LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,149
تعداد مقالات10,518
تعداد مشاهده مقاله45,415,424
تعداد دریافت فایل اصل مقاله11,291,214
Khalil, Husam. (1399). In Silico Approach for Prediction of the Structural and Functional Impacts of 184(T>C) Missense Mutation Identified in BRCA1 Gene in a Syrian Breast Cancer Patient. سامانه مدیریت نشریات علمی, 11(4), 399-409. doi: 10.30476/mejc.2020.81440.1009
Husam Khalil. "In Silico Approach for Prediction of the Structural and Functional Impacts of 184(T>C) Missense Mutation Identified in BRCA1 Gene in a Syrian Breast Cancer Patient". سامانه مدیریت نشریات علمی, 11, 4, 1399, 399-409. doi: 10.30476/mejc.2020.81440.1009
Khalil, Husam. (1399). 'In Silico Approach for Prediction of the Structural and Functional Impacts of 184(T>C) Missense Mutation Identified in BRCA1 Gene in a Syrian Breast Cancer Patient', سامانه مدیریت نشریات علمی, 11(4), pp. 399-409. doi: 10.30476/mejc.2020.81440.1009
Khalil, Husam. In Silico Approach for Prediction of the Structural and Functional Impacts of 184(T>C) Missense Mutation Identified in BRCA1 Gene in a Syrian Breast Cancer Patient. سامانه مدیریت نشریات علمی, 1399; 11(4): 399-409. doi: 10.30476/mejc.2020.81440.1009

In Silico Approach for Prediction of the Structural and Functional Impacts of 184(T>C) Missense Mutation Identified in BRCA1 Gene in a Syrian Breast Cancer Patient

Middle East Journal of Cancer
مقاله 3، دوره 11، شماره 4 - شماره پیاپی 44، دی 2020، صفحه 399-409    اصل مقاله (5.73 M)
نوع مقاله: Original Article(s)
شناسه دیجیتال (DOI): 10.30476/mejc.2020.81440.1009
نویسنده
Husam Khalil*
Al-Rasheed International University for Science and Technology (RU), Damascus, Syria
چکیده
Background: The classification of genetic variations depending on their clinical impacts is highly relevant for clinical decision-making. Therefore, predicting the effects of missense mutations using in silico tools has become a frequently employed approach. The objective of this study was to analyze the impacts of a previously detected BRCA1 missense mutation using an in silico prediction tool in the context of invasive breast cancer.
Methods: In this bioinformatics study, application of the in silico combination of tools Phyre2 to 184(T>C), a BRCA1 missense mutation previously characterized by Khalil et al. in 2018, we used to predict its clinical impacts.
Results: Incidence of the missense mutation caused a disorder in the zinc binding RING finger functional domain of BRCA1 protein. This incidence was considered to be a major contributor to the interaction of this protein with other proteins in signal transduction pathway in the mechanism of cellular response to DNA damage. The functional analysis also revealed that the detected missense mutation might significantly affect the function of both the protein and phenotype of the living organism.
Conclusion: In silico prediction confirmed the detrimental impact of the identified missense mutation in the exon 2 of BRCA1 gene on both the structural and functional properties of the generated BRCA1 protein.
کلیدواژه‌ها
In silico؛ Missense؛ 184(T>C)؛ Syria؛ Phyre2
اصل مقاله

How to cite this article:

Khalil H. In silico approach for prediction of the structural and functional impacts of 184(t>c) missense mutation identified in BRCA1 gene in a Syrian breast cancer patient. Middle East J Cancer. 2020;11(4): 399-409. doi: 10.30476/mejc.2020.81440.1009

مراجع
  1. Vallée MP, Di Sera TL, Nix DA, Paquette AM, Parsons MT, Bell R, et al. Adding in silico assessment of potential splice aberration to the integrated evaluation of BRCA gene unclassified variants. Hum Mutat. 2016;37(7):627-39. doi: 10.1002/humu.22973.
  2. Bendl J, Stourac J, Salanda O, Pavelka A, Wieben Eric D, Zendulka J, et al. Predict SNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Comput Biol. 2014;10(1).     https://doi.org/10.1371/journal.pcbi.1003440.
  3. Ernst C, Hahnen E, Engel C, Nothnagel M, J, Schmutzler RK, Hauke J. Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics. BMC Med Genomic. 2018;11(1):35. doi: 10.1186/s12920-018-0353-y.
  4. Kerr I, Cox H, Moyes K, Evans B, Burdett B, van Kan A, et al. Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes. J Community Genet. 2017;8(2): 87-95. doi: 10.1007/s12687-016-0289-x.
  5. Sadowski C, Kohlstedt D, Meisel C, Keller K, Becker K, Mackenroth L, et al. BRCA1/2 missense mutations and the value of in-silico analyses. Eur  J  Med Genet. 2017;60(11): 572-7. doi: 10.1016/j.ejmg.2017.08.005.
  6. Leong I, Stuckey A, Lai D, Skinner J, Love D. Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations. BMC Med Genet. 2015;16:34. doi: 10.1186/s12881-015-0176-z.
  7. Mathe E, Olivier M, Kato S, Ishioka C, Hainaut P, Tavtigian SV, et al. Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods. Nucleic Acids Res. 2006;34(5):1317-25. doi: 10.1093/nar/gkj518.
  8. Olatubosun A, Väliaho J, Härkönen J, Thusberg J, Vihinen M. PON-P: Integrated predictor for pathogenicity of missense variants. Hum Mutat. 2012;33(8):1166-74. doi: 10.1002/humu.22102.
  9. Pshennikova V, Barashkov N, Romanov G, Teryutin F, Solov’ev A, Gotovtsev N, et al. Comparison of predictive in silico tools on missense variants in GJB2, GJB6, and GJB3 genes associated with autosomal recessive deafness 1A (DFNB1A). Sci World J. 2019;1-9. doi: 10.1155/2019/5198931.
  10. Rahim F, Galehdari H, Mohammadi-asl J, Saki N. Regression modeling and meta-analysis of diagnostic accuracy of SNP-based pathogenicity detection tools for UGT1A1 gene mutation. Genet Res Int. 2013;2013:1-7. doi: 10.1155/2013/546909.
  11. Thompson B, Greenblatt M, Vallee M, Herkert J, Tessereau C, Young E, et al. Calibration of multiple in silico tools for predicting pathogenicity of mismatch repair gene missense substitutions. Hum Mutat. 2012; 34(1):255-65. doi: 10.1002/humu.22214.
  12. Thusberg J, Olatubosun A, Vihinen M. Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat. 2011;32(4):358-68. doi: 10.1002/humu.21445.
  13. Tutt A, Robson M, Garber J, Domchek S, Audeh M, Weitzel J, et al. Oral poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1 or BRCA2 mutations and advanced breast cancer: a proof-of-concept trial. Lancet. 2010;376(9737):235-44. doi: 10.1016/S0140-6736(10)60892-6.
  14. Walters-Sen L, Hashimoto S, Thrush D, Reshmi S, Gastier-Foster J, Astbury C, et al. Variability in pathogenicity prediction programs: impact on clinical diagnostics. Mol Genet Genomic Med. 2014;3(2):99-110. doi: 10.1002/mgg3.116.
  15. Tavtigian SV, Samollow PB, Silva Dd, Thomas A. An analysis of unclassified missense substitutions in human BRCA1. Fam Cancer. 2006;5(1):77–88. doi: 10.1007/s10689-005-2578-0.
  16. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073–81. doi: 10.1038/nprot.2009.86.
  17. Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods. 2014;11(4):361-2. doi: 10.1038/nmeth.2890.
  18. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):48–9. doi: 10.1038/nmeth0410-248.
  19. Santos C, Peixoto A, Rocha P, Pinto P, Bizarro S, Pinheiro M, et al. Pathogenicity evaluation of BRCA1 and BRCA2 unclassified variants identified in Portuguese breast/ovarian cancer families. J Mol Diagn. 2014;16(3):324-34. doi: 10.1016/j.jmoldx.2014.01.005.
  20. Kelley L, Mezulis S, Yates C, Wass M, Sternberg M. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10(6):845-858. doi: 10.1038/nprot.2015.053.
  21. Kelley L, Sternberg M. Protein structure prediction on the Web: a case study using the Phyre server. Nat Protoc. 2009;4(3):363-71. doi: 10.1038/nprot.2009.2.
  22. Bennett-Lovsey RM, Herbert AD, Sternberg ME, Kelley LA. Exploring the extremes of sequence/structure space with ensemble fold recognition in the program Phyre. Proteins. 2007;70 (3):611–25. doi: 10.1002/prot.21688.
  23. Khalil H, Monem F, Al-Quobaili F. Identification of three BRCA1/2 mutations and a study of the likelihood of an association with certain characteristics in Syrian familial breast cancer patients. Middle East J Cancer. 2018;9(4):274-281.
  24. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993;2(9):1511-9. doi: 10.1002/pro.5560020916.
  25. Pontius J, Richelle J, Wodak SJ. Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol. 1996;264(1):121-36. doi: 10.1006/jmbi.1996.0628
  26. O’Donovan PJ, Livingston DM. BRCA1 and BRCA2: breast/ovarian cancer susceptibility gene products and participants in DNA double-strand break repair. Carcinogenesis. 2010;31(6):961–7. doi: 10.1093/carcin/bgq069.
  27. Johnson NC, Kruk PA. BRCA1 zinc ring finger domain disruption alters caspase response in ovarian surface epithelial cells. Cancer Cell Int. 2002;2:7. doi: 10.1186/1475-2867-2-7.
آمار
تعداد مشاهده مقاله: 6,550
تعداد دریافت فایل اصل مقاله: 6,021


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب