Protein folding requires crowd control in a simulated cell - PubMed (original) (raw)

Protein folding requires crowd control in a simulated cell

Benjamin R Jefferys et al. J Mol Biol. 2010.

Abstract

Macromolecular crowding has a profound effect upon biochemical processes in the cell. We have computationally studied the effect of crowding upon protein folding for 12 small domains in a simulated cell using a coarse-grained protein model, which is based upon Langevin dynamics, designed to unify the often disjoint goals of protein folding simulation and structure prediction. The model can make predictions of native conformation with accuracy comparable with that of the best current template-free models. It is fast enough to enable a more extensive analysis of crowding than previously attempted, studying several proteins at many crowding levels and further random repetitions designed to more closely approximate the ensemble of conformations. We found that when crowding approaches 40% excluded volume, the maximum level found in the cell, proteins fold to fewer native-like states. Notably, when crowding is increased beyond this level, there is a sudden failure of protein folding: proteins fix upon a structure more quickly and become trapped in extended conformations. These results suggest that the ability of small protein domains to fold without the help of chaperones may be an important factor in limiting the degree of macromolecular crowding in the cell. Here, we discuss the possible implications regarding the relationship between protein expression level, protein size, chaperone activity and aggregation.

Copyright (c) 2010 Elsevier Ltd. All rights reserved.

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Figures

Fig. 1

Fig. 1

Structure prediction results. TM scores for the proteins are shown on the left. The shape and colour indicate broad protein class: red circles are all-helical, blue squares are all-strand and green diamonds are mixed. Twenty-four targets were predicted with structures better than 0.3 in TM score, and four of those were predicted with structures better than 0.4 in TM score. For comparison, we plotted the largest proportion of the protein that can be aligned to the native at less than 5 Å RMSD on the right. The point shape and colour are as those for the graph on the left.

Fig. 2

Fig. 2

Best of five predictions and the best structure (by TM score) generated by the model during structure prediction for an all-helical protein, a mixed protein and an all-sheet protein. Proteins are shown coloured from red at the C-terminus to blue at the N-terminus, with arrows representing strands and ribbons representing helices. The best TM score structure in the right-hand column shows the best conformation, as compared with the known native structure, out of several thousands of structures produced by 150 separate folding trajectories and therefore does not represent a prediction but rather the theoretical best prediction that could be made by poing. Images were generated using PyMOL.

Fig. 3

Fig. 3

Illustration of model for synthesizing protein into a crowded container. The white curly lines represent springs that hold together the ribosome, ribosome exit point and container and keep particles inside the container. The ribosome is the large red circle (only partially visible), the ribosome synthesis exit point is the small blue circle on the ribosome's surface, the crowding macromolecules are in translucent gray and the protein is shown with colours going from red at the C-terminus to purple at the N-terminus.

Fig. 4

Fig. 4

A series of images showing the synthesis of a protein into a cell with 41% volume excluded by macromolecules. Each image is approximately 10,000 time steps apart. The protein is shown in cartoon form, with ribbons showing the assigned secondary structure. Colour scheme is as that for Fig. 3, with translucent crowding macromolecules. Note that the protein is becoming squeezed between the crowding macromolecules as it is synthesized. Images were generated by poing, the protein model used for this work.

Fig. 5

Fig. 5

Effects of crowding upon conformation size, conformational freedom and native-like time. N = 1.2 million for each crowding level for all graphs, although structures produced during simulation of a single folding trajectory are not strictly independent, and 1200 folding trajectories were simulated. The illustrations at the bottom show how crowding changes the space remaining for the protein to fold. Colour scheme is as that for Fig. 3. All elements have proportions designed to show the relative excluded volume resulting from crowding as relative excluded area in two dimensions—for example, at 50% crowding, 50% of the area within the container not occupied by the ribosome is occupied by macromolecules. (Row A) These illustrate the space that remains for a successfully folded protein. Space is limited at 50% (c), but there is still space for more proteins in the three-dimensional case. (Row B) These illustrate the largest effect of crowding upon folding. At 14% (d), there is no measurable effect on the folded protein. At 36% (e), proteins are starting to get trapped in non-native but compact conformations. At 50% (f), proteins are becoming trapped in extended conformations.

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