Aveneu Park, Starling, Australia

Oral its adhesion function.Chou reported chemoresistance in functional

Oral
Squamous Cell Carcinoma (OSCC) is most prevalent cancer worldwide with
noticeable human death rate1.The survival rate of the disease has
not increased though the advancements in the treatment as surgery and
chemo-radio therapy.2The major cause of failure to cure this OSCC
could be the resistance towards therapies reoccurrence3. Hyaluronon,a
major component of extra cellular matrix and ligand for CD44 plays significant
role in oral squamous cell carcinoma progression4.CD44,a trans-membrane
glycoprotein,hyaluronic-bindingHAreceptor,expressed in a wide variety of
cells5,6,7. Previously it was reported the use of CD44 as a marker for
early molecular diagnosis of lung10,prostate11,colorectal12,breast13,gynecologic14,gastric15,head
and neck cancer16,lymphoma17,osteosarcoma18.

Changes in CD44 Glucosylation site alters CD44
binding to hyaluronic acid, any mutations in the phosphorylation site of
cytoplasmic domain of CD44 hinder its adhesion function.Chou reported
chemoresistance in functional CD44 variants,compared to wild type carriers19.In
this scenario,we have carried out this present modelling and simulations study to
understand the mutational changes on the overall structure, functionality of
CD44. Although,many mutations were reported for functional damage to the
protein. we have selected six major mutations i.e, T27A,R41A,T102A,S112A,S122A,R162A
reported around the glycosylation site of CD44 based on the importance on its
functionality keeping in view of the complete crystallized structure accessibility20.

 

Materials
and Methods

Selection of SNPs for in silico analysis

Human CD44 gene information data was collected from Online
Mendelian Inheritance in Man (OMIM)21 and Entrez Gene on National
Centre for Biological Information (NCBI) dbSNP was used to take SNPs reported
in CD44 gene associated with Oral cancer 22,23.6 SNPs were analysed further.The
CD44 proteins amino acid sequence was retrieved from the Uniprot database(P16070).Protein
3D structure from protein data bank (1UUH) (Fig.1) 24.

Mutant protein modeling

The 3D structure of a protein is crucial to study its
functionality,to understand the effect of SNP’s on its overall structure and
function.We used rcsb.org to identify the protein coded by CD44 gene (PDB ID 1UUH),which
is 158 amino acids in length starting from residue at position 20,ending at 178.We
have studied six mutations by using “mutate a residue” in the Schrödinger
maestro v9.6 visualization program Maestro, Version
9.6, used wild type available 3D structure (1UUH) as
standard.

MD simulations in water

Simulations  were run using “Desmondv3.6
Package”25,26. Predefined TIP3P water model27 to
simulate water molecules. Orthorhombic periodic boundary conditions were set up
to specify the shape, size of the repeating unit buffered at 10Å distances.To
neutralize the system electrically,appropriate counter Na+/Cl- ions were added
to balance the system charge, placed randomly in solvated system.After building
the solvated system,performed minimization,relaxation of protein/protein-ligand
complex under NPT ensemble using default practice of Desmond28,29,which
includes 9 stages, only  2 minimization
and 4 short simulations (equilibration phase) are involved before starting the
actual production time.

Summary of Desmond’s MD simulation stages

Simulations
were carried out with the periodic boundary conditions in the NPT ensemble
using OPLS 2005 force field parameters30,31.The temperature were
kept at 300K and pressure at 1 atmospheric pressure using Nose-Hoover
temperature coupling,isotropic scaling32.The operation was followed
by running the 10ns NPT production simulation each and saving the
configurations thus obtained at 5ps intervals.

Analysis
of molecular dynamics (MD)  trajectory

These
were analyzed by using simulation quality, event analysis, simulation
interaction diagram programs of Desmond for calculating Energies, root-mean-square
deviation and fluctuation.Total intramolecular hydrogen bonds,Radius of Gyration
along with secondary structure elements of protein conferring stability.Simulation
quality assurance validates the system stability throughout the simulated
length of chemical time for the given temperature,pressure,volume of the total simulation
box.Whereas, simulation event analyzes each frame of simulated trajectory output
and simulation nteraction diagram  for estimating
the total secondary structure elements change in the protein structure during
simulation.

Pre-processing
and preparation

 protein target structure

Crystal
structure of CD44 protein in complex with hyaluronic acid1UUH was resolved by
X-ray diffraction,with a resolution factor of 2.30Å was retrieved from Protein Data
Bank33,34,which was further modified for docking
calculations as follows:CD44 protein was imported to Maestro v9.635.Using
Protein Preparation Wizard (PPW),Schrödinger36 included biological
units and assigned bond orders,created zero-order bonds to metals,created
disulfide bonds,converted any selenomethionines to methionines,deleted all
water molecules,generated metal binding states for hetero atoms,added missing
hydrogens and capped termini.Also checked for any missing side chains,missing
loops to fill using prime module integrated within PPW and found none.Under
review and modify tab of PPW,all the co-crystallized ligands/hetero atoms and
waters were identified,removed from the structure.Under the refine tab of
PPW,we have optimized the H-bond network to fix the overlapping hydrogens and
the most likely positions of thiol and hydroxyl hydrogen atoms, protonation
states,tautomers of ‘His’ residues,Chi ‘flip’ assignments for ‘Gln’,’Asn’,’His’
residues were selected by protein assignment script shipped by Schrodinger.At
pH 7.0,the protein was minimized by applying OPLS2005 force field30,31.Finally,restrained
minimization was performed until the average root mean square deviation (RMSD)
of the non-hydrogen atoms converged to 0.30Å.

Ligand
and  docking

The
3D coordinates of quinine were retrieved from Pubchem database37.Ligands
for docking studies were prepared using Autodock mgltoolsv1.4.6.Before ligand
preparation,ligand structure was energy minimized by charmm’s force field.Ionization
state was set to generate all possible states at pH7.0±2.0.Keeping in view of
the flexibility of the rings present in each ligand and their possibility to
change conformations during docking calculations,we have specified to generate
low energy ring conformation via allowing maximum possible rotatable bonds.

CD44-Quinine docking
analysis

Recently,a considerable amount of literature has
suggested high potent activity of quinine compound against cancer.In
continuation to the quest of understanding the potential of this natural
compound, we have recently performed a lab scale study to evaluate the anticancer effects
of quinine on KB and HEp-2 cancer cells. Our MTT
assay based studies has revealed that quinine has a IC50 value of 125.23?m
for 24hr and 117.81?m for 48hr with respect to KB cell line.Whereas, it was 147.58?m
and 123.74?m with Hep2.39 In another study,we have demonstrated that quinine treatment significantly
inhibited the cell viability and cell proliferation leading to increased
reactive oxygen species generation,induction of  depolarization of mitochondrial membrane, DNA
damage in dose and time-dependent manner. Moreover,quinine significantly
decreased the iNOS,COX-2,IL-6,Bcl-2,mutant p53 simultaneously up-regulated
Bax,caspase-3 expressions suggesting,that quinine may serve as a potential
candidate in the prevention of cell proliferation and enhances apoptosis via
inhibiting up-stream signaling.40In this scenario, taking our
present study to a step further,we have investigated the
impact of mutations on the inhibitor recognition functions of CD44
protein,docking analysis was carried out with specific inhibitor quinine indicated
that the mutations contribute to weaker interaction with the drug, primarily
due to loss of interactions of the drug with surrounding residues. We utilised wild-type(CD44-quinine),T27A(T27A-quinine)
for our analysis (Figure.8).Comparing the binding free energy of CD44 to the
drug,mutant T27A exhibited the weakest interaction with the energy value of ?5.58
Kcal/mol with 81.25?m of inhibition constant when compared to wild-type complex
-6.05 Kcal/mol with 36.62?m.This result signifies better conjugation of
inhibitor to the binding pocket of the receptor.Mutant T27A complex exhibited
the least binding affinity towards quinine, which was confirmed by the docking
scores.

Protein-ligand MD
simulations in water

Since molecular docking represents
only a single snapshot of protein–ligand interactions, we have performed
molecular dynamic simulations in order to study the protein–ligand interactions
in motion contributing for their stable bound conformation and to visualize the
effect of ligand binding on protein conformational changes. The effect of
quinine on wild-type CD44 and T27A mutant was studied through MD simulations.

Quinine compound simulation
studies with wild  and T27A mutant

The dynamic behaviour of wild and
mutant protein via simulations. The RMSD contributions were plotted as the time
dependant function of MD simulations between the wild-type and mutant (T27A).Two
independent simulations were carried out.

The results in Figure 9 shows that
the RMSDs of the trajectories for the wild-type complex was well below 3.0Å for the first 5ns.Throughout the simulation
period,no significant fluctuations were observed in the backbone of the
wild-type implying that the binding of quinine at the active site of the
proteins is not only stable and strong but also does not disturb the protein
backbone stability.When mutant protein residue fluctuations were calculated in
presence of ligand quinine,it was observed that movements and continous
fluctuations noticeable at 1ns (Fig 9b) measured.RMSD value of the ligand
observed in the figure is significantly larger than the RMSD of the protein.According
to this observation the ligand has diffused away from its initial binding site
in the early simulations,which leads to the inefficient binding with T27A
mutant protein.Indeed,wild-type and mutant T27A complex tend to reach a steady
equilibrium,while RMSD of the mutant complex was noticeably high.Mutant complex
T27A remained distinguished throughout the simulation resulting in maximum
backbone RMSD of ?3.2Å.This difference in the deviation range explains
the change in stability of the mutant protein,which in turn reflects the impact
of substituted amino acid in the protein structure.

In order to calculate the residual
mobility of each lead molecules in CD44 protein–ligand complexes (wild-type and
mutant),Root Mean Square Fluctuation was calculated in each complexes and the
graph was plotted against the residue number based on the trajectory period of
MD simulation to identify the higher flexibility regions in the protein.In protein
RMSF graph of mutant complex,we can see that the major peaks of fluctuations
have been observed with 120-125 residues with over 4Å,and residues between 140-145
with >4.2Å have highest deviation during the MD simulations.Rest of the
residues were found to be quite stable and fluctuating well below 2.0Å.Despite
the fact that mutant complex T27A showed deviation from its starting
conformation.Analysis of fluctuation score depicted that the higher degree of
flexibility was observed in mutant(T27A) complex than wild-type structure.This
suggests that T27A mutation affects the binding of quinine and makes the
backbone more flexible to move.We also monitor changes in secondary structure
during the simulations,it was observed that wild-type and mutant proteins
maintaining an average of around 64% SSE,there is no significant change observed
in the secondary structure of mutant complex (Figure 10).From our analysis,it
is well revealed that wild-type complex form strong hydrogen bond with quinine
and it is maintained throughout the simulation,while the mutant complex T27A
showing very weak intermolecular hydrogen bonds and these bonds were not maintained
thorough out the simulation time. Hydrogen bonds in the wild-type complex
structure might help to maintain its rigidity while less tendency of the mutant
involved in participating in hydrogen bonding with solvent makes it more
flexible.The most notable change was seen in T27A mutation which was well
supported by an increase in binding energy and loss of hydrogen bond
interactions with the mutant protein when compared to the wild-type protein. In
our study, a clear understanding of stability loss was seen in the RMSF,RMSD
which were also accompanied by less number of intermolecular bonds for T27A
when compared to wild-type CD44 protein.

 

Interaction profile of
ligand with wild  and mutant during MD
simulation

When one of the best snapshots of
MD trajectory was analyzed,it has been observed that quinine forming strong
hydrogen bond with GLU 75 residue at catalytic site of wild-type CD44 protein
with over 94% occupancy,but mutant protein was not forming hydrogen bonds with
GLU 75 residue  whereas it was trying to
form hydrogen bond with SER71 with over 2% occupancy only during MD trajectory
(Figure 9).Results of the hydrophobic interactions of the ligand with wild-type
protein shows, that it was found to be interacting with PHE30,His35 and in the
mutant protein MD simulations it was found to be forming hydrophobic
interactions with LEU70,ILE91 but these interactions not maintaining at least
10% of the MD simulation time.

From the total contacts formed between
quinine with wild-type and T27A mutant CD44 residues,wild-type was found to be in
contact with residues ASN25,ILE26,THR27,PHE30, HIS35,GLY73,GLU75,THR76,CYS77,ARG78
and mutant protein was found to be in contact with residues
PHE30,HIS35,LEU70,SER71,ILE72,GLY73,PHE74,GLU75,
THR76,CYS77,ILE91,HIS92,PRO93,THR102,GLU127,ARG150,TYR169(Figure10).

Quinine trying to contact more
amino acids of mutant complex compared to wild-type during MD simulation.T27A
mutant complex residues were not able to form strong interaction with
quinine,due to this less interactions in T27 mutant structure might help to
lose its rigidity and makes it more flexible.Further,from our study it is clear
that the mutant model T27A has higher overall flexibility when compared to the
native protein.Ligand RMSF may give the insights on how ligand fragments
interact with the protein and their entropic role in the binding event.Mutant
complex T27A showed a higher deviation with wild type.The number of hydrogen
bonds formed between quinine and protein models (wild-type and mutant) during
the MD simulation were also calculated.