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编码疾病相关的固有无序蛋白同义密码子使用偏好性的研究
Synonymous Codon Usage Bias in Nucleic Acids Encoding Disease-Associated Intrinsically Disordered Proteins

DOI: 10.12677/HJBM.2023.134043, PP. 368-377

Keywords: 固有无序蛋白,同义密码子,密码子相对使用度,GC含量,方差分析
Intrinsically Disordered Proteins
, Synonymous Codons, GC Content, Relative Synonymous Codon Usage, Analysis of Variance

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Abstract:

固有无序蛋白(简称IDPs)在生理条件下不具有稳定的空间结构,但是在生物体内发挥重要的生物学功能,除此之外,尤为重要的是它们与人类许多重大疾病密切相关。因此,研究疾病相关的IDPs,可以进一步了解IDPs与一系列疾病的病理学上的关系,这为蛋白质的药物设计和疾病治疗提供新思路。研究表明:密码子的使用偏好与固有无序蛋白的无序程度存在一定的关联性。本文研究了编码疾病相关的固有无序蛋白(IDPs)中同义密码子和GC含量的使用偏好,以及编码这些蛋白质的有序/无序区域的密码子中四个核苷酸在密码子的三个位点的分布差异。结果表明,这些蛋白中同义密码子的使用、GC含量均存在显著差异,另外四个核苷酸在其密码子三个位点的分布在编码3类疾病相关的IDPs有序和无序区中均存在偏好性。这些结果为后期研究IDPs提供了很好的参考信息。
Despite the fact that intrinsically disordered proteins (IDPs) lack stable spatial structures under physiological conditions, they perform critical biological functions in organisms. Moreover, intrinsically disordered proteins (IDPs) are associated with many major human diseases. Therefore, the systematic study of disease-associated intrinsically disordered proteins (IDPs) can further understand the pathological relationship between intrinsically disordered proteins (IDPs) and a series of diseases, providing a new idea for protein drug design and disease treatment. Some studies have shown a specific correlation between codon usage bias and intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs). In this paper, we analyzed the use bias of synonymous codons and GC content in nucleic acids encoding the disease-associated intrinsically disordered proteins (IDPs). Moreover, we studied the distribution of four nucleotides at three sites of the codon in encoding the ordered/disordered regions of these proteins. The results showed that the use of synonymous codons, the content of GC, and the distribution of four nucleotides at three sites of the codon are biased in the encoding of ordered regions and disordered regions of disease-associated intrinsically disordered proteins (IDPs). The results can provide a reference for further study of intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs).

References

[1]  Bo, H., et al. (2009) Predicting Intrinsic Disorder in Proteins: An Overview. Cell Research, 19, 929-949.
https://doi.org/10.1038/cr.2009.87
[2]  Munsky, B., Neuert, G. and van Oudenaarden, A. (2012) Using Gene Expression Noise to Understand Gene Regulation. Science, 336, 183-187.
https://doi.org/10.1126/science.1216379
[3]  Csizmok, V., Follis, A.V., Kriwacki, R.W. and Forman-Kay, J.D. (2016) Dynamic Protein Interaction Networks and New Structural Paradigms in Signaling. Chemical Reviews, 116, 6424-6462.
https://doi.org/10.1021/acs.chemrev.5b00548
[4]  Wright, P.E. and Dyson, H.J. (2015) Intrinsically Disordered Proteins in Cellular Signaling and Regulation. Nature Reviews Molecular Cell Biology, 16, 18-29.
https://doi.org/10.1038/nrm3920
[5]  Binolfi, A., Limatola, A., Verzini, S., Kosten, J., Theillet, F.X., Rose, H.M., Bekei, B., Stuiver, M., Rossum, M.V. and Selenko, P. (2016) Intracellular Repair of Oxidation-Damaged α-Synuclein Fails to Target C-Terminal Modification Sites. Nature Communications, 7, Article No. 10251.
https://doi.org/10.1038/ncomms10251
[6]  Fung, H.Y.J., Birol, M. and Rhoades, E. (2018) IDPs in Macromolecular Complexes: The Roles of Multivalent Interactions in Diverse Assemblie. Current Opinion in Structural Biology, 49, 36-43.
https://doi.org/10.1016/j.sbi.2017.12.007
[7]  Babu, M.M. (2016) The Contribution of Intrinsically Disordered Regions to Protein Function, Cellular Complexity, and Human Disease. Biochemical Society Transactions, 44, 1185-1200.
https://doi.org/10.1042/BST20160172
[8]  Babu, M.M., van der Lee, R. and de Groot, N.S. (2011) Intrinsically Disordered Proteins: Regulation and Disease. Current Opinion in Structural Biology, 21, 432-440.
https://doi.org/10.1016/j.sbi.2011.03.011
[9]  Kastenhuber, E.R. and Lowe, S.W. (2017) Putting p53 in Context. Cell, 170, 1062-1078.
https://doi.org/10.1016/j.cell.2017.08.028
[10]  Bykov, V.J., Eriksson, S.E. and Bianchi, J. (2018) Targeting Mutant p53 for Efficient Cancer Therapy. Nature Reviews Cancer, 18, 89-102.
https://doi.org/10.1038/nrc.2017.109
[11]  Romero, P., Obradovic, Z., Li, X., Garner, E.C., Brown, C.J. and Dunker, A.K. (2001) Sequence Complexity of Disordered Protein. Proteins: Structure, Function, and Bioinformatics, 42, 38-48.
https://doi.org/10.1002/1097-0134(20010101)42:1<38::AID-PROT50>3.0.CO;2-3
[12]  Oldfield, C.J., Peng, Z., Uversky, V.N. and Kurgan, L. (2020) Codon Selection Reduces GC Content Bias in Nucleic Acids Encoding for Intrinsically Disordered Proteins. Cellular and Molecular Life Sciences, 77, 149-160.
https://doi.org/10.1007/s00018-019-03166-6
[13]  Zhou, M., Wang, T., Fu, J., Xiao, G. and Liu, Y. (2015) Nonoptimal Codon Usage Influences Protein Structure in Intrinsically Disordered Regions. Molecular Microbiology, 97, 974-987.
https://doi.org/10.1111/mmi.13079
[14]  Homma, K., Noguchi, T. and Fukuchi, S. (2016) Codon Usage Is Less Optimized in Eukaryotic Gene Segments Encoding Intrinsically Disordered Regions than in Those Encoding Structural Domains. Nucleic Acids Research, 44, 10051-10061.
https://doi.org/10.1093/nar/gkw899
[15]  Peng, Z., Uversky, V.N. and Kurgan, L. (2016) Genes Encoding Intrinsic Disorder in Eukaryota Have High GC Content. Intrinsically Disordered Proteins, 4, e1262225.
https://doi.org/10.1080/21690707.2016.1262225
[16]  Basile, W., Sachenkova, O., Light, S. and Elofsson, A. (2017) High GC Content Causes Orphan Proteins to Be Intrinsically Disordered. PLOS Computational Biology, 13, e1005375.
https://doi.org/10.1371/journal.pcbi.1005375
[17]  Bateman, A., Martin, M.J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E.H., Britto, R. and Bursteinas, B. (2021) UniProt: The Universal Protein Knowledgebase in 2021. Nucleic Acids Research, 49, D480-D489.
[18]  Hatos, A., Hajdu-Soltész, B., Monzon, A.M., Palopoli, N., álvarez, L., Aykac-Fas, B., Bassot, C., et al. (2019) DisProt: Intrinsic Protein Disorder Annotation in 2020. Nucleic Acids Research, 48, D269-D276.
https://doi.org/10.1093/nar/gkz975
[19]  Piovesan, D., Tabaro, F., Paladin, L., Necci, M., Micetic, I., Camilloni, C., Davey, N., Dosztányi, Z., Mészáros, B., Monzon, A.M., Parisi, G., Schad, E., Sormanni, P., Tompa, P., Vendruscolo, M., Vranken, W.F. and Tosatto, S.C.E. (2018) MobiDB 3.0: More Annotations for Intrinsic Disorder, Conformational Diversity and Interactions in Proteins. Nucleic Acids Research, 46, D471-D476.
https://doi.org/10.1093/nar/gkx1071
[20]  Madeira, F., Park, Y.M., Lee, J., et al. (2019) The EMBL-EBI Search and Sequence Analysis Tools APIs in 2019. Nucleic Acids Research, 47, W636-W641.
https://doi.org/10.1093/nar/gkz268
[21]  Oldfeld, C.J., Peng, Z.L., Uversky, V.N. and Kurgan, L. (2020) Codon Selection Reduces GC Content Bias in Nucleic Acids Encoding for Intrinsically Disordered Proteins. Cellular and Molecular Life Sciences, 77, 149-160.
https://doi.org/10.1007/s00018-019-03166-6
[22]  Panda, A., Podder, S., Chakraborty, S. and Ghosh, T.C. (2014) GC-Made Protein Disorder Sheds New Light on Vertebrate Evolution. Genomics, 104, 530-537.
[23]  Bi, K., Lu, Z.H., Ge, Q.Y. and Gu, W.J. (2022) Extended XOR Algorithm with Biotechnology Constraints for Data Security in DNA Storage. Current Bioinformatics, 17, 401-410.
https://doi.org/10.2174/1574893617666220314114732
[24]  Sauvat, A., et al. (2021) High-Throughput Label-Free Detection of DNA-to-RNA Transcription Inhibition Using Brightfield Microscopy and Deep Neural Networks. Computers in Biology and Medicine, 133, Article ID: 104371.
https://doi.org/10.1016/j.compbiomed.2021.104371
[25]  Kou, G.S. and Feng, Y.E. (2015) Identify Five Kinds of Simple Super Secondary Structures with Quadratic Discriminant Algorithm Based on the Chemical Shifts. Journal of Theoretical Biology, 380, 392-398.
https://doi.org/10.1016/j.jtbi.2015.06.006
[26]  Li, J., et al. (2021) Comprehensive Analysis Reveals GPRIN1 Is a Potential Biomarker for Non-Small Cell Lung Cancer. Current Bioinformatics, 16, 130-138.
https://doi.org/10.2174/1574893615999200530201333
[27]  Prakosa, A., Southworth, M.K., Avari Silva, J.N., Silva, J.R. and Trayanova, N.A. (2021) Impact of Augmented-Real- ity Improvement in Ablation Catheter Navigation as Assessed by Virtual-Heart Simulations of Ventricular Tachycardia Ablation. Computers in Biology and Medicine, 133, Article ID: 104366.
https://doi.org/10.1016/j.compbiomed.2021.104366
[28]  Tang, H., Zhao, Y.W., Zou, P., Zhang, C.M., Chen, R., Huang, P. and Lin, H. (2018) HBPred: A Tool to Identify Growth Hormone-Binding Proteins. International Journal of Biological Sciences, 14, 957-964.
https://doi.org/10.7150/ijbs.24174
[29]  Dao, F.Y., Lv, H., Fullwood, M.J. and Lin, H. (2022) Accurate Identification of DNA Replication Origin by Fusing Epigenomics and Chromatin Interaction Information. Research, 2022, Article ID: 9780293.
https://doi.org/10.34133/2022/9780293
[30]  Sharp, P.M. and Li, W. (1986) An Evolutionary Perspective on Synonymous Codon Usage in Unicellular Organisms. Journal of Molecular Evolution, 24, 28-38.
https://doi.org/10.1007/BF02099948

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