scholarly journals Non-native Interactions Explain the Folding Rate Differences in α-Spectrin Domains and the Origin of Internal Friction Effects

2017 ◽  
Author(s):  
Fernando Bruno da Silva ◽  
Vinícius G. Contessoto ◽  
Vinícius M. de Oliveira ◽  
Jane Clarke ◽  
Vitor B. P. Leite
Keyword(s):  
Experimental Data ◽  
Protein Folding ◽  
Internal Friction ◽  
Computational Studies ◽  
Folding Rate ◽  
Folding Rates ◽  
Cooperativity Effect ◽  
Folding Dynamics ◽  
Molecular Origin ◽  
Dynamics Simulations

AbstractRecent experimental and computational studies have shown the influence of internal friction in protein folding dynamics. However, uncertainty remains over its molecular origin. α-spectrin experimental results indicate that R15 domain folds three orders of magnitude faster than its homologous R16 and R17. Such anomalous observations are usually attributed to the influence of internal friction on protein folding rates. To study this phenomenon, we carried out molecular dynamics simulations with structure-based Cα models, in which the folding process of α-spectrin domains was investigated by adding non-native interactions. The simulations take into account the hydrophobic and the electrostatic contributions separately. The folding time results have shown a qualitative agreement with experimental data. We have also investigated mutations in R16 and R17, and the simulation folding time results correlate with the observed experimental ones. We suggest that the origin of the internal friction emerges from a cooperativity effect of these non-native interactions.

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.

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.

Capturing protein folding-relevant topology via absolute contact order variants

2014 ◽  
Vol 13 (01) ◽  
pp. 1450005 ◽  
Author(s):  
Amy S. Wagaman ◽  
Sheila S. Jaswal
Keyword(s):  
Protein Folding ◽  
Heavy Atom ◽  
Folding Rate ◽  
Data Set ◽  
Structural Class ◽  
Protein Structural Class ◽  
Folding Rates ◽  
Contact Order ◽  
Energetic Interactions ◽  
Structural Classes

Absolute contact order is one of the simplest parameters used to predict protein folding rates. Many variants of contact order (CO) have been applied to highlight different aspects of contact neighborhoods and their relationship to folding. However, a systematic study of the influence of CO variants on correlation with folding rate has not been performed for a large combined set of multi- and two-state proteins. We explore different contact neighborhoods and resulting CO by varying the distance thresholds and weighting of sequence separation for heavy atom and residue-based counting methods for a set of 136 proteins diverse across folding and structural classes. We examine the changes in contact neighborhoods and compare correlations with our CO variants and the protein folding rates across our data set as well as by folding type and structural class. Different CO variants lead to the strongest correlations within each protein structural class. Our results demonstrate that backbone topology at a distance beyond where energetic interactions dominate is able to capture folding determinants, and suggest that more sensitive methods of characterizing contact relationships may improve ln kf prediction for diverse protein sets.

scholarly journals Assessing the effect of dynamics on the closed-loop protein-folding hypothesis

2014 ◽  
Vol 11 (91) ◽  
pp. 20130935 ◽  
Author(s):  
Sree V. Chintapalli ◽  
Christopher J. R. Illingworth ◽  
Graham J. G. Upton ◽  
Sophie Sacquin-Mora ◽  
Philip J. Reeves ◽  
...  
Keyword(s):  
Protein Folding ◽  
Closed Loop ◽  
Coarse Grain ◽  
Folding Rate ◽  
Elastic Network ◽  
Contact Order ◽  
Equivalent Result ◽  
Contact Distance ◽  
Network Simulations ◽  
Dynamics Simulations

The closed-loop (loop-n-lock) hypothesis of protein folding suggests that loops of about 25 residues, closed through interactions between the loop ends (locks), play an important role in protein structure. Coarse-grain elastic network simulations, and examination of loop lengths in a diverse set of proteins, each supports a bias towards loops of close to 25 residues in length between residues of high stability. Previous studies have established a correlation between total contact distance (TCD), a metric of sequence distances between contacting residues (cf. contact order), and the log-folding rate of a protein. In a set of 43 proteins, we identify an improved correlation ( r 2 = 0.76), when the metric is restricted to residues contacting the locks, compared to the equivalent result when all residues are considered ( r 2 = 0.65). This provides qualified support for the hypothesis, albeit with an increased emphasis upon the importance of a much larger set of residues surrounding the locks. Evidence of a similar-sized protein core/extended nucleus (with significant overlap) was obtained from TCD calculations in which residues were successively eliminated according to their hydrophobicity and connectivity, and from molecular dynamics simulations. Our results suggest that while folding is determined by a subset of residues that can be predicted by application of the closed-loop hypothesis, the original hypothesis is too simplistic; efficient protein folding is dependent on a considerably larger subset of residues than those involved in lock formation.

A novel model-based on FCM–LM algorithm for prediction of protein folding rate

2017 ◽  
Vol 15 (04) ◽  
pp. 1750012 ◽  
Author(s):  
Longlong Liu ◽  
Mingjiao Ma ◽  
Jing Cui
Keyword(s):  
Neural Networks ◽  
Protein Folding ◽  
Protein Sequence ◽  
Deep Neural Networks ◽  
Enzyme Engineering ◽  
Folding Rate ◽  
Folding Rates ◽  
Model Based ◽  
Protein Folding Rate ◽  
Lm Algorithm

The prediction of protein folding rates is of paramount importance in describing the protein folding mechanism, which has broad applications in fields such as enzyme engineering and protein engineering. Therefore, predicting protein folding rates using the first-order of protein sequence, secondary structure and amino acid properties has become a very active research topic in recent years. This paper presents a new fuzzy cognitive map (FCM) model based on deep learning neural networks which uses data obtained from biological experiments to predict the protein folding rate. FCM extracts the important data features from the protein sequence which then initializes the deep neural networks effectively. It was found that the Levenberg–Marquardt (LM) algorithm for deep neural networks can improve the prediction accuracy of the protein folding rates. The correlation coefficient between the predicted values and those real values obtained from experiments reached 0.94 and 0.9 in two independent numerical tests.

scholarly journals Long‐Time Protein Folding Dynamics from Short‐Time Molecular Dynamics Simulations

2006 ◽  
Vol 5 (4) ◽  
pp. 1214-1226 ◽  
Author(s):  
John D. Chodera ◽  
William C. Swope ◽  
Jed W. Pitera ◽  
Ken A. Dill
Keyword(s):  
Molecular Dynamics ◽  
Protein Folding ◽  
Molecular Dynamics Simulations ◽  
Folding Dynamics ◽  
Long Time ◽  
Dynamics Simulations ◽  
Short Time

scholarly journals Molecular origins of internal friction effects on protein-folding rates

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
David de Sancho ◽  
Anshul Sirur ◽  
Robert B. Best
Keyword(s):  
Protein Folding ◽  
Internal Friction ◽  
Folding Rates

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.

Sign in / Sign up

Export Citation Format

Share Document