The CFTR gene belongs to a family of genes that regulate the energy transfer that allows a cell to open and close its ion channels. It is located on human chromosome 7 and consists of twenty-seven sequences of DNA that encode 1,480 amino acids. The CFTR gene produces the CFTR protein, which regulates the chloride ion content of epithelial cells that line the nasal cavity, lungs, and stomach.

1. By: Kainat Ramzan

2. Introduction
Cystic Fibrosis
ABC Transporters
Chromosomal Location
Structure of CFTR gene
Location and Function
Mutation of CFTR Polypeptide
Delta F508 Mutation
Causes
Classification of CFTR gene
Material and Method
Results
Conclusion
References
2

3. INTRODUCTION
 Cystic fibrosis transmembrane conductance regulator (CFTR) protein that
in humans is encoded by the CFTR gene
 CFTR gene discovered by ROMMENS
 Cystic fibrosis is also known as CF or mucoviscidosis
- Causing progressive disability and often early death
 The name cystic fibrosis refers to the characteristic scarring (fibrosis),
and cyst formation within the pancreas, first recognized in the 1930s
 Mutations of the CFTR gene affect the functioning of the chloride ion
channels in these cell membranes,
- leading to cystic fibrosis and congenital absence of the vas deferens
 Difficulty breathing results from frequent lung infections
3

4. CYSTIC FIBROSIS (Mucoviscidosis)
An autosomal recessive genetic disorder
• An inherited disease characterized by the
buildup of thick, sticky mucus that can
damage many of the body's organs
• Characterized by abnormal transport of
chloride and sodium across an epithelium
leading to thick viscous secretions
• Cystic fibrosis refers to the characteristic
scarring (fibrosis) and cyst formation
within the pancreas
• In 1938, Firstly recognized in the
Andersen DH (1938)
Affects
mainly the
lungs and
also the
pancreas,
liver, and
intestine
4

5. ABC Transporters
CFTR is an ABC transporter-class ion channel
- transports chloride and thiocyanate ions across epithelial
cell membranes
• These are responsible for transporting small foreign molecule (like drugs
& toxins) especially out of cells by exsorption & thus called efflux pump
5
Figure 1. ATP-binding cassette (ABC) transporters represent a heterogeneous group of ATP-dependent transport proteins. (A) The illustration shows the structure of ABC
transporters. (B) The cartoon provides an overview of typical ABC transporter substrates (Christoph Thurm et al., 2021)

6. CHROMOSOMAL LOCATION
Gene that encodes the CFTR protein is found
on the human chromosome 7
On the
long arm
at position
q31.2
From base pair
116,907,253 to
base pair
117,095,955
6

7. STRUCTURE OF CFTR GENE
CFTR is a glycoprotein with 1480 amino acids
The normal allelic variant for this gene is about 250,000
bp long and contains 27 exons
The CFTR chloride channels has five domains:
1. Two membrane Spanning domains
- Each with six transmembrane sequences
2. Two nucleotide(ATP)-binding domains;
3. Regulatory domain with multiple phosphorylation site
7
Figure 3. The putative domain structure of the cystic fibrosis transmembrane
conductance regulator (CFTR) protein

8. STRUCTURE OF CFTR GENE
8
Figure 4. From Gene To Protein Structure (https://cftr.iurc.montp.inserm.fr/cgi-bin/gene_prot.cgi)

9. LOCATION AND FUNCTION
The CFTR is found in the epithelial cells of
many organs including the lung, liver,
pancreas, digestive tract, reproductive tract,
and skin and sweat glands
CFTR functions as a cAMP-activated ATP
gated anion channel,
By increasing the conductance for certain
anions (e.g. Cl ions) to flow down their
electrochemical gradient
• ATP-driven conformational changes in CFTR open and
close a gate,
To allow the transmembrane flow of anions
down their electrochemical gradient.
• CFTR defects result in a reduced transport of sodium
chloride
• And sodium thiocyanate in the reabsorptive duct and saltier
sweat
• This was the basis of a clinically important sweat test for
cystic fibrosis
9

10. MUTATION IN THE CFTR POLYPEPTIDE
The first mutation identified, a
deletion of a phenylalanine residue
at position 508 in the first ATP
binding fold,
Accounting for about 70% of all
CF alleles in white populations
• In these populations, only seven other
mutations are more frequent than 0.5%,
and the remainder are therefore are rare
10

11. ΔF508 MUTATION
Most common mutation,
ΔF508, is a deletion (Δ
signifying deletion) of
three nucleotides that
results in a loss of the
amino acid phenylalanine
(F) at the 508th position on
the protein.
When a CFTR protein with
the delta F508 mutation
reaches the ER, the
quality-control mechanism
recognizes that the protein
is folded incorrectly and
marks the defective protein
for degradation
As a result, delta F508
never reaches the cell
membrane
• People (delta F508 mutation)
tend to have the most severe
symptoms of CF
• due to critical loss of chloride
ion transport
This upsets the sodium and chloride
ion balance needed to maintain the
normal, thin mucus layer that is
easily removed by cilia lining the
lungs and other organs
•The sodium and chloride ion imbalance creates
a thick, sticky mucus layer that cannot be
removed by cilia and traps bacteria, resulting
in chronic infections
•This mutation accounts for 2/3rd of cases
worldwide
11

12. CAUSES
12
In cystic fibrosis, a defect (mutation) in a (CFTR) gene
- changes a protein that regulates the movement of salt in and out of
cells
• The result is thick, sticky mucus in the respiratory, digestive, and reproductive
systems, as well as increased salt in sweat
• Many different defects can occur in the gene
• A persistent cough that produces thick mucus (sputum)
• Wheezing
• Exercise intolerance
• Repeated lung infections
• Inflamed nasal passages or a stuffy nose
• Recurrent sinusitis
• Foul-smelling, greasy stools
• Poor weight gain and growth
• Intestinal blockage(meconium ileus)
• Chronic or severe constipation
Signs and
symptoms
such as:

13. CLASSIFICATION OF CFTR MUTATIONS
13
Class 1 mutations
•- Defective protein production with premature termination of
CFTR protein production
- Class 1 produce few or no functioning CFTR chloride channels
Class 2 mutations
•- Defective trafficking of CFTR so that it does not reach the apical
surface membrane
Class 3 mutations
•- Defective regulation (opening and closing) of the CFTR chloride
channel
- which allows movement of chloride in and out of the cell even,
- though the CFTR protein is able to reach the apical cell surface
Class 4 mutations
•- CFTR reaches the apical surface but conduction (passage of
chloride ions through the channel) is defective
Class 5 mutations
•- Associated with reduced synthesis of functional CFTR
Class 6 mutations
•- Proteins are synthesized normally but are unstable at the cell
surface

14. CLASSIFICATION OF CFTR MUTATIONS
14
Figure 6. Schematic representation of CFTR (CF transmembrane conductance regulator) mutation classes (Boyle et al., 2013)

16. MATERIALAND METHOD
• CFTR protein sequence obtained
from the UniProtKB database
(https://www.uniprot.org) and NCBI
(http://www.ncbi.nlm.nih.gov)
• Complete list of SNPs was gained
from dbSNP-NCBI
(https://www.ncbi.nlm.nih.gov/snp)
1. Retrival of
SNPs
• SNPNEXUS (https://www.snp-nexus.org), which
includes SIFT and PolyPhen
• SIFT score of 0.05 are deleterious and
classifying mutations as damaging or tolerable
based on a tolerance score
• PolyPhen Classified as Benign, probably
damaging, or possibly damaging and score range
from 0-1
• SNAP2 (https://rostlab.org/services/snap2web),
as input the CFTR gene FASTA sequence was
provided
2. Functional
consequences
of CFTR gene
16

17. 2. Functional consequences of CFTR gene
PROVEAN acquires a protein sequence as an input and provides a PROVEAN score with less than -2.5 were
considered to be harmful
PPh-2 (http://genetics.bwh.harvard.edu/pph2) estimates the influence of an amino acid substitution on the
structure and function of protein. The input query was submitted in FASTA format
Using Polyphen-2, the substitution is estimated as 0 (benign) to 1 (damaging), with 1 being the most deleterious
CADD (https://cadd.gs.washington.edu), an integrated annotation system in the human genome, including point
mutation and short inserts and deletions
CONDEL (https://bbglab.irbbarcelona.org/fannsdb) assess to predict the non-synonymous SNVs on protein
structure and function and the results are shown as 0 to be neutral and 1 considered to be deleterious
17

18. SNPs & GO (https://snps-and-go.biocomp.unibo.it/snps-and-go) examined the amino acid alterations at
a single site of the protein. As input UniProt accession number also with the mutation position was
submitted. And probability score > 0.5 was predicted to be detrimental.
Meta-SNP (https://snps.biofold.org/meta-snp) was optimized to evaluate the disease-causing single
nucleotide variants based on point mutation. If the mutation is higher than 0.5 consider to cause a
damaging effect on protein and achieve an overall accuracy rate of 79%
P-MUT (http://mmb.irbbarcelona.org/PMut) predicts pathological traits associated with a mutation at a
rate of 80% and provides complete access to all single amino acid variants as part of disease prediction
PhD-SNP (https://snps.biofold.org/phd-snp.html) with a 78% accuracy rate and a scale range from 0 to 9
and identifies disease associations
18

19. MU Pro evaluates the
effects of substitutions on
protein structure and
function and score < 0
indicates the decrease in
protein stability
I-Mutant 3.0 algorithm has
an accuracy of up to 77
percent, Estimates a reliability
index (RI) of the result ranging
from 0 to 10
I-Stable examined
protein stability
alterations after a protein
substitution using a
support vector machine
(SVM)
19

20. 5. Identification of Sequence conservation by ConSurf
• The conserved region of amino acids was evaluated by ConSURF
(https://consurf.tau.ac.il)
• ConSURF conservation scores are calculated using genetic analyses
between homologous sequences
• In the conservation analysis, result ranging from 1-9 while 1–3 being
variable, 4–6 being typical, and 7–9 being well conserved
6. Post Translational Modifications (PTM) analysis
• PTM analysis was used to understand the consequences of amino acid
changes on disease association by adding functional groups including
phosphorylation, acetylation, methylation, or ubiquitylation
• Musite Deep (https://www.musite.net) predicts phosphorylation sites with
protein sequences are given as input
20

21. 7. Protein Structure refinement and Modeling
SWISS model was used regarded as the wild protein structure of
CFTR and then undergoes a single point mutation using PyMol
For the structural refining, the 3D refine server requires solely a
task name and protein structure
SAVES server has been used for checking protein models.
The SAVES incorporate PROCHECK, PROVE, ERRAT, and
RAMACHANDRAN plots created by the PROCHECK with over 90%
residues in the favorable region.
TM-align computes the template modeling score (TM score) and the
root-mean-square deviation (RMSD) to compare the structures of
wild-type and mutant proteins
And the score ranges from 0 and 1, with 1 denoting similarity
between wild and mutant. A higher RMSD indicates a greater
degree of variation between wild-type and mutant structures
21

22. 8. Molecular Docking and screening by CFTR
To illustrate the
interaction and binding
energy between the
CFTR and Ligands,
molecular docking
studies were performed
using the computational
program PyRx
Using PyRx software,
the ligands were docked
with the prepared 3D
CFTR structure
The PDB files of the
Ligands and Proteins
were converted to
PDBqt format using the
AutoDock tools
included in PyRx
The docked results
were visualized in 2D
and 3D diagrams
utilizing Discovery
Studio Visualizer
software
22

23. The VMD and NAMD 2.13
versions were used in the MD
simulation to compare
structural changes over time
The CHARMM-GUI server
was used to generate the force
field for ligands. PSF structures
were generated from the PDB
dataset and then solvated. Later,
VMD was used to simulate
molecular dynamics
To see results in a realistic time,
we use an explicit solvent
model, set the temperature to
310K, and set the simulation
time to 0.1 ns
The plots of RMSD, energy,
temperature, and pressure were
then created
23

25. Retrieval of Functional CFTR SNPs
 All 282,490 SNPs and protein sequences from the NCBI dbSNP database
25
Figure 7. a). Distribution of Single nucleotide polymorphism (SNP) b). Chromosomal location of a CFTR gene

26. According to SIFT algorithm, 5124 SNPs were predicted to be
deleterious, whereas 3474 SNPs were indicated to be tolerated
as in Figure 8.
The confidence value of 0 has been assigned to 132 SNPs
while 132 SNPs with one score were considered to be
highly deleterious.
The Polyphen output score ranges from 0 to 1 and
classified into possibly, probably and benign, with 1 being
the most damaging and 0 shows neutral behaviour.
Among SNPs, 4367 SNPs were predicted to be probably
damaging as shown in Figure 8.
26
Figure 8. Prediction of SNPs by SIFT and PolyPhen

27. From a total of 65132 SNPs, 132 SNPs were commonly predicted as deleterious by SIFT and to be functionally
damaging by PolyPhen server with a score of 1.
SNAP-2 indicated that 115 SNPs had an effect on protein function, while only 17 were expected to be neutral
PROVEAN predicts 112 SNPs as deleterious and 20 as neutral
PPh-2 predicts that 132 SNPs are most likely to be damaging with the score ranging from 0 to 1.00
From CADD score varies from deleterious to non-deleterious and variants with higher scores (above 40) are most
likely to be deleterious
K273Q, L165S, G1244R, L1356S, G149R, W1282C, G1298R, R560G variant shows highest score and are more
likely to be deleterious.
CONDEL uses a weighted average to identify mutations as neutral or deleterious
27

28.  SNP & GO make use of sequence and evolutionary information to estimate whether a mutation is disease-
related or not
- 132 nsSNPs, all of which were disease-causing
 P-Mutant predicts E116G, D192G, L467F, S519R, Q767H, S707H false result
- with score 0.4215 to 0.4722
 PhD-SNP predicts a given nsSNPs have damaging impacts, which classify them as disease-causing or
neutral
 As shown 40 SNPs were neutral and the rest were disease-causing
 Meta-SNP predicts 12 SNPs shows a neutral effect on CFTR protein
28

29. CFTR gene
stability and
changes
predicted by
using I-
mutant,
which
completes
this work by
analyzing
single site
mutations.
A total of 132
nsSNPs were
submitted to
I-mutant for
RI prediction
As a result,
such
polymorphis
m induces the
greatest
damage to
the protein by
reducing its
stability
A decrease in
protein
stability leads
to an increase
in
deterioration,
misfolding,
and protein
synthesis
By
calculating
the change in
thermodynam
ic free energy
(G) predicts
the change in
stability.
A positive G
value
indicates a
destabilizing
mutation and
a negative
value, a
stabilizing
mutation.
MU Pro
observed that
P99L,
K273M,
L441P,
G545V, and
S707F
increase the
protein
stability
Istable
predicts 26
nsSNPs
increase the
protein
stability
whereas 106
SNPs were
considered to
decrease the
protein
stability
29

30. Evolutionary Conservation of deleterious SNPs
A total of 132 SNPs were discovered, with a score range of 7-9 being
conserved throughout 98 of the SNPs
Highly conserved residues are either characterized as functional or
structural based on their position.
As a result, SNPs placed in conserved regions are regarded to be
more harmful to proteins than those found in non-conserved regions.
A total of 46 SNPs with a score of 9 were identified as highly
conserved
By comparing all the tools, only 31 variants were considered to be
highly deleterious and used for further protein modeling
30

31. Post- Translational Modification (PTMs)
Post-translational modification refer to the addition of functional
group to amino acid residues,
Vital in altering the protein structure and function, such as
protein-protein interactions and cell signaling
Musite-deep server predicts PTMs related to our candidate SNPs
Protein sequences in FASTA format are provided as input
According to Musite-deep, mutant C524R(S-Palmitoyl cysteine),
S737F and S707F show phosphoserine have been filtered out
31

32. Molecular Modeling for the CFTR
 The 3D CFTR structure that has been generated by SWISS model is given in Figure 9.
 PROCHECK produced a RAMACHANDRAN plot for each of the generated PDB structures
 The mutant L102P (1.05), G1249R(0.93), I105N (0.98), and K273Q (0.93) have higher RMSD values
and thus molecular docking and Molecular Dynamic (MD) simulation were performed
 RAMACHANDRAN structure validation depicted that 89.40% of the residue in the most favored or
core region of the
 A quality factor of 83.8258 % was investigated by studying the ERRAT error values
 3D Verify allows a template to score 80% or more of its residual amino acid 0.2 percent in the 3D/1D
profile native CFTR structure
32

33. Figure 9. a) 3D modeled structure of CFTR protein generated by Swiss Model b) RAMACHANDRAN plot of CFTR protein
33

34. AutoDock VINA, which was
used in the PyRx tool, produced
9 different conformational
changes for each ligand, which
are classified according to
binding affinity (-Kcal/mol).
The docking results indicate that
these ligands binding affinities
correlate with their activity
values and the ligands binding
interactions for all 30 compounds
As ligands with significant
binding affinities were as
follows: Filipin, NAD, Congo
red, Cpd B, cAMP, and melanin
All of the selected ligands have
binding free energy greater than -
4 Kcalmol-1. Ligands have higher
binding affinities, which are
greater than other ligand-binding
affinities
Using Discovery Studio
Visualizer, the ligands with the
highest binding affinities were
visualized and analyzed.
34

35. MD simulation by VMD and NAMD
 The docked proteins including I105N, G1249R and K273Q
mutants with ligands, which served as a starting point for
simulations
 The native and mutant CFTR protein structure trajectories were
both given root mean square deviations
 After minimization, the normal and mutant confirmations showed
only a very small change in temperature, pressure, and density.
 The wild-type structure was shown to be considerably more
stable than the mutant relying on RMSD plots of backbone atoms
35
Figure 10. Graph plotted between Temperature and
Simulation time of CFTR

36. CONCLUSION
Our research has shown a
chemical platform for the
detection of large ligands and
small compounds, as well as a
powerful platform for virtual
screening of probable small
molecules CFTR using a
consistent and complete approach
We evaluated the unique SNPs
connected with the CFTR gene in
this study, which served as a
platform data for demonstrating
virtual screening of CFTR via in-
silico analysis, as well as
revealing the biological platform
for understanding changes in
activity, stability, binding, and
other aspects
The study facts and findings can
aid in interpreting the impact of
these mutations or other
techniques such as drug
designing etc.
36

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