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In silico scanning of structural and functional deleterious nsSNPs in Arabidopsis thaliana’s SOG1 protein, using molecular dynamic simulation approaches

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Pages 11629-11646 | Received 28 Jul 2022, Accepted 02 Jan 2023, Published online: 03 Feb 2023

References

  • Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010). A method and server for predicting damaging missense mutations. Nature Methods, 7(4), 248–249.
  • Ahmad, S. U., Ali, Y., Jan, Z., Rasheed, S., Nazir, N. u. A., Khan, A., Rukh Abbas, S., Wadood, A., & Rehman, A. U. (2022). Computational screening and analysis of deleterious nsSNPs in human p 14ARF (CDKN2A gene) protein using molecular dynamic simulation approach. Journal of Biomolecular Structure and Dynamics, 1–12. https://doi.org/10.1080/07391102.2022.2059570
  • Ahmad, S. U., Khan, M. S., Jan, Z., Khan, N., Ali, A., Rehman, N., Haq, M., Khan, U., Bashir, Z., & Tayyab, M. (2021). Genome wide association study and phylogenetic analysis of novel SARS-COV-2 virus among different countries. Pakistan Journal of Pharmaceutical Sciences, 34 (4), 1305–1313.
  • Amir, M., Kumar, V., Mohammad, T., Dohare, R., Hussain, A., Rehman, M. T., Alam, P., Alajmi, M. F., Islam, A., Ahmad, F., & Hassan, M. I. (2019). Investigation of deleterious effects of nsSNPs in the POT1 gene: A structural genomics‐based approach to understand the mechanism of cancer development. Journal of Cellular Biochemistry, 120(6), 10281–10294.
  • Andrzej, J., Magdalena, G., Beata, H., Katarzyna, D., & Grazyna, J.-W. (2014). SNP genetic diversity within a fragment of the gene myo15a responsible for the hearing process in a population of farmed and free-living animals of the canidae family/SNP. Acta Veterinaria, 64(3), 358–366. https://doi.org/10.2478/acve-2014-0034
  • Ashkenazy, H., Abadi, S., Martz, E., Chay, O., Mayrose, I., Pupko, T., & Ben-Tal, N. (2016). ConSurf 2016: An improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Research, 44(W1), W344–W350.
  • Bashir, Z., Ahmad, S. U., Kiani, B. H., Jan, Z., Khan, N., Khan, U., Haq, I., Zahir, F., Qadus, A., & Mahmood, T. (2021 ). Immunoinformatics approaches to explore B and T cell epitope-based vaccine designing for SARS-CoV-2 Virus. Pakistan Journal of Pharmaceutical Sciences, 34, 345–352.
  • Bellato, M., De Marchi, D., Gualtieri, C., Sauta, E., Magni, P., Macovei, A., & Pasotti, L. (2019). A bioinformatics approach to explore MicroRNAs as tools to bridge pathways between plants and animals. Is DNA damage response (DDR) a potential target process? Frontiers in Plant Science, 10, 1535.
  • Bolser, D., Staines, D. M., Pritchard, E., & Kersey, P. (2016). Ensembl plants: Integrating tools for visualizing, mining, and analyzing plant genomics data. In Plant bioinformatics (pp. 115–140). Springer.
  • Capriotti, E., & Fariselli, P. (2017). PhD-SNPg: A webserver and lightweight tool for scoring single nucleotide variants. Nucleic Acids Research, 45(W1), W247–W252.
  • Capriotti, E., Fariselli, P., & Casadio, R. (2005). I-Mutant2.0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic acids Research, 33(Web Server issue), W306–W310. https://doi.org/10.1093/nar/gki375
  • Case, D. A., Aktulga, H. M., Belfon, K., Ben-Shalom, I., Brozell, S. R., Cerutti, D., Cheatham, T., Cruzeiro, V. W. D., Darden, T., & Duke, R. E. (2021). Amber Reference Manual.
  • Case, D. A., Belfon, K., Ben-Shalom, I., Brozell, S. R., Cerutti, D., Cheatham, T., Cruzeiro, V. W. D., Darden, T., Duke, R. E., & Giambasu, G. (2020). Amber 2020.
  • Cheng, J., Randall, A., & Baldi, P. (2006). Prediction of protein stability changes for single‐site mutations using support vector machines. Proteins: Structure, Function, and Bioinformatics, 62(4), 1125–1132.
  • Choi, Y., & Chan, A. P. (2015). PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics, 31(16), 2745–2747. https://doi.org/10.1093/bioinformatics/btv195
  • Cimini, S., Gualtieri, C., Macovei, A., Balestrazzi, A., De Gara, L., & Locato, V. (2019). Redox balance-DDR-miRNA triangle: Relevance in genome stability and stress responses in plants. Frontiers in Plant Science, 10, 989.
  • Consortium, U (2015). UniProt: A hub for protein information. Nucleic Acids Research, 43(D1), D204–D212.
  • Dakal, T. C., Kala, D., Dhiman, G., Yadav, V., Krokhotin, A., & Dokholyan, N. V. (2017). Predicting the functional consequences of non-synonymous single nucleotide polymorphisms in IL8 gene. Scientific Reports, 7 (1), 1–18.
  • Davalli, P., Marverti, G., Lauriola, A., & D’Arca, D. (2018). Targeting oxidatively induced DNA damage response in cancer: Opportunities for novel cancer therapies. Oxidative Medicine and Cellular Longevity, 2018, 2389523.
  • Davidchack, R. L., Handel, R., & Tretyakov, M. (2009). Langevin thermostat for rigid body dynamics. The Journal of Chemical Physics, 130(23), 234101.
  • DeLano, W. L. (2002). Pymol: An open-source molecular graphics tool. CCP4 Newsletter on Protein Crystallography, 40(1), 82–92.
  • Diederichs, S., Bartsch, L., Berkmann, J. C., Fröse, K., Heitmann, J., Hoppe, C., Iggena, D., Jazmati, D., Karschnia, P., Linsenmeier, M., Maulhardt, T., Möhrmann, L., Morstein, J., Paffenholz, S. V., Röpenack, P., Rückert, T., Sandig, L., Schell, M., Steinmann, A., … Wullenkord, R. (2016). The dark matter of the cancer genome: aberrations in regulatory elements, untranslated regions, splice sites, non‐coding RNA and synonymous mutations. EMBO Molecular Medicine, 8(5), 442–457.
  • Garcia-Hernandez, M., Berardini, T., Chen, G., Crist, D., Doyle, A., Huala, E., Knee, E., Lambrecht, M., Miller, N., Mueller, L., Mundodi, S., Reiser, L., Rhee, S., Scholl, R., Tacklind, J., Weems, D., Wu, Y., Xu, I., Yoo, D., Yoon, J., & Zhang, P. (2002). TAIR: A resource for integrated Arabidopsis data. Functional & Integrative Genomics, 2(6), 239–253. https://doi.org/10.1007/s10142-002-0077-z
  • Ghosh, I., Mukherjee, A., & Mukherjee, A. (2017). In planta genotoxicity of nZVI: influence of colloidal stability on uptake, DNA damage, oxidative stress and cell death. Mutagenesis, 32(3), 371–387.
  • Halim, S. A., Waqas, M., Khan, A., & Al-Harrasi, A. (2021). In silico prediction of novel inhibitors of SARS-CoV-2 main protease through structure-based virtual screening and molecular dynamic simulation. Pharmaceuticals, 14 (9), 896. https://doi.org/10.1016/j.neuron.2018.08.011
  • Hollingsworth, S. A., & Dror, R. O. (2018). Molecular dynamics simulation for all. Neuron, 99 (6), 1129–1143.
  • Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: visual molecular dynamics. Journal of Molecular Graphics, 14(1), 33–38.
  • Izadi, S. (2016). Optimal Point Charge Approximation: From 3-Atom Water Molecule to Million-Atom Chromatin Fiber. Virginia Tech.
  • Jan, Z., Ahmad, S. U., Amara Qadus, Y. A., Sajjad, W., Rais, F., Tanveer, S., Khan, M. S., & Haq, I. (2021). Insilico structural and functional assessment of hypothetical protein L345_13461 from Ophiophagus hannah. Pure and Applied Biology (PAB), 10 (4), 1109–1118.
  • Jensen, L. J., Kuhn, M., Stark, M., Chaffron, S., Creevey, C., Muller, J., Doerks, T., Julien, P., Roth, A., Simonovic, M., Bork, P., & von Mering, C. (2009). STRING 8—a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Research, 37(Database), D412–D416. https://doi.org/10.1093/nar/gkn760
  • Judd, J., Lovas, J., & Huang, G. N. (2019). Defined factors to reactivate cell cycle activity in adult mouse cardiomyocytes. Scientific Reports, 9(1), 1–13. https://doi.org/10.1038/s41598-019-55027-8
  • Kalia, N., Sharma, A., Kaur, M., Kamboj, S. S., & Singh, J. (2016). A comprehensive in silico analysis of non-synonymous and regulatory SNPs of human MBL2 gene. Springerplus, 5 (1), 1–14.
  • Kasahara, K., Fukuda, I., & Nakamura, H. (2014). A novel approach of dynamic cross correlation analysis on molecular dynamics simulations and its application to Ets1 dimer–DNA complex. PloS One, 9(11), e112419.
  • Kräutler, V., van Gunsteren, W. F., & Hünenberger, P. H. (2001). A fast SHAKE algorithm to solve distance constraint equations for small molecules in molecular dynamics simulations. Journal of Computational Chemistry, 22(5), 501–508. https://doi.org/10.1002/1096-987X(20010415)22:5<501::AID-JCC1021>3.0.CO;2-V
  • Kumar, R., Kumar, R., Goel, H., & Tanwar, P. (2022). Computational investigation reveals that the mutant strains of SARS-CoV2 have differential structural and binding properties. Computer Methods and Programs in Biomedicine, 215, 106594. https://doi.org/10.1016/j.jmb.2008.10.018
  • Likić, V. A., Gooley, P. R., Speed, T. P., & Strehler, E. E. (2005). A statistical approach to the interpretation of molecular dynamics simulations of calmodulin equilibrium dynamics. Protein Science, 14(12), 2955–2963. https://doi.org/10.1110/ps.051681605
  • 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.
  • Lu, Y.-F., Mauger, D. M., Goldstein, D. B., Urban, T. J., Weeks, K. M., & Bradrick, S. S. (2015). IFNL3 mRNA structure is remodeled by a functional non-coding polymorphism associated with hepatitis C virus clearance. Scientific Reports, 5(1), 1–14. https://doi.org/10.1038/srep16037
  • Maisuradze, G. G., Liwo, A., & Scheraga, H. A. (2009) Principal component analysis for protein folding dynamics. Journal of Molecular Biology, 385 (1), 312–329.
  • Martens, M., Horres, R., Wendeler, E., & Reiss, B. (2020). The importance of ATM and ATR in Physcomitrella patens DNA damage repair, development, and gene targeting. Genes, 11 (7), 752. https://doi.org/10.1073/pnas.1608829113
  • Maslowska, K. H., Makiela‐Dzbenska, K., & Fijalkowska, I. J. (2019). The SOS system: A complex and tightly regulated response to DNA damage. Environmental and Molecular Mutagenesis, 60(4), 368–384.
  • Meza, J. C. (2010). Steepest descent. WIREs Computational Statistics, 2(6), 719–722. https://doi.org/10.1002/wics.117
  • Molecular Operating Environment. (2022). 2022:02. Chemical Computing Group ULC: Sherbrooke St. West Suite #910, Montreal, QC, Canada, H3A 2R7.
  • Nastasi, C., Mannarino, L., & D’Incalci, M. (2020). DNA damage response and immune defense. International Journal of Molecular Sciences, 21(20), 7504. https://doi.org/10.3390/ijms21207504
  • Ng, P. C., & Henikoff, S. (2003). SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Research, 31(13), 3812–3814.
  • OriginLab Corporation. (2021). Northampton, MA, USA.
  • Owji, H., Eslami, M., Nezafat, N., & Ghasemi, Y. (2020). In silico elucidation of deleterious non-synonymous SNPs in SHANK3, the autism spectrum disorder gene. Journal of Molecular Neuroscience, 70 (10), 1649–1667. https://doi.org/10.1038/s41598-017-06575-4
  • Pagano, A., Gualtieri, C., Mutti, G., Raveane, A., Sincinelli, F., Semino, O., Balestrazzi, A., Macovei, A. (2022). Identification and characterization of SOG1 (suppressor of gamma response 1) homologues in plants using data mining resources and gene expression profiling. Genes, 13(4), 667.
  • Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera—a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612.
  • Press, W. H., Flannery, B. P., Teukolsky, S. A., Vetterling, W. T., & Kramer, P. B. (1987). Numerical recipes: The art of scientific computing. Physics Today, 40(10), 120–122. https://doi.org/10.1063/1.2820230
  • Roe, D. R., & Cheatham, T. E. III. (2013). PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data. Journal of Chemical Theory and Computation, 9(7), 3084–3095.
  • Roitinger, E., Hofer, M., Köcher, T., Pichler, P., Novatchkova, M., Yang, J., Schlögelhofer, P., & Mechtler, K. (2015). Quantitative phosphoproteomics of the ataxia telangiectasia-mutated (ATM) and ataxia telangiectasia-mutated and Rad3-related (ATR) dependent DNA damage response in Arabidopsis thaliana*[S]. Molecular & Cellular Proteomics, 14 (3), 556–571. https://doi.org/10.1007/s00018-014-1666-4
  • Ryu, T. H., Go, Y. S., Choi, S. H., Kim, J. I., Chung, B. Y., & Kim, J. H. (2019). SOG 1‐dependent NAC 103 modulates the DNA damage response as a transcriptional regulator in Arabidopsis. The Plant Journal, 98 (1), 83–96.
  • Sakamoto, A. N., Sakamoto, T., Yokota, Y., Teranishi, M., Yoshiyama, K. O., & Kimura, S. (2021). SOG1, a plant‐specific master regulator of DNA damage responses, originated from nonvascular land plants. Plant Direct, 5 (12), e370. https://doi.org/10.1111/tpj.14201
  • Salomon-Ferrer, R., Gotz, A. W., Poole, D., Le Grand, S., & Walker, R. C. (2013). Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. Journal of Chemical Theory and Computation, 9(9), 3878–3888.
  • Sengupta, A., Li, Z., Song, L. F., Li, P., & Merz, K. M. Jr. (2021). Parameterization of monovalent ions for the Opc3, Opc, Tip3p-Fb, and Tip4p-Fb water models. Journal of Chemical Information and Modeling, 61(2), 869–880. https://doi.org/10.1021/acs.jcim.0c01390
  • Tarapara, B., & Shah, F. (2022). An in-silico analysis to identify structural, functional and regulatory role of SNPs in hMRE11. Journal of Biomolecular Structure and Dynamics, 1–15.
  • Waterworth, W. M., Footitt, S., Bray, C. M., Finch-Savage, W. E., & West, C. E. (2016). DNA damage checkpoint kinase ATM regulates germination and maintains genome stability in seeds. Proceedings of the National Academy of Sciences, 113 (34), 9647–9652.
  • Weiser, J., Shenkin, P. S., & Still, W. C. (1999). Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). Journal of Computational Chemistry, 20(2), 217–230. https://doi.org/10.1002/(SICI)1096-987X(19990130)20:2<217::AID-JCC4>3.0.CO;2-A
  • Welner, D. H., Deeba, F., Leggio, L. L., & Skriver, K. (2016). NAC transcription factors: From structure to function in stress-associated networks. In Plant transcription factors (pp. 199–212). Elsevier.
  • Yan, S., Sorrell, M., & Berman, Z. (2014). Functional interplay between ATM/ATR-mediated DNA damage response and DNA repair pathways in oxidative stress. Cellular and Molecular Life Sciences : CMLS, 71 (20), 3951–3967.
  • Yoshiyama, K., Conklin, P. A., Huefner, N. D., & Britt, A. B. (2009). Suppressor of gamma response 1 (SOG1) encodes a putative transcription factor governing multiple responses to DNA damage. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 12843–12848.
  • Yoshiyama, K. O., & Kimura, S. (2018). Ser-Gln sites of SOG1 are rapidly hyperphosphorylated in response to DNA double-strand breaks. Plant Signaling & Behavior, 13(6), e1477904.
  • Zhang, M., Huang, C., Wang, Z., Lv, H., & Li, X. (2020). In silico analysis of non-synonymous single nucleotide polymorphisms (nsSNPs) in the human GJA3 gene associated with congenital cataract. BMC molecular and Cell Biology, 21(1), 1–13.

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