STRUCTURAL MODELING AND FUNCTIONAL PREDICTION OF IFN-Y GENE VARIANTS
DOI:
https://doi.org/10.55197/qjmhs.v4i3.160Keywords:
cytokine, IFNG, NKT cells, non-synonymous SNPsAbstract
IFN-γ is a key immunomodulatory cytokine primarily secreted by activated T lymphocytes, NK cells, NKT cells, and dendritic cells. The IFN-γ protein is encoded by the IFNG gene located on chromosome 9q14.3 and plays a central role in host immune responses. The current study employed an integrated computational approach to predict deleterious missense SNPs of the IFN-γ gene. These variants potentially disrupt the structural integrity and biological activity of IFN-γ, contributing to aberrant immune responses implicated in tumorigenesis and chronic inflammation. To assess the functional consequences of these mutations, molecular docking analyses were conducted. Through comprehensive screening, 12 deleterious nsSNPs were identified, localized within non-synonymous regions. SOPMA revealed that the IFN-γ protein is predominantly α-helical, constituting about 66.27% of the total secondary structure. Our results show high disorder scores for the G161R, R152Q, M1L, and A164S mutants, suggesting a loss of structural order, which may negatively impact protein function. Structural modeling was performed using AlphaFold, followed by validation with the SAVES v6.0 server. K28T, Y37C, and Y76F induced marked conformational changes involved in receptor binding, as evidenced by high RMSD values. Our results emphasize Laminin, Tamoxifen, Fulvestrant, Melanin, Parecoxib, and Rofecoxib. Both Laminin and Melanin demonstrated strong binding affinities with native and mutant IFN-γ structures, engaging crucial residues such as Phe115, Glu116, Phe105, and Val73. These residues are crucial for ligand binding and cytokine function, highlighting their therapeutic importance. Our findings provide insights for the development of targeted therapies for IFN-γ-related disorders, including autoimmune diseases, cancer, and infectious conditions. The novelty of this study lies in its comprehensive analysis of mutant IFN-γ forms, paving the way for precision medicine approaches tailored to genetically diverse populations. Further experimental validation is necessary to substantiate these findings and evaluate their clinical significance.
References
[1] AbdulAzeez, S., Borgio, J.F. (2016): In-silico computing of the most deleterious nsSNPs in HBA1 gene. – PloS One 11(1): 13p.
[2] Adeniji, S.E., Uba, S., Uzairu, A. (2020): In silico study for evaluating the binding mode and interaction of 1, 2, 4-triazole and its derivatives as potent inhibitors against Lipoate protein B (LipB). – Journal of King Saud University-Science 32(1): 475-485.
[3] Adzhubei, I., Jordan, D.M., Sunyaev, S.R. (2013): Predicting functional effect of human missense mutations using PolyPhen‐2. – Current Protocols in Human Genetics 76(1): 7-20.
[4] Angamuthu, K., Piramanayagam, S. (2017): Evaluation of in silico protein secondary structure prediction methods by employing statistical techniques. – Biomedical and Biotechnology Research Journal (BBRJ) 1(1): 29-36.
[5] Arshad, M., Bhatti, A., John, P. (2018): Identification and in silico analysis of functional SNPs of human TAGAP protein: A comprehensive study. – PloS One 13(1): 13p.
[6] Capriotti, E., Calabrese, R., Casadio, R. (2006): Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. – Bioinformatics 22(22): 2729-2734.
[7] Carugo, O., Pongor, S. (2001): A normalized root‐mean‐spuare distance for comparing protein three‐dimensional structures. – Protein Science 10(7): 1470-1473.
[8] Choi, Y., Sims, G.E., Murphy, S., Miller, J.R., Chan, A.P. (2012): Predicting the functional effect of amino acid substitutions and indels. – Plos One 7(10): 13p.
[9] Colovos, C., Yeates, T.O. (1993): Verification of protein structures: patterns of nonbonded atomic interactions. – Protein Science 2(9): 1511-1519.
[10] Dallakyan, S., Olson, A.J. (2014): Small-molecule library screening by docking with PyRx. – In Chemical Biology: Methods and Protocols, New York, NY: Springer New York 8p.
[11] Fareed, M.M., Ullah, S., Aziz, S., Johnsen, T.A., Shityakov, S. (2022): In-silico analysis of non-synonymous single nucleotide polymorphisms in human β-defensin type 1 gene reveals their impact on protein-ligand binding sites. – Computational Biology and Chemistry 98: 8p.
[12] Geourjon, C., Deleage, G. (1995): SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. – Bioinformatics 11(6): 681-684.
[13] Gnad, F., Baucom, A., Mukhyala, K., Manning, G., Zhang, Z. (2013): Assessment of computational methods for predicting the effects of missense mutations in human cancers. – BMC Genomics 14: 1-13.
[14] Griggs, N.D., Jarpe, M.A., Pace, J.L., Russell, S.W., Johnson, H.M. (1992): The N-terminus and C-terminus of IFN-gamma are binding domains for cloned soluble IFN-gamma receptor. – Journal of Immunology (Baltimore, Md.: 1950) 149(2): 517-520.
[15] Hasnain, M.J.U., Shoaib, M., Qadri, S., Afzal, B., Anwar, T., Abbas, S.H., Sarwar, A., Talha Malik, H.M., Tariq Pervez, M. (2020): Computational analysis of functional single nucleotide polymorphisms associated with SLC26A4 gene. – PLoS One 15(1): 20p.
[16] Isaacs, A., Lindenmann, J. (1957): Virus interference. I. The interferon. – Proceedings of the Royal Society of London, Series B-Biological Sciences 147(927): 258-267.
[17] Jahandideh, S., Zhi, D. (2014): Systematic investigation of predicted effect of nonsynonymous SNPs in human prion protein gene: a molecular modeling and molecular dynamics study. – Journal of Biomolecular Structure and Dynamics 32(2): 289-300.
[18] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A. (2021): Highly accurate protein structure prediction with AlphaFold. – Nature 596(7873): 583-589.
[19] Kaur, S., Ali, A., Ahmad, U., Siahbalaei, Y., Pandey, A.K., Singh, B. (2019): Role of single nucleotide polymorphisms (SNPs) in common migraine. – The Egyptian Journal of Neurology, Psychiatry and Neurosurgery 55: 1-7.
[20] Khan, S.M., Faisal, A.R.M., Nila, T.A., Binti, N.N., Hosen, M.I., Shekhar, H.U. (2021): A computational in silico approach to predict high-risk coding and non-coding SNPs of human PLCG1 gene. – PloS One 16(11): 22p.
[21] Khanna, N.R., Gerriets, V. (2020): Interferon. – StatPearls Publishing 6p.
[22] Kotenko, S.V., Durbin, J.E. (2017): Contribution of type III interferons to antiviral immunity: location, location, location. – Journal of Biological Chemistry 292(18): 7295-7303.
[23] López-Ferrando, V., Gazzo, A., De La Cruz, X., Orozco, M., Gelpí, J.L. (2017): PMut: a web-based tool for the annotation of pathological variants on proteins, 2017 update. – Nucleic Acids Research 45(W1): W222-W228.
[24] Magesh, R., George Priya Doss, C. (2014): Computational pipeline to identify and characterize functional mutations in ornithine transcarbamylase deficiency. – 3 Biotech 4: 621-634.
[25] Mahmud, Z., Malik, S.U.F., Ahmed, J., Azad, A.K. (2016): Computational Analysis of Damaging Single‐Nucleotide Polymorphisms and Their Structural and Functional Impact on the Insulin Receptor. – BioMed Research International 11p.
[26] Morris, G.M., Huey, R., Olson, A.J. (2008): Using autodock for ligand‐receptor docking. – Current Protocols in Bioinformatics 24(1): 8-14.
[27] Pacheco, A.G., Moraes, M.O. (2009): Genetic Polymorphisms of Infectious Diseases in Case‐Control Studies. – Disease Markers 27(3-4): 173-186.
[28] Qi, Z., Nie, P., Secombes, C.J., Zou, J. (2010): Intron-containing type I and type III IFN coexist in amphibians: refuting the concept that a retroposition event gave rise to type I IFNs. – The Journal of Immunology 184(9): 5038-5046.
[29] Reynard, M. (2002): Cytokine gene polymorphisms and their clinical relevance. – University of London, University College London (United Kingdom) 312p.
[30] Sang, Y., Liu, Q., Lee, J., Ma, W., McVey, D.S., Blecha, F. (2016): Expansion of amphibian intronless interferons revises the paradigm for interferon evolution and functional diversity. – Scientific Reports 6(1): 17p.
[31] Santhoshkumar, R., Yusuf, A. (2020): In silico structural modeling and analysis of physicochemical properties of curcumin synthase (CURS1, CURS2, and CURS3) proteins of Curcuma longa. – Journal of Genetic Engineering and Biotechnology 18(1): 9p.
[32] Savan, R., Ravichandran, S., Collins, J.R., Sakai, M., Young, H.A. (2009): Structural conservation of interferon gamma among vertebrates. – Cytokine & Growth Factor Reviews 20(2): 115-124.
[33] Sim, N.L., Kumar, P., Hu, J., Henikoff, S., Schneider, G., Ng, P.C. (2012): SIFT web server: predicting effects of amino acid substitutions on proteins. – Nucleic Acids Research 40(W1): W452-W457.
[34] Trott, O., Olson, A.J. (2010): AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. – Journal of Computational Chemistry 31(2): 455-461.
[35] Xu, D., Zhang, Y. (2011): Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. – Biophysical Journal 101(10): 2525-2534.
[36] Zhang, Y., Skolnick, J. (2005): TM-align: a protein structure alignment algorithm based on the TM-score. – Nucleic Acids Research 33(7): 2302-2309.
[37] Zhang, Y., Skolnick, J. (2004): SPICKER: a clustering approach to identify near‐native protein folds. – Journal of Computational Chemistry 25(6): 865-871.
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