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

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.

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PREDICTING PROTEIN FOLDING RATE FROM AMINO ACID SEQUENCE

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720011005306 ◽

2011 ◽

Vol 09 (01) ◽

pp. 1-13 ◽

Cited By ~ 8

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.

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The Influence of Dipeptide Composition on Protein Folding Rates

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.378-379.157 ◽

2011 ◽

Vol 378-379 ◽

pp. 157-160

Author(s):

Jian Xiu Guo ◽

Ni Ni Rao

Keyword(s):

Protein Folding ◽

Amino Acid ◽

Amino Acid Sequences ◽

Dipeptide Composition ◽

Coupling Effects ◽

Jackknife Test ◽

Folding Rates ◽

Important Challenge ◽

The Relationship

Understanding the relationship between amino acid sequences and folding rates of proteins is an important challenge in computational and molecular biology. All existing algorithms for predicting protein folding rates have never taken into account the sequence coupling effects. In this work, a novel algorithm was developed for predicting the protein folding rates from amino acid sequences. The prediction was achieved on the basis of dipeptide composition, in which the sequence coupling effects are explicitly included through a series of conditional probability elements. Based on a non-redundant dataset of 99 proteins, the proposed method was found to provide an excellent agreement between the predicted and experimental folding rates of proteins when evaluated with the jackknife test. The correlation coefficient was 87.7% and the standard error was 2.04, which indicated the important contribution from sequence coupling effects to the determination of protein folding rates.

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Experimental Evidence for Information Redundancy in Amino Acid Sequences: Implication for Protein Folding and Evolution.

Seibutsu Butsuri ◽

10.2142/biophys.41.224 ◽

2001 ◽

Vol 41 (5) ◽

pp. 224-229

Author(s):

Satoshi AKANUMA ◽

Yutaka KURODA

Keyword(s):

Protein Folding ◽

Amino Acid ◽

Experimental Evidence ◽

Amino Acid Sequences ◽

Information Redundancy

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Predicting Protein Folding Rate From Amino Acid Sequence

PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS ◽

10.3724/sp.j.1206.2010.00380 ◽

2011 ◽

Vol 37 (12) ◽

pp. 1331-1338 ◽

Cited By ~ 3

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

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

Protein and Peptide Letters ◽

10.2174/0929866526666191014152207 ◽

2020 ◽

Vol 27 (4) ◽

pp. 321-328 ◽

Cited By ~ 2

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.

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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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Predicting secondary structures, contact numbers, and residue-wise contact orders of native protein structures from amino acid sequences using critical random networks

BIOPHYSICS ◽

10.2142/biophysics.1.67 ◽

2005 ◽

Vol 1 ◽

pp. 67-74 ◽

Cited By ~ 14

Author(s):

Akira R. Kinjo ◽

Ken Nishikawa

Keyword(s):

Amino Acid ◽

Protein Structures ◽

Secondary Structures ◽

Amino Acid Sequences ◽

Random Networks ◽

Native Protein

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In-silicoprediction and modeling of theEntamoeba histolyticaproteins: Serine-richEntamoeba histolyticaprotein and 29 kDa Cysteine-rich protease

PeerJ ◽

10.7717/peerj.3160 ◽

2017 ◽

Vol 5 ◽

pp. e3160 ◽

Cited By ~ 5

Author(s):

Kumar Manochitra ◽

Subhash Chandra Parija

Keyword(s):

Amino Acid ◽

Structure Prediction ◽

Tertiary Structure ◽

Protein Structures ◽

Amino Acid Sequences ◽

Treatment Modalities ◽

Bioinformatic Tools ◽

Complex Protein ◽

A Cell ◽

Quaternary Structures

BackgroundAmoebiasis is the third most common parasitic cause of morbidity and mortality, particularly in countries with poor hygienic settings. There exists an ambiguity in the diagnosis of amoebiasis, and hence there arises a necessity for a better diagnostic approach. Serine-richEntamoeba histolyticaprotein (SREHP), peroxiredoxin and Gal/GalNAc lectin are pivotal inE. histolyticavirulence and are extensively studied as diagnostic and vaccine targets. For elucidating the cellular function of these proteins, details regarding their respective quaternary structures are essential. However, studies in this aspect are scant. Hence, this study was carried out to predict the structure of these target proteins and characterize them structurally as well as functionally using appropriatein-silicomethods.MethodsThe amino acid sequences of the proteins were retrieved from National Centre for Biotechnology Information database and aligned using ClustalW. Bioinformatic tools were employed in the secondary structure and tertiary structure prediction. The predicted structure was validated, and final refinement was carried out.ResultsThe protein structures predicted by i-TASSER were found to be more accurate than Phyre2 based on the validation using SAVES server. The prediction suggests SREHP to be an extracellular protein, peroxiredoxin a peripheral membrane protein while Gal/GalNAc lectin was found to be a cell-wall protein. Signal peptides were found in the amino-acid sequences of SREHP and Gal/GalNAc lectin, whereas they were not present in the peroxiredoxin sequence. Gal/GalNAc lectin showed better antigenicity than the other two proteins studied. All the three proteins exhibited similarity in their structures and were mostly composed of loops.DiscussionThe structures of SREHP and peroxiredoxin were predicted successfully, while the structure of Gal/GalNAc lectin could not be predicted as it was a complex protein composed of sub-units. Also, this protein showed less similarity with the available structural homologs. The quaternary structures of SREHP and peroxiredoxin predicted from this study would provide better structural and functional insights into these proteins and may aid in development of newer diagnostic assays or enhancement of the available treatment modalities.

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