Oral have studied six mutations and modelled it

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(HA),a major component of extra cellular matrix,a primary 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
ofCD44asamarkerforearlymoleculardiagnosisoflung10,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 computational modelling and
simulations approach to understand the mutation induced 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 this site importance on its functionality keeping in view of the
complete crystallized structure availability20.

 

Materials and Methods

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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 amino acid
sequence of CD44 protein 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 the residue at position 20,ending at 178.We have
studied six mutations and modelled it by using “mutate a residue” tool in the
Schrödinger maestro v9.6 visualization program Maestro, Version 9.6, used
wild type available 3D structure (1UUH) as reference.

MD
simulations in water

“Desmondv3.6
Package”25,26 was used to run the molecular dynamic
simulations.Used 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 protocol of Desmond28,29,which
includes a total of 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

Molecular
dynamic 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 trajectory

The
molecular dynamics(MDS) trajectory files were analyzed by using simulation
quality, event analysis alongside 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.SQA qualitatively validates the system
stability throughout the simulated length of chemical time for the given
temperature,pressure,volume of the total simulation box.Whereas,SEA analyzes
each frame of simulated trajectory output and SID for estimating the total SSE
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 pH7.0,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

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.

Ligand
docking

The
molecular dynamics(MDS) trajectory files were analyzed by using simulation
quality, event analysis alongside 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.SQA qualitatively
validates the system stability throughout the simulated length of chemical time
for the given temperature,pressure,volume of the total simulation
box.Whereas,SEA analyzes each frame of simulated trajectory output and SID for
estimating the total SSE change in the protein structure during simulation.

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 antioxidant
effects of quinine via anti-lipidperoxidation,
antioxidant effect on cancer cells (KB and HEp-2).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 MMP
depolarization,morphological changes,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-typeandmutant),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 GLU75 residue at catalytic site of wild-type CD44 protein
with over 94% occupancy,but mutant protein was not forming hydrogen bonds with
GLU75 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 withresiduesPHE30,HIS35,LEU70,SER71,ILE72,GLY73,PHE74,GLU75,THR76,CYS77,ILE91,HIS92,PRO93,THR102,GLU127,ARG150,TYR169(Figure10).

 

 

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