Study on the Influence of mRNA, the Genetic Language, on Protein Folding Rates

Ruifang Li1, Hong Li2, Xue Feng1

  • 1College of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot, China.

Frontiers in Genetics
|April 23, 2021
PubMed

Insights

Messenger RNA (mRNA) sequences influence protein folding rates. Adjacent base information redundancy and GC content negatively correlate with folding rates, while single base information redundancy positively correlates.

Area of Science:

  • Biochemistry
  • Molecular Biology
  • Bioinformatics

Background:

  • Protein folding rates are known to be influenced by amino acid sequences and protein structures.
  • The relationship between messenger RNA (mRNA) sequences and protein folding rates remains largely unexplored.

Purpose of the Study:

  • To investigate the correlation between mRNA sequence characteristics (genetic language parameters) and protein folding rates.
  • To determine if mRNA sequence information is crucial for predicting protein folding rates.

Main Methods:

  • Linear regression analysis was employed to examine the relationship between mRNA sequence parameters and protein folding rates.
  • Key mRNA parameters analyzed included adjacent base-related information redundancy (D2), single base information redundancy (D), and GC content.

Main Results:

  • Significant negative correlations were found between D2 values and GC content of mRNA sequences with protein folding rates.
  • A significant positive correlation was observed between D values of mRNA sequences and protein folding rates.
  • The identified relationships varied across different protein groups, suggesting protein-specific regulatory mechanisms.

Conclusions:

  • mRNA sequence features, beyond protein structure and amino acid sequence, provide essential information for understanding and predicting protein folding rates.
  • Genetic language parameters derived from mRNA sequences are significant regulators of protein folding dynamics.
  • Incorporating mRNA sequence information can improve the accuracy of protein folding rate predictions.

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