M (2011) Superoxide dismutases: role in redox signaling, vascular function, and diseases. Antioxid Redox Signal 15(6):1583–1606. Google Scholar
  15. Gerstein M, Levitt M (1998) Comprehensive assessment of automatic structural alignment against a manual standard, the scop classification of proteins. Protein Sci 7(2):445–456. Google Scholar
  16. Hassett DJ, Alsabbagh E, Parvatiyar K, Howell ML, Wilmott RW, Ochsner UA (2000) A protease-resistant catalase, KatA, released upon cell lysis during stationary phase is essential for aerobic survival of a Pseudomonas aeruginosa oxyR mutant at low cell densities. J Bacteriol 182:4557–4563Google Scholar
  17. Hayatsu M, Hirano M, Tokuda S (2000) Involvement of two plasmids in fenitrothion degradation by Burkholderia sp. strain NF100. Appl Environ Microbiol 66(4):1737–1740Google Scholar
  18. Hoskeri RS, Mulla SI, Ninnekar HZ (2014) Biodegradation of chloroaromatic pollutants by bacterial consortium immobilized in polyurethene foam and other matrices. Biocatal Agric Biotechnol 3:390–396Google Scholar
  19. Javaid MK, Ashiq M, Tahir M (2016) Potential of biological agents in decontamination of agricultural soil. Scientifica 2016:1598325. Google Scholar
  20. Kamachi S, Wada K, Tamoi M, Shigeoka S, Tada T (2014) The 2.2 Å resolution structure of the catalase-peroxidase KatG from Synechococcus elongatus PCC7942. Acta Crystallogr F Struct Biol Commun 70:288–293Google Scholar
  21. Kurjogi M, Satapute P, Jogaiah S, Abdelrahman M, Daddam JR, Ramu V, Tran LP (2018) Computational modeling of the Staphylococcal enterotoxins and their interaction with natural antitoxin compounds. Int J Mol Sci 19(1):133. Google Scholar
  22. Latha MS, Saddala MS (2017) Molecular docking based screening of a simulated HIF-1 protein model for potential inhibitors. Bioinformation 13(11):388–393. Google Scholar
  23. Lengauer T, Rarey M (1996) Computational methods for biomolecular docking. Curr Opin Struct Biol 6(3):402–406Google Scholar
  24. Loper JE, Henkels MD (1999) Utilization of heterologous siderophores enhances levels of iron available to Pseudomonas putida in the rhizosphere. Appl Environ Microbiol 65(12):5357–5363Google Scholar
  25. Lovell SC, Davis IW, Arendall WB 3rd, de Bakker PI, Word JM, Prisant MG, Richardson JS, Richardson DC (2003) Structure validation by Calpha geometry: phi, psi and Cbeta deviation. Proteins 50(3):437–450. Google Scholar
  26. Mangasuli SN, Hosamani KM, Satapute P, Joshi SD (2018) Synthesis, molecular docking studies and biological evaluation of potent coumarin–carbonodithioate hybrids via microwave irradiation. Chem Data Collect 15–16:115–125Google Scholar
  27. Milgrom LR (2016) Why is catalase so fast? A preliminary network hypothesis for the rapid enzyme-catalysed decomposition of hydrogen peroxide. Water 7:129–146Google Scholar
  28. Miller CD, Kim YC, Anderson AJ (1997) Cloning and mutational analysis of the gene for the stationary-phase inducible catalase (catC) from Pseudomonas putida. J Bacteriol 179(16):5241–5245Google Scholar
  29. Misura KM, Baker D (2005) Progress and challenges in high-resolution refinement of protein structure models. Proteins 59(1):15–29. Google Scholar
  30. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791Google Scholar
  31. Mulla SI, Sun Q, Hu A, Wang Y, Ashfaq M, Eqani SAMAS, Yu CP (2016a) Evaluation of sulfadiazine degradation in three newly isolated pure bacterial cultures. PLoS One 11:e0165013Google Scholar
  32. Mulla SI, Wang H, Sun Q, Hu A, Yu CP (2016b) Characterization of triclosan metabolism in Sphingomonas sp. strain YL-JM2C. Sci Rep 6:21965Google Scholar
  33. Mulla SI, Ameen F, Tallur PN, Bharagava RN, Bangeppagari M, Eqani S, Bagewadi ZK, Mahadevan GD, Yu CP, Ninnekar HZ (2017) Aerobic degradation of fenvalerate by a Gram-positive bacterium, Bacillus flexus strain XJU-4. 3 Biotech 7(5):320. Google Scholar
  34. Mulla SI, Hu A, Sun Q, Li J, Suanon F, Ashfaq M, Yu CP (2018) Biodegradation of sulfamethoxazole in bacteria from three different origins. J Environ Manag 206:93–102Google Scholar
  35. Mulla SI, Bharagava RN, Belhaj D, Saratale GD, Bagewadi ZK, Saxena G, Kumar A, Mohan H, Yu CP, Ninnekar HZ (2019) An overview of nitro group-containing compounds and herbicides degradation in microorganisms. In: Arora PK (ed) Microbial metabolism of xenobiotic compounds Microorganisms for sustainability, vol 10. Springer, Singapore, pp 319–335. Google Scholar
  36. Munita JM, Arias CA (2016) Mechanisms of antibiotic resistance. Microbiol. Spectrum 1:1. Google Scholar
  37. Norman RS, Moeller P, McDonald TJ, Morris PJ (2004) Effect of pyocyanin on a crude-oil-degrading microbial community. Appl Environ Microbiol 70(7):4004–4011. Google Scholar
  38. Pattanashetty SH, Hosamani KM, Satapute P, Joshi SD, Obelannavar K (2017) Discovery of new drugs and computational studies of coumarin-carprofen scaffolds as a novel class of anti-tubercular, anti-inflammatory and anti-bacterial agents. Eur J Pharm Med Res 4:486–498Google Scholar
  39. Phugare SS, Gaikwad YB, Jadhav JP (2012) Biodegradation of acephate using a developed bacterial consortium and toxicological analysis using earthworms (Lumbricus terrestris) as a model animal. Int Biodeterior Biodegrad 69:1–9Google Scholar
  40. Raja CE, Selvam GS (2009) Plasmid profile and curing analysis of Pseudomonas aeruginosa as metal resistant. Int J Environ Sci Technol 6:259–266Google Scholar
  41. Satapute P, Kaliwal B (2016a) Biodegradation of propiconazole by newly isolated Burkholderia sp strain BBK_9. 3 Biotech 6(1):110. Google Scholar
  42. Satapute P, Kaliwal B (2016b) Biodegradation of the fungicide propiconazole by Pseudomonas aeruginosa PS-4 strain isolated from a paddy soil. Ann Microbiol 66:1355–1365Google Scholar
  43. Satapute PP, Kaliwal BB (2018) Burkholderia Sp. Strain BBK_9: a potent agent for propiconazole Degradation. In: Bidoia ED, Montagnolli RN (eds) Toxicity and biodegradation testing, Methods in pharmacology and toxicology. Humana Press, New York, pp 87–108Google Scholar
  44. Satapute P, Kamble MV, Adhikari SS, Jogaiah S (2019a) Influence of triazole pesticides on tillage soil microbial populations and metabolic changes. Sci Total Environ 651:2334–2344Google Scholar
  45. Satapute P, Paidi MK, Kurjogi M, Jogaiah S (2019b) Physiological adaptation and spectral annotation of arsenic and cadmium heavy metal-resistant and susceptible strain Pseudomonas taiwanensis. Environ Pollut 251:555–563Google Scholar
  46. Shetti AA, Kaliwal BB (2012) Biodegradation of imidacloprid by soil isolates Brevundimonas sp. Int J Curr Res 4:100–106Google Scholar
  47. Singh R, Kumar M, Mittal A, Mehta PK (2016) Microbial enzymes: industrial progress in 21st century. 3 Biotech 6(2):174. Google Scholar
  48. Talwar MP, Mulla SI, Ninnekar HZ (2014) Biodegradation of organophosphate pesticide quinalphos by Ochrobactrum sp. strain HZM. J Appl Microbiol 117(5):1283–1292. Google Scholar
  49. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S (2013) MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol Biol Evol 30(12):2725–2729. Google Scholar
  50. van der Meer JR (1994) Genetic adaptation of bacteria to chlorinated aromatic compounds. FEMS Microbiol Rev 15(2–3):239–249. Google Scholar
  51. Zou P, Borovok I, de Orue Ortiz, Lucana D, Muller D, Schrempf H (1999) The mycelium-associated Streptomyces reticuli catalase-peroxidase, its gene and regulation by FurS. Microbiology 145(Pt 3):549–559. Google Scholar

Copyright information

© Society for Environmental Sustainability 2019

Authors and Affiliations

  • Praveen Satapute
    • 1
    Email author
  • Rajeshwari D. Sanakal
    • 2
  • Sikandar I. Mulla
    • 3
  • Basappa Kaliwal
    • 1
  1. 1.Department of Microbiology and BiotechnologyKarnatak UniversityDharwadIndia
  2. 2.Department of ZoologyKarnatak Science CollegeDharwadIndia
  3. 3.Department of Biochemistry, School of Applied SciencesREVA UniversityBangaloreIndia

Personalised recommendations