The Influences of Palindromes in mRNA on Protein Folding Rates

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

Journal Name: Protein & Peptide Letters

Volume 27 , Issue 4 , 2020

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Graphical 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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Year: 2020
Page: [303 - 312]
Pages: 10
DOI: 10.2174/0929866526666191014144015
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