218
Views
2
CrossRef citations to date
0
Altmetric
Research Article

In silico and structure-based assessment to classify VUS identified in the α-helical domain of BRCA2

ORCID Icon &
Pages 9879-9889 | Received 16 Jun 2022, Accepted 10 Nov 2022, Published online: 20 Nov 2022

References

  • Abkevich, V., Zharkikh, A., Deffenbaugh, A. M., Frank, D., Chen, Y., Shattuck, D., Skolnick, M. H., Gutin, A., & Tavtigian, S. V. (2004). Analysis of missense variation in human BRCA1 in the context of interspecific sequence variation. Journal of Medical Genetics, 41(7), 492–507. https://doi.org/10.1136/jmg.2003.015867
  • Agata, S., Tognazzo, S., Alducci, E., Matricardi, L., Moserle, L., Barana, D., & Montagna, M. (2020). Segregation analysis of the BRCA2 c.9227G > T variant in multiple families suggests a pathogenic role in breast and ovarian cancer predisposition. Scientific Reports, 10(1), 6. https://doi.org/10.1038/s41598-020-70729-0
  • Amadei, A., Linssen, A. B. M., & Berendsen, H. J. C. (1993). Essential dynamics of proteins Opportunities Connect with Wiley. Proteins: Structure, Function, and Bioinformatics, 17(4), 412–425. https://doi.org/10.1002/prot.340170408
  • 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. https://doi.org/10.1093/nar/gkw408
  • Berezin, C., Glaser, F., Rosenberg, J., Paz, I., Pupko, T., Fariselli, P., Casadio, R., & Ben-Tal, N. (2004). ConSeq: The identification of functionally and structurally important residues in protein sequences. Bioinformatics (Oxford, England), 20(8), 1322–1324. https://doi.org/10.1093/bioinformatics/bth070
  • Breast Cancer Linkage Consortium, T. (1999). Cancer risks in BRCA2 mutation carriers: The breast cancer linkage consortium. The Journal of the National Cancer Institute, 91(15), 1310–1316. https://doi.org/10.1093/jnci/91.15.1310
  • Chen, Y., Lu, H., Zhang, N., Zhu, Z., Wang, S., & Li, M. (2020). PremPS: Predicting the impact of missense mutations on protein stability. PLoS Computational Biology. 16, 1–22. https://doi.org/10.1371/journal.pcbi.1008543
  • Choi, Y., & Chan, A. P. (2015). PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics (Oxford, England), 31(16), 2745–2747. https://doi.org/10.1093/bioinformatics/btv195
  • 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), e46688. https://doi.org/10.1371/journal.pone.0046688
  • Eggington, J. M., Bowles, K. R., Moyes, K., Manley, S., Esterling, L., Sizemore, S., Rosenthal, E., Theisen, A., Saam, J., Arnell, C., Pruss, D., Bennett, J., Burbidge, L. A., Roa, B., & Wenstrup, R. J. (2014). A comprehensive laboratory-based program for classification of variants of uncertain significance in hereditary cancer genes. Clinical Genetics, 86(3), 229–237. https://doi.org/10.1111/cge.12315
  • Eisenhaber, F., Lijnzaad, P., Argos, P., Sander, C., & Scharf, M. (1995). The double cubic lattice method: Efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies. Journal of Computational Chemistry, 16(3), 273–284. https://doi.org/10.4324/9781003028253
  • Ernst, C., Hahnen, E., Engel, C., Nothnagel, M., Weber, J., Schmutzler, R. K., & Hauke, J. (2018). Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics. BMC Medical Genomics, 11(1), 35–10. https://doi.org/10.1186/s12920-018-0353-y.
  • Frank, T. S., Deffenbaugh, A. M., Reid, J. E., Hulick, M., Ward, B. E., Lingenfelter, B., Gumpper, K. L., Scholl, T., Tavtigian, S. V., Pruss, D. R., & Critchfield, G. C. (2002). Clinical characteristics of individuals with germline mutations in BRCA1 and BRCA2: Analysis of 10,000 individuals, from Myriad Genet. Journal of Clinical Oncology : official Journal of the American Society of Clinical Oncology, 20(6), 1480–1490. https://doi.org/10.1200/JCO.2002.20.6.1480
  • Guidugli, L., Carreira, A., Caputo, S., Ehlen, A., Galli, A., Monteiro, A. N. A., Neuhausen, S. L., Hansen, T. v O., Couch, F. J., & Vreeswijk, M. P. G. (2014). Functional assays for analysis of VUS in BRCA2. Human Mutation, 18, 1199–1216. https://doi.org/10.1002/humu.22478.Functional
  • Guidugli, L., Pankratz, V. S., Singh, N., Thompson, J., Erding, C. A., Engel, C., Schmutzler, R., Domchek, S., Nathanson, K., Radice, P., Singer, C., Tonin, P. N., Lindor, N. M., Goldgar, D. E., & Couch, F. J. (2013). A classification model for BRCA2 DNA binding domain missense variants based on homology-directed repair activity. Cancer Research, 73(1), 265–275. https://doi.org/10.1158/0008-5472.CAN-12-2081
  • Hess, B., Kutzner, C., Van Der Spoel, D., & Lindahl, E. (2008). GRGMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. Journal of Chemical Theory and Computation, 4(3), 435–447. https://doi.org/10.1021/ct700301q
  • Holloman, W. K. (2011). Unraveling the mechanism of BRCA2 in HR. Nature Structural & Molecular Biology, 18(7), 748–754. https://doi.org/10.1038/nsmb.2096.Unraveling
  • Kabsch, W., & Sander, C. (1983). Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22(12), 2577–2637. https://doi.org/10.1002/bip.360221211
  • Karplus, M., & McCammon, J. A. (2002). Molecular dynamics simulations of biomolecules. Nature Structural Biology, 9(9), 646–652. https://doi.org/10.1038/nsb0902-646
  • Khan, M. A., Siddiqui, M. Q., Kuligina, E., & Varma, A. K. (2022). Evaluation of conformational transitions of h-BRCA2 functional domain and unclassified variant Arg2502Cys using multimodal approach. International Journal of Biological Macromolecules, 209, 716–724. https://doi.org/10.1016/j.ijbiomac.2022.04.049
  • Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Jang, W., Karapetyan, K., Katz, K., Liu, C., Maddipatla, Z., Malheiro, A., McDaniel, K., Ovetsky, M., Riley, G., Zhou, G., … Maglott, D. R. (2018). ClinVar: Improving access to variant interpretations and supporting evidence. Nucleic Acids Research, 46(D1), D1062–D1067. https://doi.org/10.1093/nar/gkx1153
  • Lee, E., McKean-Cowdin, R., Ma, H., Chen, Z., Van Den Berg, D., Henderson, B. E., Bernstein, L., & Ursin, G. (2008). Evaluation of unclassified variants in the breast cancer susceptibility genes BRCA1 and BRCA2 using five methods: Results from a population-based study of young breast cancer patients. Breast Cancer Research, 10(1), 1–12. https://doi.org/10.1186/bcr1865
  • Li, J., Zou, C., Bai, Y., Wazer, D. E., Band, V., & Gao, Q. (2006). DSS1 is required for the stability of BRCA2. Oncogene, 25(8), 1186–1194. https://doi.org/10.1038/sj.onc.1209153
  • M. E., Richardson, C., Hu, K. Y., Lee, H., LaDuca, K., Fulk, K. M., Durda, A. M., Deckman, D. E., Goldgar, A., N., A., Monteiro, R., Gnanaolivu, S. N., Hart, E. C., Polley, E., Chao, T., Pesaran., & F. J., Couch. (2021). Strong functional data for pathogenicity or neutrality classify BRCA2 DNA-binding-domain variants of uncertain significance. American Journal of Human Genetics, 108(3), 458–468. https://doi.org/10.1016/j.ajhg.2021.02.005
  • R. A., Laskowski, J. A. C., Rullmann, M. W., MacArthur, R., Kaptein, J., & M., Thornton. (1996). AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR. Journal of Biomolecular NMR, 8(4), 477–486. https://doi.org/10.1007/BF00228148
  • Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W. W., Hegde, M., Lyon, E., Spector, E., Voelkerding, K., & Rehm, H. L. (2015). Standards and guidelines for the interpretation of sequence variants: A joint consensus recommendation of the American College of medical genetics and genomics and the association for molecular pathology. Genetics in Medicine: Official Journal of the American College of Medical Genetics, 17(5), 405–424. https://doi.org/10.1038/gim.2015.30
  • Rodrigues, C. H. M., Pires, D. E. V., & Ascher, D. B. (2018). DynaMut: Predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Research, 46(W1), W350–W355. https://doi.org/10.1093/nar/gky300
  • Ryckaert, J. P., Ciccotti, G., & Berendsen, H. J. C. (1977). Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. Journal of Computational Physics, 23(3), 327–341. https://doi.org/10.1016/0021-9991(77)90098-5
  • Sinha, S., & Wang, S. M. (2020). Classification of VUS and unclassified variants in BRCA1 BRCT repeats by molecular dynamics simulation classification of VUS and unclassified variants in BRCA1 BRCT repeats by molecular dynamics simulation. Computational and Structural Biotechnology Journal, 18, 723–736. https://doi.org/10.1016/j.csbj.2020.03.013
  • Sinha, S., Qin, Z., Tam, B., & Wang, S. M. (2022). Identification of deleterious variants of uncertain significance in BRCA2 BRC4 repeat through molecular. Briefings in Functional Genomics, 21(3), 202–215. https://doi.org/10.1093/bfgp/elac003
  • Sippl, M. J. (1993). Recognition of errors in three‐dimensional structures of proteins. Proteins: Structure, Function, and Bioinformatics, 17(4), 355–362. https://doi.org/10.1002/prot.340170404
  • Studer, G., Rempfer, C., Waterhouse, A. M., Gumienny, R., Haas, J., & Schwede, T. (2020). QMEANDisCo—Distance constraints applied on model quality estimation. Bioinformatics (Oxford, England), 36(6), 1765–1771. https://doi.org/10.1093/bioinformatics/btz828
  • Studer, R. A., Dessailly, B. H., & Orengo, C. A. (2013). Residue mutations and their impact on protein structure and function: Detecting beneficial and pathogenic changes. The Biochemical Journal, 449(3), 581–594. https://doi.org/10.1042/BJ20121221
  • Tang, H., & Thomas, P. D. (2016). PANTHER-PSEP: Predicting disease-causing genetic variants using position-specific evolutionary preservation. Bioinformatics (Oxford, England), 32(14), 2230–2232. https://doi.org/10.1093/bioinformatics/btw222
  • Tavtigian, S. V., Deffenbaugh, A. M., Yin, L., Judkins, T., Scholl, T., Samollow, P. B., De Silva, D., Zharkikh, A., & Thomas, A. (2006). Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral. Journal of Medical Genetics, 43(4), 295–305. https://doi.org/10.1136/jmg.2005.033878
  • Tavtigian, S. V., Greenblatt, M. S., Goldgar, D. E., & Boffetta, P. (2008). Assessing pathogenicity: Overview of results from the IARC unclassified genetic variants working group. Human Mutation, 29(11), 1261–1264. https://doi.org/10.1002/humu.20903
  • Toland, A. E., & Andreassen, P. R. (2017). DNA repair-related functional assays for the classification of BRCA1 and BRCA2 variants: A critical review and needs assessment. Journal of Medical Genetics, 54(11), 721–731. https://doi.org/10.1136/jmedgenet-2017-104707
  • Venkitaraman, A. R. (2014). Cancer suppression by the chromosome custodians, BRCA1 and BRCA2. Science (New York, N.Y.), 343(6178), 1470–1475. https://doi.org/10.1126/science.1252230
  • W., L. J., George, A., Kaminski, R. A., Friesner., & J., Tirado-Rives. (1976). Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. Journal of Physical Chemistry, 43, 54–58. https://doi.org/10.1021/jp003919d
  • Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., De Beer, T. A. P., Rempfer, C., Bordoli, L., Lepore, R., & Schwede, T. (2018). SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Research, 46(W1), W296–W303. https://doi.org/10.1093/nar/gky427
  • Wiederstein, M., & Sippl, M. J. (2007). ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Research, 35, W407–W410. https://doi.org/10.1093/nar/gkm290
  • Williams, R. S., & Glover, J. N. M. (2003). Structural consequences of a cancer-causing BRCA1-BRCT missense mutation. The Journal of Biological Chemistry, 278(4), 2630–2635. https://doi.org/10.1074/jbc.M210019200
  • Yang, H., Jeffrey, P. D., Miller, J., Kinnucan, E., Sun, Y., Thoma, N. H., Zheng, N., Chen, P.-L., Lee, W.-H., & Pavletich, N. P. (2002). BRCA2 function in DNA binding and recombination from a BRCA2[ndash]DSS1-ssDNA structure. Science (New York, N.Y.), 297(5588), 1837–1848. https://doi.org/10.1126/science.297.5588.1837
  • Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020). Improved protein structure prediction using predicted interresidue orientations. Proceedings of the National Academy of Sciences of the United States of America, 117(3), 1496–1503. https://doi.org/10.1073/pnas.1914677117

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.