Bioinformatic analysis of Interferon gamma in humans

1. Kainat Ramzan – MPhil BioChemistry. Sem-IV 2022
Department of Biochemistry, University of Okara
A Presentation on

2. 1. Introduction
2. Interferon
3. Classification of IFN
4. IFNG Structure
5. IFNG Gene
6. IFN Function
7. IFNG Mutation and Polymorphism
8. Material and Methods
9. Results
10. Conclusion
11. References
OUTLINE

3. INTRODUCTION
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⊳ Single nucleotide polymorphisms (SNPs) represents a single
nucleotide differences between at least two DNA sequences
⊳ SNPs are associated with various complex diseases
⊳ Mostly locate within a gene or in a regulatory region that can affect
the genes function
⊳SNPs are often used interchangeably with mutations, polymorphism
and substitution
⊳ Polymorphisms in genes related to cytokine expression could
affect the susceptibility to different diseases

4. INTERFERON
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⊳ Interferons are protein family which produce antiviral and antiproliferative responses in cells
- No homology with type I IFNs
- Describe a factor with the ability to interfere
- With the growth of live influenza virus
⊳ IFN gamma, also known as IFNG, is a secreted protein that belongs to the type II interferon
family
⊳ All three major types differ in their;
• Primary protein sequences,
• Cognate receptors,
• Genetic loci,
• Cell types responsible for their production
First coined in 1957

5. CLASSIFICATION OF IFN
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1. Type I IFNs
 Encoded by 17 nonallelic genes
 Lack introns
 Located on chromosome 9 in humans
– Glycosylated proteins,160-200 amino acids
– Sharing 30% to 55% homology
2. Type II IFN
 Encoded by 17 nonallelic genes
 Lack introns
 140 amino acids and shares no homology with type I IFNs
3. Type III IFNs
 IFN molecules
– IL-28A, IL-28B, and IL-29
– Co-produced with IFN-β
– But act by binding to a different receptor from type I IFN receptors
IFNs are consisting of three major
types
Type I - IFN-α, -β,
-ε, -ω, Type II - IFN-γ,
Type III - IFN-λ1,
IFN-λ2 and IFN-λ3,
also called IL-29, IL-
28A and IL-28B

6. IFN-γ :Sources
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7. IFN-γ Structure

8. IFN-γ Structure
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⊳ Dimeric in solution
⊳ Each subunit
- 6 α-helices, that comprise 62% of the structure
- No β-sheet
- Composed of 140 amino acids
- IFN-γ is a homo-dimer
- Composed of a four chain bundle
- IFN-γR1 and IFN-γR2 genes

9. IFN-γ Gene

10. IFN-γ Gene
⊳ X-ray crystallography and Nuclear Magnetic Resonance (NMR) methods shows;
⊳ Cytogenetic Location: 12q14
⊳ Base pairs 68,154,769 to 68,159,740
⊳ Composed of a four chain bundle of IFN-γR1 and IFN-γR2 genes
⊳ IFN-γR1 and IFN-γR2 receptors are located on chromosomes 6q23-q24 and 21q22.11 in
human and chromosomes 10 and 16 in mouse, respectively.
⊳ IFN-γ homo-dimer binds to the two IFN-γR1 chains but does not directly interact with
IFN-γR2
⊳ IFN-γR2 has been shown to be essential for downstream signaling events;
binding of the IFN-γ homo-dimer to the pre-assembled receptor triggers downstream JAK-
STAT events that activate IFN-γ regulated genes
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11. IFN Function
⊳ Activating macrophages and enhancing their expression of
MHC class II molecules
- Resulting in enhanced antigen presentation to T cells
⊳ Regulates the expression of the major histocompatibility
complexes (MHC) I and II
- Involved in the antigen processing presentation pathways
⊳ Also mediates functions leukocyte attraction, maturation
and differentiation, natural killer (NK) cell
activity and immunoglobulin (Ig)
production and class switching in B cells
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12. IFNG Mutations
Several SNPs in this gene have reportedly been associated with immunologic diseases
- Such as aplastic anemia, hepatitis infection, systemic lupus erythematosus, and asthma
The first intron of IFN-γ gene contains a polymorphic microsatellite that has been closely
correlated with disease susceptibility
Some of the disease-associated SNPs are functional
The SNPs in the 59 untranslated regions (UTR) are translation-level regulators
Some SNPs in the introns may function to modify mRNA expression
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13. MaterialandMethods
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1. Retrival of SNPs datasets
2. Predicting deleterious nature of SNPs
- SIFT, PolyPhen, PPH2,SNAP2, Provean, CADD, ConDEL
3. Predicting the association of SNPs
- P-Mutant, PhD-SNP, SNP & GO, Meta SNP
4. Effect of SNPs on Protein stability
- MU-Pro, I-mutant, iStable
5. Analysis of Sequence consequences
- ConSurf
6. PTM Modification
- Musite Deep
7. Protein Modeling & Visualization
- Alpafold, PyMol,SAVES
8. Analysis of Ligand binding/ Protein-ligand docking
- PyRx, Discovery Bovia
9. Molecular Dynamic Simulation
- VMD and NAMD

14. Results
Screening of Functional Single Nucleotide Polymorphism (SNPs)
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15. Prediction of functional detrimental nsSNPs by SIFT and PolyPhen
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16. Prediction of deleterious SNPs by SIFT, PolyPhen and SNAP2
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dbSNP Variant A.A SIFT Score Prediction Polyphen
Score
Prediction Prediction SNAP2
Score
rs769209772 C/G G161R 0 Deleterious 0.923 Probably Damaging effect 81
rs377736305 C/T R152Q 0 Deleterious 0.967 Probably Damaging effect 77
rs755519988 G/A R130C 0 Deleterious 0.995 Probably Damaging effect 55
rs761801101 T/G K78T 0 Deleterious 0.984 Probably Damaging effect 56
rs867244009 T/A Y76F 0 Deleterious 0.998 Probably Damaging effect 70
rs564666653 A/G I72T 0 Deleterious 0.992 Probably Damaging effect 73
rs564666653 A/T I72N 0 Deleterious 0.997 Probably Damaging effect 79
rs1009245499 A/T V45E 0 Deleterious 0.963 Probably Damaging effect 79
rs1304053808 T/A M1L 0 Deleterious 0.956 Probably Damaging effect 84
rs1178805738 C/A D114Y 0.01 Deleterious 0.987 Probably Damaging effect 45
rs1477303678 T/C Y37C 0.01 Deleterious 0.991 Probably Damaging effect 59
rs369578383 C/A A164S 0.02 Deleterious 0.991 Probably Damaging neutral -3

17. Prediction of deleterious SNPs
- Provean, PPH2, CADD and ConDEL
PROVEAN PPH2 CADD CONDEL
dbSNP Variant A.A Prediction cutoff= -2.5 Prediction Score Score Score Prediction
rs769209772 C/G G161R Deleterious -3.04 Probably Damaging 0.992 22.7 0.471646 Neutral
rs377736305 C/T R152Q Deleterious -2.63 Probably Damaging 1 23.7 0.584477 Deleterious
rs755519988 G/A R130C Deleterious -6.301 Probably Damaging 1 24.7 0.588284 Deleterious
rs761801101 T/G K78T Deleterious -4.615 Probably Damaging 0.999 26.6 0.577837 Deleterious
rs867244009 T/A Y76F Deleterious -3.63 Probably Damaging 1 28 0.656726 Deleterious
rs564666653 A/G I72T Deleterious -4.52 Probably Damaging 0.998 25.3 0.613629 Deleterious
rs564666653 A/T I72N Deleterious -6.41 Probably Damaging 1 25.3 0.614522 Deleterious
rs1009245499 A/T V45E Deleterious -4.324 Probably Damaging 0.996 21 0.594845 Deleterious
rs1304053808 T/A M1L Neutral -1.921 Probably Damaging 0.984 23.5 0.33308 Neutral
rs1178805738 C/A D114Y Deleterious -4.244 Probably Damaging 1 22.5 0.576615 Deleterious
rs1477303678 T/C Y37C Deleterious -6.158 Probably Damaging 1 26.4 0.584235 Deleterious
rs369578383 C/A A164S Neutral -0.856 Possibily damaging 0.818 9.356 0.442043 Neutral
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18. Prediction of Disease Association nsSNPs
- P-Mut, SNP & GO, PhD-SNP, Meta-SNP
P-Mu SNP &
GO
PhD-SNP Meta SNP
dbSNP Variant A.A Prediction Score Effect Prediction Score Prediction Score
rs769209772 C/G G161R FALSE 0.4901 Neutral Neutral 3 Neutral 6
rs377736305 C/T R152Q FALSE 0.3049 Disease Disease 0 Neutral 3
rs755519988 G/A R130C TRUE 0.6878 Disease Disease 6 Disease 4
rs761801101 T/G K78T FALSE 0.4397 Disease Disease 3 Neutral 5
rs867244009 T/A Y76F TRUE 0.5444 Disease Disease 2 Neutral 0
rs564666653 A/G I72T TRUE 0.632 Disease Disease 4 Disease 0
rs564666653 A/T I72N TRUE 0.7265 Disease Disease 5 Disease 5
rs1009245499 A/T V45E TRUE 0.6811 Disease Disease 6 Disease 4
rs1304053808 T/A M1L TRUE 0.7101 Neutral Neutral 6 Neutral 5
rs1178805738 C/A D114Y FALSE 0.4585 Neutral Neutral 0 Neutral 1
rs1477303678 T/C Y37C FALSE 0.4691 Disease Disease 4 Disease 2
rs369578383 C/A A164S FALSE 0.3771 Neutral Neutral 9 Neutral 8
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19. Protein Stability Prediction
- MU-Pro, I-mutant, and iStable
Mu-Pro I-Mutant iStable
dbSNP Variant A.A Prediction Detal Delta Prediction RI Prediction Score
rs769209772 C/G G161R DECREASE stability -0.721782 Decrease 3 Decrease 0.596863
rs377736305 C/T R152Q DECREASE stability -0.609974 Decrease 7 Decrease 0.839532
rs755519988 G/A R130C DECREASE stability -0.613561 Decrease 6 Decrease 0.78906
rs761801101 T/G K78T DECREASE stability -0.826083 Increase 1 Decrease 0.577897
rs867244009 T/A Y76F DECREASE stability -0.70054301 Increase 3 Decrease 0.545853
rs564666653 A/G I72T DECREASE stability -1.61176 Decrease 8 Decrease 0.863103
rs564666653 A/T I72N DECREASE stability -1.4484849 Decrease 6 Decrease 0.839617
rs1009245499 A/T V45E DECREASE stability -1.128579 Decrease 9 Decrease 0.739799
rs1304053808 T/A M1L DECREASE stability -0.53571933 Decrease 4 Decrease 0.760701
rs1178805738 C/A D114Y DECREASE stability -0.14857625 Decrease 6 Increase 0.744133
rs1477303678 T/C Y37C DECREASE stability -1.0222426 Increase 3 Decrease 0.551278
rs369578383 C/A A164S DECREASE stability -0.80448176 Decrease 8 Decrease 0.790735
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20. Molecular Modelingand Dockingof the IFNG
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 The modeled 3D structure and sequence of IFNG
generated by Alpha-Fold
 The SDF file of ligand structures was converted to the
PDB format by using PyMol
 PROCHECK produced a RAMACHANDRAN plot
for each of the generated IFNG PDB structures
 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).
A.A ERRAT 3D verify Pro Check TM Align
CORE Allowed Generously Disallowed TM
Score
RMS
D
IFNG 95.2381 48.19% 95.50% 4.50% 0.00% 0.00%
R130C 97.9167 49.40% 96.10% 3.20% 0.60% 0.00% 0.99029 0.49
R152Q 95.2703 52.41% 94.80% 4.50% 0.60% 0.00% 0.99048 0.49
I72N 94.4444 45.18% 95.50% 4.50% 0.00% 0.00% 0.98851 0.53
I72T 96.6216 43.37% 97.40% 2.60% 0.00% 0.00% 0.99204 0.44
K78T 96.6216 51.81% 96.10% 3.20% 0.60% 0.00% 0.98802 0.55
V45E 94.7368 40.36% 95.50% 4.50% 0.00% 0.00% 0.99223 0.43
Y37C 98.6207 44.58% 95.50% 4.50% 0.00% 0.00% 0.98575 0.61
Y76F 97.9592 50.60% 94.20% 5.80% 0.00% 0.00% 0.98709 0.59

21. Conclusion
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In the present study, we analyzed the novel nsSNPs associated with the IFNG
gene and serve as a platform data for exhibiting virtual screening of IFNG via in
silico analyses and also revealed the molecular approach to study fluctuations in
activity, durability, affinity, and other attributes.
The study findings and results can assist in interpreting the impact of these
mutations and other strategy such as drug design, and so on.

22. References
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23. Thanks!
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