The influence of protein coding sequences on protein folding rates of all-β proteins

Background: Protein folding rate is mainly determined by the size of the conformational space to search, which in turn is dictated by factors such as size, structure and amino-acid sequence in a protein. It is important to integrate these factors effectively to form a more precisely description of conformation space. But there is no general paradigm to answer this question except some intuitions and empirical rules. Therefore, at the present stage, predictions of the folding rate can be improved through finding new factors, and some insights are given to the above question. Objective: Its purpose is to propose a new parameter that can describe the size of the conformational space to improve the prediction accuracy of protein folding rate. Method: Based on the optimal set of amino acids in a protein, an effective cumulative backbone torsion angles (CBTAeff) was proposed to describe the size of the conformational space. Linear regression model was used to predict protein folding rate with CBTAeff as a parameter. The degree of correlation was described by the coefficient of determination and the mean absolute error MAE between the predicted folding rates and experimental observations. Results: It achieved a high correlation (with the coefficient of determination of 0.70 and MAE of 1.88) between the logarithm of folding rates and the (CBTAeff)0.5 with experimental over 112 twoand multi-state folding proteins. Conclusion: The remarkable performance of our simplistic model demonstrates that CBTA based on optimal set was the major determinants of the conformation space of natural proteins.

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The Influences of Palindromes in mRNA on Protein Folding Rates

Protein and Peptide Letters ◽

10.2174/0929866526666191014144015 ◽

2020 ◽

Vol 27 (4) ◽

pp. 303-312 ◽

Cited By ~ 1

Author(s):

Ruifang Li ◽

Hong Li ◽

Sarula Yang ◽

Xue Feng

Keyword(s):

Protein Folding ◽

Regression Analysis ◽

Linear Regression ◽

Linear Regression Analysis ◽

Folding Rate ◽

Folding Rates ◽

Negative Linear Correlation ◽

Protein Folding Rate ◽

The Relationship ◽

Structure Environment

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. Method: 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.

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Draft Genome Sequence of Urease-Producing Pseudorhodobacter sp. Strain E13, Isolated from the Yellow Sea in Gunsan, South Korea

Microbiology Resource Announcements ◽

10.1128/mra.00189-19 ◽

2019 ◽

Vol 8 (23) ◽

Author(s):

Si Chul Kim ◽

Hyo Jung Lee

Keyword(s):

South Korea ◽

Genome Sequence ◽

Yellow Sea ◽

Draft Genome ◽

The Yellow Sea ◽

Draft Genome Sequence ◽

Protein Coding ◽

Coding Sequences ◽

Gram Negative ◽

Content Type

Here, we report the draft genome sequence of Pseudorhodobacter sp. strain E13, a Gram-negative, aerobic, nonflagellated, and rod-shaped bacterium which was isolated from the Yellow Sea in South Korea. The assembled genome sequence is 3,878,578 bp long with 3,646 protein-coding sequences in 159 contigs.

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Evolutionary Patterns of Sex-Biased Genes in Three Species of Haplodiploid Insects

Insects ◽

10.3390/insects11060326 ◽

2020 ◽

Vol 11 (6) ◽

pp. 326

Author(s):

Yu-Jun Wang ◽

Hua-Ling Wang ◽

Xiao-Wei Wang ◽

Shu-Sheng Liu

Keyword(s):

Gene Expression ◽

Slow Rate ◽

Species Complex ◽

Evolutionary Patterns ◽

Protein Coding ◽

Coding Sequences ◽

Species Comparisons ◽

Differential Gene ◽

Males And Females ◽

And Behavior

Females and males often differ obviously in morphology and behavior, and the differences between sexes are the result of natural selection and/or sexual selection. To a great extent, the differences between the two sexes are the result of differential gene expression. In haplodiploid insects, this phenomenon is obvious, since males develop from unfertilized zygotes and females develop from fertilized zygotes. Whiteflies of the Bemisia tabaci species complex are typical haplodiploid insects, and some species of this complex are important pests of many crops worldwide. Here, we report the transcriptome profiles of males and females in three species of this whitefly complex. Between-species comparisons revealed that non-sex-biased genes display higher variation than male-biased or female-biased genes. Sex-biased genes evolve at a slow rate in protein coding sequences and gene expression and have a pattern of evolution that differs from those of social haplodiploid insects and diploid animals. Genes with high evolutionary rates are more related to non-sex-biased traits—such as nutrition, immune system, and detoxification—than to sex-biased traits, indicating that the evolution of protein coding sequences and gene expression has been mainly driven by non-sex-biased traits.

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