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. 2021 Nov 30;76(5):1245-1253.
doi: 10.22092/ari.2021.356106.1780. eCollection 2021 Nov.

Investigation of Interferon Gamma Activity Using Bioinformatics Methods

N E Hassan  1 , A A Al-Janabi  1
Affiliations

Investigation of Interferon Gamma Activity Using Bioinformatics Methods

N E Hassan et al. Arch Razi Inst. .

Abstract

Breast cancer grows from the breast tissue and is a severe health problem worldwide. Genetics is believed to be the primary cause of all cases of breast cancer via gene mutation. Bioinformatics methodology has been used to determine the sequences and structures of bioactive substances. This study aimed to analyze the function and structure of the Interferon Gamma (IFNγ) in healthy controls and patients with breast cancer using bioinformatics methods. Blood samples were collected from 75 patients with breast cancer and 25 healthy subjects as control samples. The results showed transition mutation (30%) and transversion mutation (70%) in patients with breast cancer. Moreover, missense mutations (84%) and silent mutations (16%) were detected by BLAST. In addition, the amino acid of the IFNγ protein consisting of alpha-helical, β-sheet, and coil of secondary structure was determined in this study using BioEdit. The results of the physicochemical properties of the IFNγ protein reflect the function, stability, molecular weight, isoelectric point, and instability index of the IFNγ protein using ProtParam. Moreover, the results of mutation affected the percentage of alpha-helix, β-turns, and coil in breast cancer patients compared to healthy groups with reference of NCBI using PSIpred program. Additionally, the PHYRE2 server and RasMol program showed a tertiary structure of the IFNγ protein in breast cancer patients. Furthermore, the STRING program revealed the poly IFNγ protein interacted with other proteins to perform its functions normally. From the recorded data in the current study, it was concluded that IFNγ is considered a marker for patients with breast cancer.

Keywords: Bioinformatics Technique; Breast Cancer; Interferon Gamma.

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Figures

Figure 1
Figure 1
Agarose gel electrophoresis pattern showing the conventional PCR for the IFNγ gene in infected and non-infected samples
Figure 2
Figure 2
Confirmation of the IFNγ protein using the BioEdit software for drawing structure protein (helixes, β-sheets, and coils) of amino acids
Figure 3
Figure 3
IFNγ tertiary structure prediction modeling using PHYRE2 server illustrating the confidence in the model along its length. The colors from high (red) to low (blue) use a rainbow spectrum (from N→C terminus)
Figure 4
Figure 4
IFNγ tertiary structure prediction modeling using PHYRE2 server showing healthy control subjects (A), and the IFNγ retrieved from NCBI (B) illustrating the confidence in the model along its length. The colors from high (red) to low (blue) use a rainbow spectrum (from N→C terminus)
Figure 5
Figure 5
Network interaction of the IFNγ (red color) with other proteins
See this image and copyright information in PMC

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