Direct correlation between proteins' folding rates and their amino acid compositions: An ab initio folding rate prediction

2006 ◽  
Vol 65 (2) ◽  
pp. 362-372 ◽  
Author(s):  
Bin-Guang Ma ◽  
Jian-Xiu Guo ◽  
Hong-Yu Zhang
Keyword(s):  
Amino Acid ◽  
Ab Initio ◽  
Folding Rate ◽  
Amino Acid Compositions ◽  
Folding Rates ◽  
Rate Prediction

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

2021 ◽  
Vol 12 ◽  
Author(s):  
Ruifang Li ◽  
Hong Li ◽  
Xue Feng ◽  
Ruifeng Zhao ◽  
Yongxia Cheng
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Protein Structures ◽  
Gc Content ◽  
Amino Acid Sequences ◽  
Information Redundancy ◽  
Folding Rate ◽  
Folding Rates ◽  
Related Information ◽  
Linear Regressions

Many works have reported that protein folding rates are influenced by the characteristics of amino acid sequences and protein structures. However, few reports on the problem of whether the corresponding mRNA sequences are related to the protein folding rates can be found. An mRNA sequence is regarded as a kind of genetic language, and its vocabulary and phraseology must provide influential information regarding the protein folding rate. In the present work, linear regressions on the parameters of the vocabulary and phraseology of mRNA sequences and the corresponding protein folding rates were analyzed. The results indicated that D2 (the adjacent base-related information redundancy) values and the GC content values of the corresponding mRNA sequences exhibit significant negative relations with the protein folding rates, but D1 (the single base information redundancy) values exhibit significant positive relations with the protein folding rates. In addition, the results show that the relationships between the parameters of the genetic language and the corresponding protein folding rates are obviously different for different protein groups. Some useful parameters that are related to protein folding rates were found. The results indicate that when predicting protein folding rates, the information from protein structures and their amino acid sequences is insufficient, and some information for regulating the protein folding rates must be derived from the mRNA sequences.

scholarly journals FOLD-RATE: prediction of protein folding rates from amino acid sequence

2006 ◽  
Vol 34 (Web Server) ◽  
pp. W70-W74 ◽  
Author(s):  
M. M. Gromiha ◽  
A. M. Thangakani ◽  
S. Selvaraj
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Folding Rates ◽  
Rate Prediction

PREDICTING PROTEIN FOLDING RATE FROM AMINO ACID SEQUENCE

2011 ◽  
Vol 09 (01) ◽  
pp. 1-13 ◽  
Author(s):  
JIANXIU GUO ◽  
NINI RAO
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Structural Information ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Folding Rate ◽  
Jackknife Test ◽  
Folding Rates ◽  
Protein Folding Rate

Predicting protein folding rate from amino acid sequence is an important challenge in computational and molecular biology. Over the past few years, many methods have been developed to reflect the correlation between the folding rates and protein structures and sequences. In this paper, we present an effective method, a combined neural network — genetic algorithm approach, to predict protein folding rates only from amino acid sequences, without any explicit structural information. The originality of this paper is that, for the first time, it tackles the effect of sequence order. The proposed method provides a good correlation between the predicted and experimental folding rates. The correlation coefficient is 0.80 and the standard error is 2.65 for 93 proteins, the largest such databases of proteins yet studied, when evaluated with leave-one-out jackknife test. The comparative results demonstrate that this correlation is better than most of other methods, and suggest the important contribution of sequence order information to the determination of protein folding rates.

Amino Acid Compositions of Common Edible Mushrooms

1995 ◽  
Vol 6 (3) ◽  
pp. 34-37
Author(s):  
Shinobu Fujihara ◽  
Atsuko Kasuga ◽  
Tatsuyuki Sugahara ◽  
Yasuo Aoyagi
Keyword(s):  
Amino Acid ◽  
Edible Mushrooms ◽  
Amino Acid Compositions

Predicting Protein Folding Rate From Amino Acid Sequence

2011 ◽  
Vol 37 (12) ◽  
pp. 1331-1338 ◽  
Author(s):  
Jian-Xiu GUO ◽  
Ni-Ni RAO ◽  
Guang-Xiong LIU ◽  
Jie LI ◽  
Yun-He WANG
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Folding Rate ◽  
Protein Folding Rate

An Effective Cumulative Torsion Angles Model for Prediction of Protein Folding Rates

2020 ◽  
Vol 27 (4) ◽  
pp. 321-328 ◽  
Author(s):  
Yanru Li ◽  
Ying Zhang ◽  
Jun Lv
Keyword(s):  
Protein Folding ◽  
Size Structure ◽  
Conformational Space ◽  
Coefficient Of Determination ◽  
Folding Rate ◽  
Torsion Angles ◽  
Folding Rates ◽  
Protein Folding Rate ◽  
Optimal Set ◽  
Conformation Space

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.

The Influences of Palindromes in mRNA on Protein Folding Rates

2020 ◽  
Vol 27 (4) ◽  
pp. 303-312 ◽  
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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