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Protein & Peptide Letters

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ISSN (Print): 0929-8665
ISSN (Online): 1875-5305

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Research Article

The Influences of Palindromes in mRNA on Protein Folding Rates

Author(s): Ruifang Li*, Hong Li, Sarula Yang and Xue Feng

Volume 27, Issue 4, 2020

Page: [303 - 312] Pages: 10

DOI: 10.2174/0929866526666191014144015

Price: $65

Abstract

Background: It is currently believed that protein folding rates are influenced by protein structure, environment and temperature, amino acid sequence and so on. We have been working for long to determine whether and in what ways mRNA affects the protein folding rate. A large number of palindromes aroused our attention in our previous research. Whether these palindromes do have important influences on protein folding rates and what’s the mechanism? Very few related studies are focused on these problems.

Objective: In this article, our motivation is to find out if palindromes have important influences on protein folding rates and what’s the mechanism.

Methods: In this article, the parameters of the palindromes were defined and calculated, the linear regression analysis between the values of each parameter and the experimental protein folding rates were done. Furthermore, to compare the results of different kinds of proteins, proteins were classified into the two-state proteins and the multi-state proteins. For the two kinds of proteins, the above linear regression analysis were performed respectively.

Results: Protein folding rates were negatively correlated to the palindrome frequencies for all proteins. An extremely significant negative linear correlation appeared in the relationship between palindrome densities and protein folding rates. And the repeatedly used bases by different palindromes simultaneously have an important effect on the relationship between palindrome density and protein folding rate.

Conclusion: The palindromes have important influences on protein folding rates, and the repeatedly used bases in different palindromes simultaneously play a key role in influencing the protein folding rates.

Keywords: mRNA sequence, protein folding rate, Palindrome frequency, Palindrome density, multi state proteomics, linear regression analysis.

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[1]
Zhou, J.M. Protein misfolding and disease. Prog. Biochem. Biophys., 2000, 27, 579-584.
[2]
Baker, D. What has de novo protein design taught us about protein folding and biophysics? Protein Sci., 2019, 28(4), 678-683.
[http://dx.doi.org/10.1002/pro.3588] [PMID: 30746840]
[3]
Zhou, J.; Dunker, A.K. Regulating protein function by delayed folding. Structure, 2018, 26(5), 679-681.
[http://dx.doi.org/10.1016/j.str.2018.04.011] [PMID: 29719237]
[4]
Komar, A.A. Unraveling co-translational protein folding: Concepts and methods. Methods, 2018, 137, 71-81.
[http://dx.doi.org/10.1016/j.ymeth.2017.11.007] [PMID: 29221924]
[5]
Song, Y.S.; Zhou, X.; Zheng, W.M. Stabilities and dynamics of protein folding nuclei by molecular dynamics simulation. Commum. Theor. Phys., 2017, 68, 137-148.
[http://dx.doi.org/10.1088/0253-6102/68/1/137]
[6]
Hatters, D.M. Protein folding: Illuminating chaperone activity. Nat. Chem. Biol., 2017, 13(4), 346-347.
[http://dx.doi.org/10.1038/nchembio.2332] [PMID: 28328919]
[7]
Pang, Y.P. How fast fast-folding proteins fold in silico. Biochem. Biophys. Res. Commun., 2017, 492(1), 135-139.
[http://dx.doi.org/10.1016/j.bbrc.2017.08.010] [PMID: 28802577]
[8]
Ljubetič, A.; Gradišar, H.; Jerala, R. Advances in design of protein folds and assemblies. Curr. Opin. Chem. Biol., 2017, 40, 65-71.
[http://dx.doi.org/10.1016/j.cbpa.2017.06.020] [PMID: 28709120]
[9]
Dill, K.A.; Ozkan, S.B.; Weikl, T.R.; Chodera, J.D.; Voelz, V.A. The protein folding problem: When will it be solved? Curr. Opin. Struct. Biol., 2007, 17(3), 342-346.
[http://dx.doi.org/10.1016/j.sbi.2007.06.001] [PMID: 17572080]
[10]
Plaxco, K.W.; Simons, K.T.; Baker, D. Contact order, transition state placement and the refolding rates of single domain proteins. J. Mol. Biol., 1998, 277(4), 985-994.
[http://dx.doi.org/10.1006/jmbi.1998.1645] [PMID: 9545386]
[11]
Zhou, H.; Zhou, Y. Folding rate prediction using total contact distance. Biophys. J., 2002, 82(1 Pt 1), 458-463.
[http://dx.doi.org/10.1016/S0006-3495(02)75410-6] [PMID: 11751332]
[12]
Gong, H.; Isom, D.G.; Srinivasan, R.; Rose, G.D. Local secondary structure content predicts folding rates for simple, two-state proteins. J. Mol. Biol., 2003, 327(5), 1149-1154.
[http://dx.doi.org/10.1016/S0022-2836(03)00211-0] [PMID: 12662937]
[13]
Mirny, L.; Shakhnovich, E. Protein folding theory: From lattice to all-atom models. Annu. Rev. Biophys. Biomol. Struct., 2001, 30, 361-396.
[http://dx.doi.org/10.1146/annurev.biophys.30.1.361] [PMID: 11340064]
[14]
Ivankov, D.N.; Finkelstein, A.V. Prediction of protein folding rates from the amino acid sequence-predicted secondary structure. Proc. Natl. Acad. Sci. USA, 2004, 101(24), 8942-8944.
[http://dx.doi.org/10.1073/pnas.0402659101] [PMID: 15184682]
[15]
Gromiha, M.M. A statistical model for predicting protein folding rates from amino acid sequence with structural class information. J. Chem. Inf. Model., 2005, 45(2), 494-501.
[http://dx.doi.org/10.1021/ci049757q] [PMID: 15807515]
[16]
Kuznetsov, I.B.; Rackovsky, S. Class-specific correlations between protein folding rate, structure-derived, and sequence-derived descriptors. Proteins, 2004, 54(2), 333-341.
[http://dx.doi.org/10.1002/prot.10518] [PMID: 14696195]
[17]
Punta, M.; Rost, B. Protein folding rates estimated from contact predictions. J. Mol. Biol., 2005, 348(3), 507-512.
[http://dx.doi.org/10.1016/j.jmb.2005.02.068] [PMID: 15826649]
[18]
Ouyang, Z.; Liang, J. Predicting protein folding rates from geometric contact and amino acid sequence. Protein Sci., 2008, 17(7), 1256-1263.
[http://dx.doi.org/10.1110/ps.034660.108] [PMID: 18434498]
[19]
Chou, K.C.; Shen, H.B. FoldRate: A web-server for predicting protein folding rates from primary sequence. Open Biol. J., 2009, 3, 31-50.
[20]
Liang, H.; Wang, L.L.; Zhang, Y. Prediction of protein folding rates from the amino acid sequence predicted backbone torsion angles. Lett. Org. Chem., 2017, 14, 648-654.
[http://dx.doi.org/10.2174/1570178614666170608130848]
[21]
Komar, A.A. A pause for thought along the co-translational folding pathway. Trends Biochem. Sci., 2009, 34(1), 16-24.
[http://dx.doi.org/10.1016/j.tibs.2008.10.002] [PMID: 18996013]
[22]
Cai, L.K.; Yin, J.; Chao, H.M.A. C3HC4-type RING finger protein regulates rhizobial infection and nodule organogenesis in Lotus japonicas. J. Integr. Plant Biol., 2018, 60, 140-158.
[http://dx.doi.org/10.1111/jipb.12703]
[23]
Yu, Z.; Chen, Q.; Chen, W.; Zhang, X.; Mei, F.; Zhang, P.; Zhao, M.; Wang, X.; Shi, N.; Jackson, S.; Hong, Y. Multigene editing via CRISPR/Cas9 guided by a single-sgRNA seed in Arabidopsis. J. Integr. Plant Biol., 2018, 60(5), 376-381.
[http://dx.doi.org/10.1111/jipb.12622] [PMID: 29226588]
[24]
Nag, D.K.; Kurst, A.A. 140-bp-long palindromic sequence induces double-strand breaks during meiosis in the yeast Saccharomyces cerevisiae. Genetics, 1997, 146(3), 835-847.
[PMID: 9215890]
[25]
Li, R.F.; Li, H. The influence of protein coding sequences on protein folding rates of all-β proteins. Gen. Physiol. Biophys., 2011, 30(2), 154-161.
[http://dx.doi.org/10.4149/gpb_2011_02_154] [PMID: 21613670]
[26]
Li, R.F.; Li, H. Study on the influences of palindromes in protein coding sequences on the folding rates of peptide chains. Protein Pept. Lett., 2010, 17(7), 881-888.
[http://dx.doi.org/10.2174/092986610791306652] [PMID: 20205658]
[27]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein databank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[28]
Baker, W.; Van Den Broek, A.; Camon, E.; Hingamp, P.; Sterk, P.; Stoesser, G.; Tuli, M.A. The EMBL nucleotide sequence database. Nucleic Acids Res., 2000, 28(1), 19-23.
[http://dx.doi.org/10.1093%2Fnar%2F28.1.19] [PMID: 10592171]
[29]
Purvis, I.J.; Bettany, A.J.; Santiago, T.C.; Coggins, J.R.; Duncan, K.; Eason, R.; Brown, A.J. The efficiency of folding of some proteins is increased by controlled rates of translation in vivo. A hypothesis. J. Mol. Biol., 1987, 193(2), 413-417.
[http://dx.doi.org/10.1016/0022-2836(87)90230-0] [PMID: 3298659]
[30]
Zhu, X.J.; Feng, C.Q.; Lai, H.Y. Predicting protein structural classes for low-similarity sequences by evaluating different features. Knowl. Base. Syst., 2019, 163, 787-793.
[http://dx.doi.org/10.1016/j.knosys.2018.10.007]
[31]
Feng, C.Q.; Zhang, Z.Y.; Zhu, X.J. iTerm-PseKNC: A sequence-based tool for predicting bacterial transcriptional terminators. Bioinformatics, 2018, 35(9), 1469-1477.
[http://dx.doi.org/10.1093/bioinformatics/bty827] [PMID: 30247625]
[32]
Dao, F.Y.; Lv, H.; Wang, F.; Feng, C.Q.; Ding, H.; Chen, W.; Lin, H. Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique. Bioinformatics, 2018, 35(12), 2075-2083.
[http://dx.doi.org/10.1093/bioinformatics/bty943] [PMID: 30428009]
[33]
Yang, H.; Lv, H.; Ding, H.; Chen, W.; Lin, H. iRNA-2OM: A sequence-based predictor for identifying 2′-O-methylation sites in Homo sapiens. J. Comput. Biol., 2018, 25(11), 1266-1277.
[http://dx.doi.org/10.1089/cmb.2018.0004] [PMID: 30113871]
[34]
Chen, W.; Lv, H.; Nie, F.; Lin, H. i6mA-Pred: Identifying DNA N6-methyladenine sites in the rice genome. Bioinformatics, 2019, 35(16), 2796-2800.
[http://dx.doi.org/10.1093/bioinformatics/btz015] [PMID: 30624619]
[35]
Lv, H.; Zhang, Z.M.; Li, S.H. Evaluation of different computational methods on 5-methylcytosine sites identification; Brief. Bioinfor, 2019. [Epub ahead of print]
[http://dx.doi.org/10.1093/bib/bbz048] [PMID: 31157855]
[36]
Liu, D.Y.; Li, G.P.; Zuo, Y.C. Function determinants of TET proteins: the arrangements of sequence motifs with specific codes. Brief. Bioinform., 2019, 20(5), 1826-1835.
[http://dx.doi.org/10.1093/bib/bby053] [PMID: 29947743]
[37]
Zuo, Y.; Li, Y.; Chen, Y.; Li, G.; Yan, Z.; Yang, L. PseKRAAC: A flexible web server for generating pseudo K-tuple reduced amino acids composition. Bioinformatics, 2017, 33(1), 122-124.
[http://dx.doi.org/10.1093/bioinformatics/btw564] [PMID: 27565583]
[38]
Long, C.S.; Li, W.; Liang, P.F. Transcriptome comparisons of multi-species identify differential genome activation of mammals embryogenesis. IEEE Access, 2019, 7, 7794-7802.
[http://dx.doi.org/10.1109/ACCESS.2018.2889809]

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