Protein folding, structure prediction and design

2014 ◽  
Vol 42 (2) ◽  
pp. 225-229 ◽  
Author(s):  
David Baker
Keyword(s):  
Protein Folding ◽  
Protein Structure ◽  
Protein Design ◽  
Structure Prediction ◽  
Experimental Studies ◽  
Folding Rate ◽  
Contact Order ◽  
Folding Structure ◽  
Protein Folding Rate ◽  
Rate Correlation

I describe how experimental studies of protein folding have led to advances in protein structure prediction and protein design. I describe the finding that protein sequences are not optimized for rapid folding, the contact order–protein folding rate correlation, the incorporation of experimental insights into protein folding into the Rosetta protein structure production methodology and the use of this methodology to determine structures from sparse experimental data. I then describe the inverse problem (protein design) and give an overview of recent work on designing proteins with new structures and functions. I also describe the contributions of the general public to these efforts through the Rosetta@home distributed computing project and the FoldIt interactive protein folding and design game.

scholarly journals Current protein structure predictors do not produce meaningful folding pathways

2021 ◽  
Author(s):  
Carlos Outeiral Rubiera ◽  
Charlotte Deane ◽  
Daniel Allen Nissley
Keyword(s):  
Protein Folding ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
State Of The Art ◽  
Predictive Ability ◽  
Folding Rate ◽  
Recent Advances ◽  
Folding Pathways ◽  
Open Question

Protein structure prediction has long been considered a gateway problem for understanding protein folding. Recent advances in deep learning have achieved unprecedented success at predicting a protein's crystal structure, but whether this achievement relates to a better modelling of the folding process remains an open question. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental folding data. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathwhay, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with parameters such as intermediate structures and the folding rate constant. These results suggest that recent advances in protein structure prediction do not yet provide an enhanced understanding of the principles underpinning protein folding.

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.

scholarly journals Protein structure prediction and design in a biologically-realistic implicit membrane

2019 ◽  
Author(s):  
Rebecca F. Alford ◽  
Patrick J. Fleming ◽  
Karen G. Fleming ◽  
Jeffrey J. Gray
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Membrane Proteins ◽  
Membrane Protein ◽  
Protein Structure Prediction ◽  
Protein Design ◽  
Structure Prediction ◽  
De Novo ◽  
Computational Design ◽  
Amino Acid Distribution

ABSTRACTProtein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational bench-marks against experimental targets including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure dis-crimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Further, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.Significance StatementMembrane proteins participate in many life processes including transport, signaling, and catalysis. They constitute over 30% of all proteins and are targets for over 60% of pharmaceuticals. Computational design tools for membrane proteins will transform the interrogation of basic science questions such as membrane protein thermodynamics and the pipeline for engineering new therapeutics and nanotechnologies. Existing tools are either too expensive to compute or rely on manual design strategies. In this work, we developed a fast and accurate method for membrane protein design. The tool is available to the public and will accelerate the experimental design pipeline for membrane proteins.

Sign in / Sign up

Export Citation Format

Share Document