↓ Skip to main content

Modeling protein folding in vivo

Overview of attention for article published in Biology Direct, July 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
1 blog
twitter
2 X users
f1000
1 research highlight platform

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
59 Mendeley
Title
Modeling protein folding in vivo
Published in
Biology Direct, July 2018
DOI 10.1186/s13062-018-0217-6
Pubmed ID
Authors

Irina Sorokina, Arcady Mushegian

Abstract

A half century of studying protein folding in vitro and modeling it in silico has not provided us with a reliable computational method to predict the native conformations of proteins de novo, let alone identify the intermediates on their folding pathways. In this Opinion article, we suggest that the reason for this impasse is the over-reliance on current physical models of protein folding that are based on the assumption that proteins are able to fold spontaneously without assistance. These models arose from studies conducted in vitro on a biased sample of smaller, easier-to-isolate proteins, whose native structures appear to be thermodynamically stable. Meanwhile, the vast empirical data on the majority of larger proteins suggests that once these proteins are completely denatured in vitro, they cannot fold into native conformations without assistance. Moreover, they tend to lose their native conformations spontaneously and irreversibly in vitro, and therefore such conformations must be metastable. We propose a model of protein folding that is based on the notion that the folding of all proteins in the cell is mediated by the actions of the "protein folding machine" that includes the ribosome, various chaperones, and other components involved in co-translational or post-translational formation, maintenance and repair of protein native conformations in vivo. The most important and universal component of the protein folding machine consists of the ribosome in complex with the welcoming committee chaperones. The concerted actions of molecular machinery in the ribosome peptidyl transferase center, in the exit tunnel, and at the surface of the ribosome result in the application of mechanical and other forces to the nascent peptide, reducing its conformational entropy and possibly creating strain in the peptide backbone. The resulting high-energy conformation of the nascent peptide allows it to fold very fast and to overcome high kinetic barriers along the folding pathway. The early folding intermediates in vivo are stabilized by interactions with the ribosome and welcoming committee chaperones and would not be able to exist in vitro in the absence of such cellular components. In vitro experiments that unfold proteins by heat or chemical treatment produce denaturation ensembles that are very different from folding intermediates in vivo and therefore have very limited use in reconstructing the in vivo folding pathways. We conclude that computational modeling of protein folding should deemphasize the notion of unassisted thermodynamically controlled folding, and should focus instead on the step-by-step reverse engineering of the folding process as it actually occurs in vivo. This article was reviewed by Eugene Koonin and Frank Eisenhaber.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 24%
Student > Master 8 14%
Student > Bachelor 8 14%
Professor 2 3%
Other 2 3%
Other 7 12%
Unknown 18 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 22 37%
Engineering 4 7%
Physics and Astronomy 3 5%
Chemistry 3 5%
Agricultural and Biological Sciences 2 3%
Other 9 15%
Unknown 16 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 March 2024.
All research outputs
#4,195,848
of 25,721,020 outputs
Outputs from Biology Direct
#153
of 538 outputs
Outputs of similar age
#73,953
of 342,036 outputs
Outputs of similar age from Biology Direct
#1
of 5 outputs
Altmetric has tracked 25,721,020 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 538 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 342,036 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them