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Research Article

与布鲁加达综合征相关的SCN5A突变蛋白理化性质的生物信息学见解

卷 30, 期 15, 2023

发表于: 30 December, 2022

页: [1776 - 1796] 页: 21

弟呕挨: 10.2174/0929867330666221130112650

价格: $65

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摘要

背景：Brugada 综合征 (BrS) 是一种心律失常，通常与心源性猝死的强烈倾向有关。恶性室性心律失常可能继发于心脏钠电压门控 Na(v)1.5 信道 (SCN5A) 的功能障碍。 目的：本研究旨在使用一组生物信息学工具对与 BrS 相关的 SCN5A 突变体的理化特性进行多参数计算分析。 方法：校准内部算法以在双盲试验中计算每个串行的极性指数法 (PIM) 概况和蛋白质内在无序易感性 (PIDP) 概况，并使用专门用于基因组分析的计算机进程。 结果：SCN5A 突变蛋白的电荷/极性和 PIDP 配置文档中的特定规律使分类学的重新创建成为可能，使我们能够提出一种生物信息学方法，利用 PIM 配置文档从其串行中识别这组蛋白质。 结论：生物信息学进程可以再现 BrS 相关 SCN5A 突变蛋白的特征 PIM 和 PIDP 图谱。这些信息有助于更好地了解这些改变的蛋白质。

关键词: SCN5A，SCN5A基因，SNC5A突变蛋白，结构蛋白质组学，生物信息学，内在疾病易感性谱，极性指数方法谱。

« Previous
[1]
Brugada, P.; Brugada, J. Right bundle branch block, persistent ST segment elevation and sudden cardiac death: A distinct clinical and electrocardiographic syndrome. J. Am. Coll. Cardiol., 1992, 20(6), 1391-1396.
[http://dx.doi.org/10.1016/0735-1097(92)90253-J] [PMID: 1309182]
[2]
Brugada, J.; Campuzano, O.; Arbelo, E.; Sarquella-Brugada, G.; Brugada, R. Present status of brugada syndrome. J. Am. Coll. Cardiol., 2018, 72(9), 1046-1059.
[http://dx.doi.org/10.1016/j.jacc.2018.06.037] [PMID: 30139433]
[3]
Li, K.H.C.; Lee, S.; Yin, C.; Liu, T.; Ngarmukos, T.; Conte, G.; Yan, G.X.; Sy, R.W.; Letsas, K.P.; Tse, G. Brugada syndrome: A comprehensive review of pathophysiological mechanisms and risk stratification strategies. Int. J. Cardiol. Heart Vasc., 2020, 26, 100468.
[http://dx.doi.org/10.1016/j.ijcha.2020.100468] [PMID: 31993492]
[4]
Barajas-Martínez, H.; Hu, D.; Antzelevitch, C. Bases genéticas y moleculares del síndrome de Brugada mediado por canales de sodio. Arch. Cardiol. Mex., 2013, 83(4), 295-302.
[http://dx.doi.org/10.1016/j.acmx.2013.10.001] [PMID: 24269159]
[5]
Wilde, A.A.M.; Amin, A.S. Clinical spectrum of SCN5A mutations. JACC Clin. Electrophysiol., 2018, 4(5), 569-579.
[http://dx.doi.org/10.1016/j.jacep.2018.03.006] [PMID: 29798782]
[6]
Savio-Galimberti, E.; Argenziano, M.; Antzelevitch, C. Cardiac arrhythmias related to sodium channel dysfunction. Handb. Exp. Pharmacol., 2017, 246, 331-354.
[http://dx.doi.org/10.1007/164_2017_43] [PMID: 28965168]
[7]
Qurban, A.M; Polanco, C.; Márquez, M.F.; Uversky, V.N.; Buhse, T; Arias-Estrada, M.O Bioinformatics analysis of dysfunctional (mutated) proteins of cardiac ion channels underlying the Brugada syndrome. Data Sci., 2022.
[8]
van Hoorn, F.; Campian, M.E.; Spijkerboer, A.; Blom, M.T.; Planken, R.N.; van Rossum, A.C.; de Bakker, J.M.T.; Wilde, A.A.M.; Groenink, M.; Tan, H.L. SCN5A mutations in Brugada syndrome are associated with increased cardiac dimensions and reduced contractility. PLoS One, 2012, 7(8), e42037.
[http://dx.doi.org/10.1371/journal.pone.0042037] [PMID: 22876298]
[9]
Polanco, C.; Uversky, V.N.; Márquez, M.F.; Buhse, T.; Estrada, M.A.; Huberman, A. Bioinformatics characterisation of the (mutated) proteins related to Andersen-Tawil syndrome. Math. Biosci. Eng., 2019, 16(4), 2532-2548.
[http://dx.doi.org/10.3934/mbe.2019127] [PMID: 31137226]
[10]
Polanco, C.; Samaniego Mendoza, J.L.; Buhse, T.; Uversky, V.N.; Bañuelos Chao, I.P.; Bañuelos Cedano, M.A.; Tavera, F.M.; Tavera, D.M.; Falconi, M.; Ponce de León, A.V. On the regularities of the polar profiles of proteins related to ebola virus infection and their functional domains. Cell Biochem. Biophys., 2018, 76(3), 411-431.
[http://dx.doi.org/10.1007/s12013-018-0839-4] [PMID: 29511990]
[11]
Xue, B.; Dunbrack, R.L.; Williams, R.W.; Dunker, A.K.; Uversky, V.N. PONDR-FIT: A meta-predictor of intrinsically disordered amino acids. Biochim. Biophys. Acta. Proteins Proteomics, 2010, 1804(4), 996-1010.
[http://dx.doi.org/10.1016/j.bbapap.2010.01.011] [PMID: 20100603]
[12]
Apweiler, R.; Bairoch, A.; Wu, C.H.; Barker, W.C.; Boeckmann, B.; Ferro, S.; Gasteiger, E.; Huang, H.; Lopez, R.; Magrane, M.; Martin, M.J.; Natale, D.A.; O’Donovan, C.; Redaschi, N.; Yeh, L.S. UniProt: The universal protein knowledgebase. Nucleic Acids Res., 2004, 32(90001), 115D-119.
[http://dx.doi.org/10.1093/nar/gkh131] [PMID: 14681372]
[13]
Mészáros, B.; Erdős, G.; Dosztányi, Z. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res., 2018, 46(W1), W329-W337.
[http://dx.doi.org/10.1093/nar/gky384] [PMID: 29860432]
[14]
Piovesan, D.; Tabaro, F.; Mičetić, I.; Necci, M.; Quaglia, F.; Oldfield, C.J.; Aspromonte, M.C.; Davey, N.E.; Davidović, R.; Dosztányi, Z.; Elofsson, A.; Gasparini, A.; Hatos, A.; Kajava, A.V.; Kalmar, L.; Leonardi, E.; Lazar, T.; Macedo-Ribeiro, S.; Macossay-Castillo, M.; Meszaros, A.; Minervini, G.; Murvai, N.; Pujols, J.; Roche, D.B.; Salladini, E.; Schad, E.; Schramm, A.; Szabo, B.; Tantos, A.; Tonello, F.; Tsirigos, K.D.; Veljković, N.; Ventura, S.; Vranken, W.; Warholm, P.; Uversky, V.N.; Dunker, A.K.; Longhi, S.; Tompa, P.; Tosatto, S.C.E. DisProt 7.0: A major update of the database of disordered proteins. Nucleic Acids Res., 2017, 45(D1), D219-D227.
[http://dx.doi.org/10.1093/nar/gkw1056] [PMID: 27899601]
[15]
Polanco, C.; Castañón-González, J.A.; Uversky, V.N.; Buhse, T.; Samaniego Mendoza, J.L.; Calva, J.J. Electronegativity and intrinsic disorder of preeclampsia-related proteins. Acta Biochim. Pol., 2017, 64(1), 99-111.
[PMID: 27824362]
[16]
Ortiz-Bonnin, B.; Rinné, S.; Moss, R.; Streit, A.K.; Scharf, M.; Richter, K.; Stöber, A.; Pfeufer, A.; Seemann, G.; Kääb, S.; Beckmann, B.M.; Decher, N. Electrophysiological characterization of a large set of novel variants in the SCN5A-gene: identification of novel LQTS3 and BrS mutations. Pflugers Arch., 2016, 468(8), 1375-1387.
[http://dx.doi.org/10.1007/s00424-016-1844-3] [PMID: 27287068]
[17]
Rivaud, M.R.; Baartscheer, A.; Verkerk, A.O.; Beekman, L.; Rajamani, S.; Belardinelli, L.; Bezzina, C.R.; Remme, C.A. Enhanced late sodium current underlies pro-arrhythmic intracellular sodium and calcium dysregulation in murine sodium channelopathy. Int. J. Cardiol., 2018, 263, 54-62.
[http://dx.doi.org/10.1016/j.ijcard.2018.03.044] [PMID: 29754923]
[18]
Zhou, J.; Oldfield, C.J.; Yan, W.; Shen, B.; Dunker, A.K. Identification of intrinsic disorder in complexes from the protein data bank. ACS Omega, 2020, 5(29), 17883-17891.
[http://dx.doi.org/10.1021/acsomega.9b03927] [PMID: 32743159]
[19]
Gautam, A.; Singh, H.; Tyagi, A.; Chaudhary, K.; Kumar, R.; Kapoor, P.; Raghava, G. P. CPPsite: A curated database of cell penetrating peptides. Database, 2012, 2012, bas015.
[http://dx.doi.org/10.1093/database/bas015]
[20]
Landrum, M.J.; Lee, J.M.; Riley, G.R.; Jang, W.; Rubinstein, W.S.; Church, D.M.; Maglott, D.R. ClinVar: Public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res., 2014, 42(D1), D980-D985.
[http://dx.doi.org/10.1093/nar/gkt1113] [PMID: 24234437]
[21]
Lappalainen, I.; Lopez, J.; Skipper, L.; Hefferon, T.; Spalding, J.D.; Garner, J.; Chen, C.; Maguire, M.; Corbett, M.; Zhou, G.; Paschall, J.; Ananiev, V.; Flicek, P.; Church, D.M. DbVar and DGVa: Public archives for genomic structural variation. Nucleic Acids Res., 2013, 41, D936-D941.
[PMID: 23193291]
[22]
Buchan, D.W.A.; Jones, D.T. The PSIPRED protein analysis workbench: 20 years on. Nucleic Acids Res., 2019, 47(W1), W402-W407.
[http://dx.doi.org/10.1093/nar/gkz297] [PMID: 31251384]
[23]
Ward, J.J.; McGuffin, L.J.; Buxton, B.F.; Jones, D.T. Secondary structure prediction with support vector machines. Bioinformatics, 2003, 19(13), 1650-1655.
[http://dx.doi.org/10.1093/bioinformatics/btg223] [PMID: 12967961]
[24]
Uversky, V.N.; Gillespie, J.R.; Fink, A.L. Why are "natively unfolded" proteins unstructured under physiologic conditions? Proteins, 2000, 41(3), 415-427.
[http://dx.doi.org/10.1002/1097-0134(20001115)41:3<415::AID-PROT130>3.0.CO;2-7] [PMID: 11025552]
[25]
Dunker, A.K.; Lawson, J.D.; Brown, C.J.; Williams, R.M.; Romero, P.; Oh, J.S.; Oldfield, C.J.; Campen, A.M.; Ratliff, C.M.; Hipps, K.W.; Ausio, J.; Nissen, M.S.; Reeves, R.; Kang, C.; Kissinger, C.R.; Bailey, R.W.; Griswold, M.D.; Chiu, W.; Garner, E.C.; Obradovic, Z. Intrinsically disordered protein. J. Mol. Graph. Model., 2001, 19(1), 26-59.
[http://dx.doi.org/10.1016/S1093-3263(00)00138-8] [PMID: 11381529]
[26]
Radivojac, P.; Iakoucheva, L.M.; Oldfield, C.J.; Obradovic, Z.; Uversky, V.N.; Dunker, A.K. Intrinsic disorder and functional proteomics. Biophys. J., 2007, 92(5), 1439-1456.
[http://dx.doi.org/10.1529/biophysj.106.094045] [PMID: 17158572]
[27]
Vacic, V.; Uversky, V.N.; Dunker, A.K.; Lonardi, S. Composition profiler: A tool for discovery and visualization of amino acid composition differences. BMC Bioinformatics, 2007, 8(1), 211.
[http://dx.doi.org/10.1186/1471-2105-8-211] [PMID: 17578581]
[28]
He, B.; Wang, K.; Liu, Y.; Xue, B.; Uversky, V.N.; Dunker, A.K. Predicting intrinsic disorder in proteins: An overview. Cell Res., 2009, 19(8), 929-949.
[http://dx.doi.org/10.1038/cr.2009.87] [PMID: 19597536]
[29]
Meng, F.; Uversky, V.N.; Kurgan, L. Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions. Cell. Mol. Life Sci., 2017, 74(17), 3069-3090.
[http://dx.doi.org/10.1007/s00018-017-2555-4] [PMID: 28589442]
[30]
Romero, P.; Obradovic, Z.; Li, X.; Garner, E.C.; Brown, C.J.; Dunker, A.K. Sequence complexity of disordered protein. Proteins, 2001, 42(1), 38-48.
[http://dx.doi.org/10.1002/1097-0134(20010101)42:1<38::AID-PROT50>3.0.CO;2-3] [PMID: 11093259]
[31]
Obradovic, Z.; Peng, K.; Vucetic, S.; Radivojac, P.; Dunker, A.K. Exploiting heterogeneous sequence properties improves prediction of protein disorder. Proteins, 2005, 61(Suppl. 7), 176-182.
[http://dx.doi.org/10.1002/prot.20735] [PMID: 16187360]
[32]
Peng, K.; Vucetic, S.; Radivojac, P.; Brown, C.J.; Dunker, A.K.; Obradovic, Z. Optimizing long intrinsic disorder predictors with protein evolutionary information. J. Bioinform. Comput. Biol., 2005, 3(1), 35-60.
[http://dx.doi.org/10.1142/S0219720005000886] [PMID: 15751111]
[33]
Aslam, M. Introducing kolmogorov–smirnov tests under uncertainty: An application to radioactive data. ACS Omega, 2020, 5(1), 914-917.
[http://dx.doi.org/10.1021/acsomega.9b03940] [PMID: 31956845]
[34]
Mitopencourseware. Available from: https://ocw.mit.edu/courses/mathematics/18-443-statistics-for-applications-fall-2006/lecture-notes/lecture14.pdf
[35]
Sherry, S.T.; Ward, M.H.; Kholodov, M.; Baker, J.; Phan, L.; Smigielski, E.M.; Sirotkin, K. dbSNP: The NCBI database of genetic variation. Nucleic Acids Res., 2001, 29(1), 308-311.
[http://dx.doi.org/10.1093/nar/29.1.308] [PMID: 11125122]
[36]
Wei, C.H.; Phan, L.; Feltz, J.; Maiti, R.; Hefferon, T.; Lu, Z. tmVar 2.0: Integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine. Bioinformatics, 2018, 34(1), 80-87.
[http://dx.doi.org/10.1093/bioinformatics/btx541] [PMID: 28968638]
[37]
XPhan, L.; Hsu, J.; Le Quang Minh Tri, M. W.; Mansour, T.; Kai, Y.; Garner, J.; Busby, B. dbVar structural variant cluster set for data analysis and variant comparison. F1000 Res., 2016, 5, 673.
[38]
Szklarczyk, D.; Franceschini, A.; Kuhn, M.; Simonovic, M.; Roth, A.; Minguez, P.; Doerks, T.; Stark, M.; Muller, J.; Bork, P.; Jensen, L.J.; Mering, C. The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res., 2011, 39, D561-D568.
[http://dx.doi.org/10.1093/nar/gkq973] [PMID: 21045058]
[39]
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873), 583-589.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[40]
Li, Z.; Jin, X.; Wu, T.; Zhao, X.; Wang, W.; Lei, J.; Pan, X.; Yan, N. Structure of human Na v 1.5 reveals the fast inactivation-related segments as a mutational hotspot for the long QT syndrome. Proc. Natl. Acad. Sci. USA, 2021, 118(11), e2100069118.
[http://dx.doi.org/10.1073/pnas.2100069118] [PMID: 33712541]
[41]
Tng, S.S.; Le, N.Q.K.; Yeh, H.Y.; Chua, M.C.H. Improved prediction model of protein lysine crotonylation sites using bidirectional recurrent neural networks. J. Proteome Res., 2022, 21(1), 265-273.
[http://dx.doi.org/10.1021/acs.jproteome.1c00848] [PMID: 34812044]
[42]
Chagot, B.; Chazin, W.J. Solution NMR structure of Apo- calmodulin in complex with the IQ motif of human cardiac sodium channel NaV1.5. J. Mol. Biol., 2011, 406(1), 106-119.
[http://dx.doi.org/10.1016/j.jmb.2010.11.046] [PMID: 21167176]
[43]
Gabelli, S.B.; Boto, A.; Kuhns, V.H.; Bianchet, M.A.; Farinelli, F.; Aripirala, S.; Yoder, J.; Jakoncic, J.; Tomaselli, G.F.; Amzel, L.M. Regulation of the NaV1.5 cytoplasmic domain by calmodulin. Nat. Commun., 2014, 5(1), 5126.
[http://dx.doi.org/10.1038/ncomms6126] [PMID: 25370050]
[44]
Le, N.Q.K. Potential of deep representative learning features to interpret the sequence information in proteomics. Proteomics, 2022, 22(1-2), 2100232.
[http://dx.doi.org/10.1002/pmic.202100232] [PMID: 34730875]
[45]
Le, N.Q.K.; Do, D.T.; Nguyen, T.T.D.; Le, Q.A. A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features. Gene, 2021, 787, 145643.
[http://dx.doi.org/10.1016/j.gene.2021.145643] [PMID: 33848577]
[46]
Ferreira, K.K.S.; de Morais Gomes, E.R.; de Lima Filho, J.L.; Castelletti, C.H.M.; Martins, D.B.G. Bioinformatics analysis of non-synonymous variants in the KLF genes related to cardiac diseases. Gene, 2018, 650, 68-76.
[http://dx.doi.org/10.1016/j.gene.2018.01.085] [PMID: 29408733]
[47]
Pedersen, J.T.; Moult, J. Genetic algorithms for protein structure prediction. Curr. Opin. Struct. Biol., 1996, 6(2), 227-231.
[http://dx.doi.org/10.1016/S0959-440X(96)80079-0] [PMID: 8728656]
[48]
Contreras-Moreira, B.; Fitzjohn, P.W.; Offman, M.; Smith, G.R.; Bates, P.A. Novel use of a genetic algorithm for protein structure prediction: Searching template and sequence alignment space. Proteins, 2003, 53(S6)(Suppl. 6), 424-429.
[http://dx.doi.org/10.1002/prot.10549] [PMID: 14579331]
[49]
Maire, F.; Friel, N.; Mira, A.; Raftery, A.E. Adaptive incremental mixture markov chain monte carlo. Journal of computational and graphical statistics: A joint publication of american statistical association, Institute of mathematical statistics. Interface Found. North Amer., 2019, 28(4), 790-805.

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