Isolation available in Protein Data Bank (PDB) database,

Isolation and identification of
organophosphate hydrolase (OPH) producers

A
potential organophosphate hydrolase
(OPH) producing Pseudomonas stutzeri
MCAS01 (Kavitha et al. 2016) has been used
in this study.

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Sequence and template search for
homology modeling

Pseudomonas stutzeri
organophosphate hydrolase (OPH), 3D structures are not available in Protein
Data Bank (PDB) database, the homologous sequences for building the 3D
structure was searched against PDB using NCBI BLAST (Basic Local Alignment
Search Tool) (Altschul et al.1990). The
homologous sequences are ability template structure for homology modeling. The
atomic coordinate report of the template structure was obtained from the PDB
(Berman et al. 2000).

Comparative modeling and model
confirmation

The
atomic coordinate file of the template along with the target and template final
sequence alignment file was used to build the model using the automated
homology modeling tool MODELER 9v9 (Eswar et al. 2006).
A bundle of models from the random generation of the starting structure was
calculated and among the generated models, the best model with the least Root
Mean Square Deviation (RMSD) value was selected by superimposing the model with
its template (Maiti et al. 2004). This model
was used for further analysis after subjecting it for energy minimization using
GROMOS of Swiss PDB viewer (Walter et al.1999).
The quality of the generated model was assessed by checking the stereo chemical
parameters using PROCHECK (Laskowski et al. 1993),
Verfiy3D (Bowie et al. 1991; Luthy et al.1992) and ERRAT at SAVES server http://nihserver.mbi.ucla.edu/SAVES
(Colovos et al. 1993).

Computational details

                All computations were carried
out on an Intel  Core  i3-3240 @ 3.40GHz capacity  processor 
with  a memory of 8GB RAM running
on windows 7 operating system. Finally docking studies were done using theFlexX
docking software package (https://www.biosolveit.de/). For the improvement and
binding energy calculations, the default settings of FlexX LeadIT were used.

Target proteins and ligands

                Protein structure were
downloaded from PDB (PDB id: 3F4D) (Fig. 2)
(Hawwa et al. 2009) and the ligand structure
were obtained from pubchem (Pubchem CID: 2730) (Fig. 1) and the functional
information of these proteins were retrieved from the Uniprot. Further,
hydrogen atoms, bond orders and formal charges were added using the protein
preparation wizard of the FlexX LeadIT tools as described.

Protein and ligand preparation

                PDB files of proteins and
ligands were prepared using FlexX LeadIT protein and ligand preparation wizard
and then binding pockets were set using the individual wizard. The interactions
of the ligand with the protein residues in the binding site were visualized.

 

Structural
analysis

Proteins and ligand interactions were calculated. A
cut-off of 1.5 to 3Å distance between the donor and acceptor were used for the
calculation of hydrogen bonds. All the positive docked sites were generated.

 

Results

 

Sequence analysis

The
organophosphate hydrolase enzyme producing bacterial strain was identified as Pseudomonas stutzeri through 16s rRNA
gene sequence analysis (Kavitha et al. 2016).
Sequenced amplicon has been submitted to NCBI database and accession number was
obtained KT757902. The organophousphorus
hydrolase gene (opd) was sequenced and submitted to the NCBI and the accession
number was MG739657. Based on the above information the organophosphate
hydrolase sequences were retrieved from PDB for homology modeling. The BLASTP
search for target sequences of organophosphate hydrolase from P. stutzeri against the PDB database
resulted that crystal structure of organophosphate hydrolase was got.

 

Homology modeling

The 3D structure of organophosphate hydrolase from Pseudomonas stutzeri was developed by
the X-ray structure. Modeler 9v9 was used to develop the 3D structure by
providing the alignment file, template file, and target file. The alignment
file was adjusted by taking into the account of overlap between the secondary
structure elements of the template and the predicted secondary structure
profile of the sequence. Further, considering the parameter provided for a
number of the model to be calculated as five, modeler provided five initial
models of cellulose by using random generation and by applying spatial
resistance. These generated models were superimposed with a template structure
to reveal the degree of modeled structure with the template by calculating the
Root Mean Square Deviation (RMSD). The modeled and energy minimized structure
of organophosphate hydrolase from P.
stutzeri was shown in cartoon representation with group color using rasmol
visualization tool (Fig. 2).

 

Binding
orientation and interaction

The
orientation of ligand is important for acceptor binding activity. Clearly,
binding orientation of chlorpyrifos model compounds inside the organophosphate
hydrolase highly varied
as can be seen from Fig. 4, which suggested the performance of organophosphate
hydrolase in catalysis
for the degradation of chlorpyrifos model compounds was different. The
interaction energies were analyzed in detail (Table 1).
When making comparisons between these complexes, the most noticeable difference
was the interaction energy. Their interaction energy changed 1 in a wide range.
This means that H-bonds were an alternative way to determine the interaction of
organophosphate
hydrolase with
chlorpyrifos. Hydrophobic interaction seemed to be a more important factor for
the binding of organophosphate hydrolase to chlorpyrifos model compounds than
H-bonds, because all chlorpyrifos model compounds formed hydrophobic
interactions with organophosphate hydrolase (Fig. 3).
We observed local differences in the types of amino acid residues participated
in hydrophobic interactions. These results showed that hydrophobic interactions
were necessary for the binding of organophosphate
hydrolase to
chlorpyrifos model compounds, and thus were potentially important to
chlorpyrifos degradation.

 

Discussion

Biodegradation
technology is becoming more and more attractive for environmental remediation
due to its environmentally friendly nature. An organophosphate hydrolase-based
application for chlorpyrifos pesticide degradation is a good example (Singh, 2009). To increase the chlorpyrifos-degrading
efficiency of organophosphate hydrolase, previous studies investigated the
impact of substrate structure on organophosphate hydrolase-mediated oxidation
rate, the stability of bacterial organophosphate hydrolase, using chlorpyrifos
as a model compounds (Fernanda et al. 2010). The chlorpyrifos-degrading efficiency
of organophosphate hydrolase was largely related to the properties of enzyme
and substrates, such as their binding property. The stability and catalytic
activities of organophosphate hydrolase was potentially influenced by the
binding modes between it and its substrates. However, the detailed interaction
mechanism between organophosphate hydrolase and chlorpyrifos is still unclear,
limiting organophosphate hydrolase application in chlorpyrifos degradation to
some extent. Thus, the illustration of interaction between organophosphate
hydrolase and chlorpyrifos model compounds is important. Molecular simulations
such as molecular docking have proved to be a robust technology for the
analyses of intermolecular interactions (Ramalha et al. 2016). This article performed an investigation of the molecular
basis of organophosphate hydrolase for chlorpyrifos degradation, using
molecular docking. We showed that the present protocol was capable of giving a
molecular insight into the interaction of organophosphate hydrolase with
chlorpyrifos model compounds, and in this way we found several rules that may
be important to chlorpyrifos degradation, this also states by Jin et al. (2015) Molecular Dynamics
Simulations of Acylpeptide Hydrolase Bound to Chlorpyrifosmethyl Oxon and
Dichlorvos. It was showed
that chlorpyrifos model compounds bound to organophosphate hydrolase by a wide
range of interaction energies. Therefore, we proposed that H-bonds were
alternative, but hydrophobic contacts were necessary to the interaction of
organophosphate hydrolase with chlorpyrifos model compounds or chlorpyrifos
this also confirmed by Castro et al. (2016).
Mean backbone RMSD values for different complexes varied (Lima et al. 2016). It not only meant stable behavior of these
complexes, but also indicated that the stability was different between various
complexes.

Conclusion

An extended binding analysis was done after docking the Chlorpyrifos
against the targeted protein. The models created by docking compounds against
target proteins were analyzed and the interactions, hydrogen bonds and distance.
Chlorpyrifos efficiently binds to the target protein organophosphate hydrolase
with the formation of three hydrogen bonds with residues Asp53, Lys54 and
yielded a binding affinity of -5.9124 kcal/mol. Therefore, the present study provides dynamic and structural information on the
interaction mechanism between organophosphate hydrolase and chlorpyrifos, being
useful to develop new organophosphate hydrolase with high
chlorpyrifos-degrading ability for the prevention of pollution in the soil and  provide an ecofriendly environment.

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