molecular dynamics simulation of HYD1.compressed

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Molecular Dynamics Simulation Studies of
HYD1
ARTICLE in INTERNATIONAL JOURNAL OF SCIENCE AND RESEARCH (IJSR) · AUGUST 2014
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Cephas Mawere
Harare Institute of Technology
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International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Impact Factor (2012): 3.358
Volume 3 Issue 8, August 2014
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Licensed Under Creative Commons Attribution CC BY
Molecular Dynamics Simulation Studies of HYD1
Cephas Mawere1
, Suresh Kumar G2
1, 2
Department of Bioinformatics, School of IT, Jawaharlal Nehru Technological University Hyderabad, India
Abstract: The unique features of HYD1 protein and its primary involvement in fungal pathogenesis of Gibberella moniliformis have
drawn attention to insilico study of potential inhibitors. This destructive mold is also on the rise of causing cancer related diseases in
both humans and animals upon ingestion of the infected maize and/or rice grains. To date, currently used fungicides are ineffective
against G. moniliformis as the fungus can grow and adapt to any environment. It has been realized that such behavior is attributed to
class I hydrophobins where HYD1 belongs. These bio-surfactant macromolecules spontaneously self assemble at hydrophobic:
hydrophilic interfaces into thin nanometric rodlet biofilms. For this reason, this paper focuses on refining a predicted HYD1 protein and
studying its conformational and thermodynamic properties. Molecular dynamics simulation studies thus prove the protein to be
thermally stable, compact, and results in a better quality model. This will help in understanding the self assembly mechanism of HYD1
and future docking studies.
Keywords: HYD1, Gibberella moniliformis, Hydrophobins, Bio-surfactant, Self-assemble, Molecular dynamics simulation
1.Introduction
Hydrophobins are a group of small surface active protein
uniquely found in filamentous fungi [1] like G. moniliformis.
They constitute ten percent of the fungal cell wall as
structural components and when secreted they are localized
on the surface of aerial mycelia [3]. At hydrophilic:
hydrophobic interfaces these proteins spontaneously self-
assemble into different types of supramolecules. Based on
this unique property, solubility and the pattern of Cys
residues in their primary sequence, hydrophobins are divided
into two groups- class I and class II [2]. Class I
hydrophobins, where HYD1 belongs, can spontaneously self-
assemble into a 10 nm wide amphipathic rodlet film in the
presence of a hydrophilic: hydrophobic interface [5]. This
biofilm lowers the surface tension of the medium or
substratum in/on which G. moniliformis will grow [6]. Of
interest, class I hydrophobins exists in three states; as
monomers they are soluble in water, but self- assemble into
the beta state at water-air interface. However, at the interface
between water and a hydrophobic solid like Teflon, HYD1
attains an alpha helical structure [7]. Such a self assembly
property of HYD1 causes it to be thermally resistant to heat
treatments below 90o
C. The protein also tends to be insoluble
in aqueous solvents only to be dissociated by harsh treatment
with strong acids such as concentrated TFA [9]. It has
therefore been suggested by Wosten et al [1] that the eight
conserved cysteine residues that form four disulphide bridges
prevent self- assembly of the hydrophobin in the absence of a
hydrophilic: hydrophobic interface, while the N-terminal part
of hydrophobin, at least partly, determines wettability of the
hydrophobic side of the assemblage. Experimental evidence
of related class I hydrophobins also indicate that the alpha
helical state is an intermediate while the end form of
assembly is the beta state [8]. These two assembled forms
have an amphipathic nature which confers water repellent
properties to many conidia, hyphae and multicellular
structures. The rodlet layer is covered by a mucilaginous
extracellular matrix that helps the fungal conidia to bind to
the substrate [6]. This makes class I hydrophobins, which
includes HYD1, to be essential for the G. moniliformis to
complete its biological life cycle. As such, class I
hydrophobins are actively involved in formation of
hydrophobic aerial structures like spores and fruiting bodies,
in fungal development, environmental adaptation and
pathogenic interactions. During pathogenesis, for instance,
class I hydrophobins mediate attachment of hyphae to each
other [3] and to the hydrophobic surface of the host plant
before penetration and infection commence [4]. For this
cause G. moniliformis has been a destructive pathogen to
more than 11,000 plant species including maize and rice [9].
This study therefore seeks to characterize the dynamic
behaviour of HYD1 in water, prior to docking simulations in
a quest to find a potential class I hydrophobin inhibitor for
the aforementioned fungus.
2.Materials and Methods
A predicted HYD1 protein model from the M4T_HYD1
server [10] validated by PROCHECK and Verify3D was
used for MD simulation studies. GROMACS 4.5.4 package
[11] was thus chosen to explore the dynamic behavior of
M4T_HYD1 protein with the empirical force-field
GROMOS96 43a1 being used. The study on protein
conformation, stability, compactness and refinement was
therefore done in a four staged simulation process as
indicated in Fig. 1 below.
Figure 1: The four staged molecular dynamics simulation
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2.1 Energy Minimization
In order to understand the atomic scale mechanisms of
HYD1, a topology file (.top), a GROMACS-compatible
coordinate file (.gro) and a position restraints file (.itp) were
created using the ‘pdb2gmx’ command. The .gro file of
HYD1 was then used to prepare the system simulation cell.
The resulting cubic box was defined by a 1.0 Ao
edge length.
This was followed by centering the protein in the box. An
SPC (a single three-point water model coordinate) file
(spc216.gro) was then used to fill up the box, hence solvating
the system. The system net charge was then neutralized by
replacing six solvent molecules (SOL) in the .top file with
Three Na+
and three Cl-
ions. In order to relax the molecular
geometry of the system by getting rid of atomic clashes or
any existing irregularities certain parameters were
considered. These include a maximum force (emtol) was set
at 100kJ/mol/nm, periodic boundary conditions in the
directions x, y, z, 0.8 nm cut-offs for short range VDW and
electrostatic interactions as well as a particle-mesh Ewald
long range electrostatics. Once these factors were set, the
system was minimized for 50,000 steps using the steepest
descent algorithm.
2.2 Equilibration
System equilibration was characterized by two ensemble
based simulations- NVT and NPT. Once minimization was
complete the system was first simulated at 25,000 steps with
a 2fs time step (a total of 50ps) to bring it to equilibrium.
Thus, an NVT ensemble was used to create trajectories at a
Langevin temperature of 298 K and a constant volume of 158
nm3
using Berendsen coupling algorithm. Position restraints
were applied on the protein backbone to avoid any wild
fluctuations. Also, all velocities were evenly distributed
across all molecule types by coupling protein atoms and non-
proteins in separate baths to keep the M4T_HYD1 rigid.
Finally, the linear constraint solver (LINCS) algorithm was
employed to constrain all bonds.
After this calibration step to a target temperature, the system
was relaxed with an NPT ensemble. This allows the system
to find the correct density. This time, the simulation was
extended to 500ps with position restraints still being applied
to the backbone of M4T_HYD1. A Nose-Hoover thermostat
was then used in place of the Berendsen method to keep the
kinetic energy of the system from fluctuating wildly. Lastly,
pressure was isotropically (the same in all directions) coupled
to a value of one bar.
2.3 Production MD
As soon as the system reached equilibrium and the correct
density was found, a classical molecular dynamics simulation
step was introduced. Here, the position restraints on the
backbone of M4T_HYD1 were lifted. As such, all other
parameters applied in the NPT equilibration were used for
2,500,000 MD steps with a 2fs timestep, totaling 5,000 ps (or
5ns) of sampling.
2.4 Analysis
A ‘trjconv’ command was used to convert trajectory files to
.xtc and .pdb format for further analysis. A ‘g_energy’ was
also used to output total energy, volume, potential energy,
kinetic energy and pressure data results for the equilibration
stage and energy minimization stage. ‘g_rms’, ‘g_rmsf’,
‘g_sas’, and ‘g_gyrate’ commands were respectively
employed to calculate the RMSD, RMSF, Solvent Accessible
Area, and Radius of gyration of the simulated protein.
Likewise ‘g_mindist’ and ‘g_dist’ were used to measure the
distance between atomic residues. The resulting files (.xvg)
were analyzed using xmgrace tool [12]. Moreover, the
‘g_confirms’ command was used to generate a pdb file of the
original M4T_HYD1 protein superimposed to the simulated
structure. RasMol [13] and PYMOL Viewer [14] programs
were used to visualize and interpret the trajectories, the
generated protein simulate, and the generated rmsf.pdb file.
Finally, the secondary structure content of the simulated
M4T_HYD1 as a function of time was analyzed against that
of the original protein using the DSSP (Dictionary of
Secondary Structure of Proteins) tool [15].
3.Results and Discussion
3.1 Energy minimization (EM)
EM of M4T_HYD1 foresaw steepest descent algorithm
converging to Fmax (maximum force) in less than 100 in
6,153 steps (approx. 6ns). This resulted in the system
potential energy being reduced from -1.8e+05 kJ/mol to
global minimum value of -2.7129166e+05 kJ/mol (Fig. 2).
Thus, energy minimization has ensured that we have a
reasonable starting structure, in terms of geometry, solvent
orientation and without sterical clashes.
Figure 2: Potential energy graph for minimized M4T_HYD1
3.2 Equilibration
a) NVT Simulation
Equilibration using the NVT ensemble was validated as
successful based on results obtained for energy attributes like
temperature, potential energy, kinetic energy, and total
energy. From Table 1.0 and temperature and total energy
plots (Fig. 3 and Fig. 4) it is apparent that the system is well
equilibrated around the target temperature (298 K) and total
system energy in the first 50ps.
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Figure 3: System temperature as a function of time during
NVT equilibration
Figure 4: Total Energy of system under equilibration
Table 1: Attributes used to validate the equilibration stage
Attribute
Average
Score
Err.
Est
RMSD
Total
drift
S.I
Unit
Temperatur 296.69 1.6 13.3317 8.8066 K
Pot. Energy -207614 270 2455.01 1462.4 kJ/mol
Kin. Energy 37238 200 1673.29 1105.3 kJ/mol
Tot. Energy -170093 44 1395.11 206.91 kJ/mol
Pressure 2.67034 2.3 186.631 -12.82 Bar
Volume 157.371 0.031 0.18585
7
-0.125 nm3
Density 994.646 0.2 1.17671 0.7904 Kg/m3
b) NPT Simulation
However, a correct density among other factors like pressure
and volume (Table 1) are required to authenticate a
successful position restraint based simulation.
Figure 5: System density as a function of time during NVT
equilibration
Accordingly, the density of the system appears to have
quickly reached equilibrium quite near to a physiological
value as shown by the density plot (Fig. 5).
3.3 Analysis
3.3.1 RMSD, RMSF and Radius of Gyration
The root means square deviation (RMSD) is a measure of
how much the structure of a protein changes over the course
of the simulation. For small, globular proteins like HYD1,
changes on the order of 1-3 Angstroms are acceptable and
expected [16]. Beyond this range the protein might be
misbehaving. In this study, the backbone option was chosen
for the least fit and for the RMSD measurement, and the
output ‘rmsd.xvg’ file was visualized in Grace (xmgrace):
Figure 6: M4T_HYD1 backbone RMSD during a 5ns
simulation
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The black line in Fig. 6 thus represents the RMSD of the
protein backbone during the simulation, which lies in the
acceptable range. Despite this being a 5ns simulation,
M4T_HYD1 is reasonably stable around 0.2 nm. Now, while
RMSD measures the global backbone deviation, the local
changes are measured using the root mean square fluctuation
(RMSF). In this study much interest was in the entire residue
fluctuation (Fig. 7) as well as the residues of the protein
backbone (Fig. 9). Thus the ‘g_rmsf’ command resulted in
two important output files; a data file of RMSF values plotted
by residue number that can be viewed with Grace and a
structure file (.pdb) that contains beta-factors equal to RMSF
values.
Figure 7: RMS fluctuations of protein residues during
simulation
On this plot, peaks indicate areas of the protein that
fluctuated the most during the simulation. The C-terminal tail
here fluctuated much more than any other part of the HYD1
protein.
Figure 8: RMSF protein residues as viewed in RasMol
When the rmsf.pdb for the above plot was loaded in RasMol
and visualized using the temperature colour scheme, the
above structure was produced. Here, blue value indicates the
most fluctuation which decreases in the colour order : red-
orange- yellow- yellowgreen- green. Information from
RasMol also reveals that HYD1 has 6 strands, 12 turns and I
helix. It therefore appears that the beta sheet is the most
stable part of the protein. However, for rms fluctuation of
backbone residues each residue is coded according to the
anisotropic temperature (beta) value stored in the pdb file
[13]. Typically, this gives the measure of the mobility of a
given residue’s position. The beta value thus increases in
order from blue-cyan- green- yellow- orange then to red as
shown in Fig. 10.
Figure 9: RMS fluctuations of backbone residues during
simulation
Also, in plot for backbone residues, three residues that form
two beta strands fluctuated the most as indicated by the red
colour in Fig. 10.
Figure 10: RMSF backbone residues as viewed in RasMol
These include Valine-47, Glycine-46 and Leucine-45.
Fluctuations were also noticed with Alanine-26 and
Glytamine-97 residues as they transition to form a fully
solvated and dynamic HYD1 protein.
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Figure 11: Radius of gyration of protein residues over 5ns
Upon compact assessment with g_gyrate which measures the
radius of gyration, the structure of M4T_HYD1 seemed to
collapse from 1.08 nm to 1.04 nm. Unusual peaks were
observed at 1ns and 3ns as the protein tried to resolve into its
original shape, before it finally attained a constant stable
structure at 1.05nm towards the 5ns mark (Fig. 11). The
collapse of the HYD1 protein was probably accompanied by
changes to its secondary structure, as revealed by backbone
fluctuations in the rmsf plot (Fig. 8, 10) and DSSP Table 2.
3.3.2Trajectory Analysis
Trajectory data generated from the classical MD simulation
step and converted to pdb format was viewed using PYMOL
tool. The protein file thus contained 2,500 frames which
caused the protein to rotate around all three axes. Six frames
were thus selected at time intervals shown in Fig. 12 below.
Figure 12: Snapshots of protein conformations in cartoon
representation during the classical MD step
The circled area is a region of beta strands and turns where
most fluctuations where observed (Fig. 7-10). Consistent to
RMSD and Rg plots significant residue/ atomic fluctuations
were observed at 1ns, and 3ns before HYD1 conformed to a
stable dynamic structure between 4ns and 5ns.
Of interest, the simulated protein seem to have slowly
exposed and partly exposed its binding cavity in the
hydrophobic region of strands resulting in an increase in the
surface accessible area for inhibitors to bind to the exposed
pocket. Fig. 13 reveals a gradual reduction of the
hydrophobic area with a sudden drop being observed towards
1ns (which is 1000ps) from 27 nm2
to 21.6 nm2
. However,
from 2ns till the end of the simulation M4T_HYD1 keeps an
average hydrophobic area of 24 nm2
.
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Figure 13: Hydrophobicity measurement as a function of
time during simulation of M4T_HYD1
3.3.3 Overall Conformation Analysis
When the solvated protein now in dynamic form is analyzed
against the original M4T_HYD1 protein, the conformation
shows an RMSD value score of 0.291738 nm. Again the
deviation lies in the acceptable range and can be credited to
the secondary structure changes observed by DSSP (Table
2).
Figuer 14: Superimposed backbone structures of original
and simulated HYD1 protein
Comparison of these two structurally aligned proteins with
DSSP reveals that as HYD1 conformed to a less compact
structure, there were significant loss of beta sheets, and less
of the helix to other secondary structure elements like bends,
hydrogen bond turns and amino acids not in atom records:
Table 2: DSSP three state composition of HYD1
DSSP value (%)
Method M4T_HYD1 Simulated HYD1
Helix 15.3 13.9
Sheet 37.5 23.6
Others 47.2 62.5
However, the total content of sheet against that of helix in the
secondary structure of the simulated protein still remains
greater by approximately double that of helix. This can prove
the theoretical fact that class hydrophobins attain a beta state
in a water solvent.
4.Conclusion and Future Scope
MD simulations were used to compute atomic trajectories by
solving equations of motion numerically using the
GROMOS96 43a1 empirical force field. From a dynamic
point of view the HYD1 protein proved to be thermally
stable in the presence of an explicit water environment at
298K and constant pressure conditions. The protein also
attained a compact structure which was credited to the low
energy fluctuating beta strands. These findings are in
agreement with previous theoretical and experimental
evidence on dynamics of class I hydrophobins in related
filamentous fungi. The authors therefore look forward to use
the simulated M4T_HYD1 structure for docking studies in
pursuit of a potential inhibitor molecule against G.
moniliformis.
Acknowledgements
The authors would like to thank Dr. A. Govardhan, Director,
School of Information Technology (SIT), Jawaharlal Nehru
Technological University Hyderabad (JNTUH) for his
support and encouragement in this research.
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Author Profile
Cephas Mawere is an M.Tech Student in the
field of Bioinformatics currently underway at the
SIT, JNTUH, India. He attained his B. Tech.
Degree in Biotechnology from CUT, Zimbabwe
in 2010. He is a Harare Institute of Technology staff
development research fellow. His research interests are in the
area of Microbiology, Molecular Dynamics Simulation,
Computer- Aided Drug Design, High Performance
Computing and Proteomics.
Suresh Kumar G is a Lecturer in Bioinformatics
at School of Information Technology (SIT),
Jawaharlal Nehru Technological University
Hyderabad (JNTUH), Hyderabad, India. He has
received Masters Degree (M.Tech) in Bioinformatics from
University of Hyderabad, Hyderabad, India. His research
interests include Computer- Aided Drug Design Molecular
Modeling, Docking, 3D-QSAR studies, Genomics and
Proteomics, Computational Systems Biology.
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molecular dynamics simulation of HYD1.compressed

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