Abstract
The present study undertaken to predict the three dimensional structure and active site analysis of inducible
serine protease inhibitor -2(ISPI -2) known to inhibit the activity of entomopathogenic fungi Metarhizium anisopliae in Galleria mellonella a severe pest of most economic important crops. This inhibitor completely
inactivates serine protease produced by M.anisopliae which acts as major virulent factor for G.mellonella and
imparts natural immunity to the pest. Initially, the structural template for G.mellonella – ISPI-2 was identified
from structural database using homology modeling or comparative modeling approach. Based on the knowledge
of the template, a three-dimensional model was predicted and processed in to energy minimization, Ramachandran
plot analysis, quality assessment and finally deposited into Protein Model Database. An active site of the theoretical
model was analyzed and helpful to recognize the effective ligands.
Keywords
Inducible serine protease inhibitor; Galleria mellonella; MODELLER; Protein Model Database; RAMPAGE;
entomopathogenic fungi; active site
Abbreviations
ISPI – Inducible serine protease inhibitor; BLAST – Basic local alignment search tool; PMDB – Protein
Model Database;
PIR – Protein information resource.
Introduction
An entomopathogenic fungus is a one kind of fungus,
which can act as a parasite of insects and now extensively
used as biocontrol agents against a wide spectrum of insects
and other arthropod pests that are harmful to various
plants, because of their high efficacy, safe to non-target
organisms and ease of multiplication (
Sahayaraj and Karthick
Raja Namasivayam, 2008). It infects susceptible hosts
through the integument, and utilizes different proteases to
carry out the digestion of cuticle proteins for colonization
and to inactivate the host immune systems (
Clarkson and
Charnley, 1996), (
Frobius et al. 2000).
Insects lack the immune system of vertebrates involving
antigen–antibody reactions, although they can protect themselves
efficiently from entomopathogenic infections. The humoral part of the insect immune system is characterized
by the rapid and transient synthesis of proteins with potent
antibacterial and antifungal activity and inactivates the effect
of the insect’s pathogenic activity (Gillespie et al. 1997)
and expressing a wide spectrum of protease inhibitors to
inhibit the proteases and other lytic enzymes produced by
entomopathogenic fungi. Such type of protease inhibitors
was reported in many insects viz Bombyx mori was effective
against entomopathogenic fungi Beauveria bassiana.
Larvae of the greater wax moth, Galleria mellonella is
very resistant to various microbes and their infections such
as Aspergillus melleus, Beauveria bassiana, Metarhizium
anisopliae etc (Eguchi et al. 1986). Because it produces
various inducible serine protease inhibitors and it was identified
recently such as ISPI-1, ISPI-2 and ISPI-3 in hemolymph that are active against various toxic proteases
produced from entomopathogenic fungi (Frobius et al. 2000).
Based on the determined amino acid sequences, Galleria
mellonella ISPI-2 represents a novel member of the Kunitztype
inhibitor family, whereas ISPI-1 and ISPI-3 share no
similarity with other known proteins (Clermont et al. 2004).
Serine protease inhibitors are mainly classified into three
basic types such as canonical, non-canonical and serpins.
Canonical inhibitors are the largest family and its size ranging
from 14 to 200 amino-acid residues. These are rigid,
stable and regularly have beta sheet or mixed alpha-beta
topologies. A few can be only alpha helical or irregular proteins
that are rich in disulfide bridges (Otlewski et al. 2005).
Non-canonical protease inhibitors (for eg. hirudin and
haemedin) are interacting with the active site of serine proteases
through their N-terminal tails (Grutter et al. 1990).
Serpins are 45–55 kDa proteins that are composed of three
beta sheets and eight or nine alpha helices forming a single
domain and inhibit through the reactive-site loop present at
their C-terminus (Gettins, 2002).
The understanding of the three-dimensional structure of
a protein would be a precious aid to understand the details
of a particular protein. Active site analysis is a key step for
identify or design the potential molecules for the purpose of
molecular docking studies followed by ligand optimization.
The main objective of this study is an attempt to predict the
structural as well as active site information of inducible serine
protease inhibitor-2 from Galleria mellonella, which is
helpful to identify the potential lead molecules for prevent
the function of inhibitors and activate the function of fungal
serine proteases.
Materials and Methods
Retrieval of Galleria Mellonella - Inducible Serine
Protease Inhibitor-2 Protein Sequence
The protein sequence of inducible serine protease inhibitor-
2 (ISPI-2) in Galleria mellonella was retrieved from
the Swissprot database (http://www.expasy.org) and taken
as target sequence. The main reason for choosing this protein
was, it active against various serine proteases including
trypsin and toxin proteases relased by several
entomopathogenic fungi (Frobius et al. 2000). It was deeprooted
that the three-dimensional structure of this protein
was not available in any three-dimensional structural databases.
Hence, the current study of developing the threedimensional
structure of the inducible serine protease inhibitor-
2 from Galleria mellonella was undertaken.
Selection of Structural Template
An effort was made to find a suitable structural homolog
or template for the modeling of the inducible serine protease
inhibitor-2 from Galleria melonella. In the beginning,
a structural template was obtained from Protein
BLAST (Altschul et al. 1990) and it is used Protein Data
Bank (Berman et al. 2003) as reference data base for identify
the closely related sequences.
Target – Template Alignment
The protein sequence of inducible serine protease inhibitor-
2 was aligned with its corresponding template by using
align-2D module in MODELLER 9V2 (Eswar et al. 2008),
which required two files such as a file containing target
sequence in PIR format and an another file containing structural
coordinates of template. This step is essential to identify
the common conserved residues or active residues
present in both the sequences.
Model Building
MODELLER 9V2 was used to predict the three-dimensional
structure of inducible serine protease inhibitor-2 using
model-single.py based on satisfaction of spatial restraints.
It is a python script, used to predict the three dimensional
model from single template. Theoretical model was subjected
into Swiss-PDB Viewer (Kaplan and Littlejohn, 2001)
for energy minimization using the steepest descent and conjugate
gradient technique to correct the stereochemistry of
the model. Computational analysis was carried out in vacuo
with the GROMOS96 43B1 parameters set, without reaction
field in Swiss-PDB viewer.
Model Evaluation
Refined model was subjected to a series of tests for testing
its internal stability and reliability. Backbone conformation
of the refined model was assessed by the examination
of the Psi/Phi Ramachandran plot obtained from RAMPAGE
web server (Lovell et al. 2003). Errat web server
(Colovos and Yeates, 1993) was used to explore the statistics
of non-bonded interactions between different atom types
and plots the graph. Finally, an evaluated model was deposited
into Protein Model Database (www.mi.caspur.it/
PMDB/).
Active Site Analysis
Potential active site and active residues was identified by
CastP server (Binkowski et al. 2003) which is essential for
function of serine protease inhibitor in Galleria melonella.
Results and Discussion
|
Figure 1: Sequence alignment of inducible serine protease inhibitor -2 [ISPI-2] with Kunitz Domain 1 of Tissue
Factor Pathway Inhibitor-2 [1ZR0] using Clustalw. “*” shows conserved region between the sequences and “-”shows
gaps.
|
|
Figure 2: Graphical representation of sequence alignment of both target [ISPI-2] and template [1ZR0]. Various
conserved regions with highlighted 100% conserved residues.
|
The amino acid sequence of inducible serine protease inhibitor-
2 was retrieved from commonly used primary protein
sequence database i.e. Swissprot (http://
www.expasy.org) and its accession numbers is P81906 and
the source was of Galleria mellonella in origin. The molecular
weight of this inhibitor is 6.3kDa obtained from mass
spectroscopy (Frobius et al. 2000). The results of Protein
BLAST (http://www.ncbi.nlm.nih.gov) search for suitable
template structure related to the target sequence (ISPI-2)
showed the crystal structure of kunitz domain 1 of tissue
factor pathway inhibitor -2 of Bos taurus with highest sequence
similarity (53%), as the most suitable template for
modeling. The alignment of inducible serine protease inhibitor-2 and its corresponding template was carefully examined
and conserved regions was identified shown in Figure
1 using align-2D module in MODELLER9V2 and it
was concluded that this alignment can be assisted to generate
a three dimensional reliable model. Jalview (Clamp et al. 2004) was used to display the conserved regions in graphical
representation shown in Figure 2.
Once target-template alignment was completed, a three
dimensional structure of ISPI-2, was predicted using the
program MODELLER9V2 produced three different conformations
and its modeller objective functions were
262.7881, 260.3169, 275.1234. The second conformation
had lowest value compared to other. Generally, the best model
could be obtained from choosing the model with the lowest
value of the MODELLER objective function, which is reported
in the second line of the model PDB file (Eswar et
al. 2008). Based on the lowest value of the modeller objective
function, one predicted model was taken and processed
in to Swiss-PDB Viewer for energy minimization.
|
Figure 3: Ramachandran plot of theoretical model of ISPI-2.
|
|
Figure 4: The final three-dimensional structural representation of ISPI-2 [PyMOL]. This model was conducted
by MODELLER 9V2 Program.
|
|
Figure 5: Cartoon display of various potential binding pockets of ISPI-2 is displayed in different colors and
active site residues are highlighted [Pocket 1 – pink, Pocket 2 – magenta, Pocket 3 – yellow, Pocket 4 – cyan,
Pocket 5 – blue and Pocket 6 – green].
|
An assessment of the refined model involved two independent
tests. The first test was to compare the residue backbone conformations in our refined model with the preferred
values obtained from Protein Data Bank of known
structures. The results of RAMPAGE web server (http://
mordred.bioc.cam.ac.uk/~rapper/rampage.php) explored
that 98% residues are found to be most favoured region
of the Ramachandran Plot of refined model of induced serine
protease inhibitor-2 which is more then cut-off of 90% in
most reliable models (Lovell et al. 2003) shown in Figure 3.
The stereo chemical quality of the predicted model was found
to be satisfactory and low percentage of residues having
phi/psi angles in the outlier region.
The second test was carried out using Errat web server
(http://nihserver.mbi.ucla.edu/ERRATv2/) for check the
quality of the models. Generally, the quality factor of high
resolution structures generally produce values around 90%
or higher (Colovos and Yeates, 1993). Here, the overall
quality factor of this refined model was 92.568 predicted
from Errat. The evaluated final reliable model has been
deposited in to Protein Model Database (http://mi.caspur.it/
PMDB/) and is now publicly accessible [PMDB id:
PM0075527]. A three dimensional structure of inducible
serine protease inhibitor-2 is shown in Figure. 4.
CastP web server (http://sts-fw.bioengr.uic.edu/castp/calculation.php) was used to predict the active site of inducible
serine protease inhibitor – 2 from Galleria
mellonella. Six potential binding pockets were identified
and displayed in Figure 5. The active site residues (Figure
5) obtained from CastP web server is essential for inhibition
of toxic serine proteases produced from various
entomopathogenic fungi (Frobius et al. 2000) and they are
cysteine, leucine, glutamic acid, histidine, arginine, phenylalanine,
glycine, tyrosine, aspartic acid, threonine, aspargine
and lysine.
Conclusion
The understanding of the three-dimensional structure and
active site of ISPI-2 is important step for identify the probable
ligand candidates. However, a three dimensional structure
of ISPI-2 is only a predictive, and needed to be confirmed
experimentally. Further molecular docking and virtual
screening approach is necessary to recognize specific
ligands for ISPI-2, which will be validated by many in silico
analysis such as molecular docking, QSAR (Quantitative
Structure-Activity Relationship) and rule of five.
Future studies helpful to design ligand against ISPI-2 – a
major immune defense factor of G. mellonella against M.
anisopliae and it completely neutralizes the inhibitory activity
of ISPI-2.
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