Schematic of the ODE model.

<p>Background colors represent translocation (orange), unfolding (green), misfolding (yellow), and aggregation/disaggregation (red) modules. Additional states and reactions involving the luminal co-chaperone Scj1 are accounted for in the aggregation, unfolded, and misfolded modules, but are omitted from this diagram due to space limitations. Species are represented as follows: Pore; nascent protein (N), sliding state (x); folded protein (F); unfolded protein (U); misfolded protein (M); BiP; Sec63; size 2 aggregate (A2); size 3 aggregate (A3); size 4 aggregate (A4). Sliding states (x) mimic the movement of the nascent protein further into the lumen.</p

Parameter map.

<p>Map of the parameter study indentifies effects of varying 7 parameters with respect to protein folding efficiency.</p

Folding efficiency vs. BiP binding rate without cooperativity.

<p>Comparison of the folding efficiency (<i>i.e.</i> fraction of proteins folded) as a function of the binding rate between BiP and unfolded proteins. In this scenario, there is no cooperative effect among chaperones in folding, unfolding, or disaggregating proteins. The model number refers to the number of binding sites. In this scenario, Model 1 has the highest folding efficiency, followed by Models 2, 3 and 4.</p

Folding efficiency vs. BiP binding rate with cooperativity.

<p>Comparison of the folding efficiency (<i>i.e.</i> fraction of proteins folded) as a function of the binding rate between BiP and unfolded proteins with a cooperativity factor of C = 10. In this scenario, Model 2 has the highest folding efficiency.</p

Folding efficiency vs. translocation rate.

<p>Folding efficiency as a function of translocation flux.</p

Models defined by number of binding sites.

<p>Schematic of the 4 models defined by the number of binding sites.</p

Protein coverage.

<p>Time series of the amount of bound protein for the different models, showing greater coverage for the single binding site model.</p

Relative protein coverage.

<p>Protein coverage of the four models relative to Model 1 in the noncooperative scenario. Coverage refers to the percentage of proteins that are protected from misfolded and aggregation at any one time.</p

Folding efficiency vs. number of BiP molecules.

<p>Comparison of the folding efficiency as a function of the number of BiP molecules with no cooperativity and U = 1.0 · 10⁶ molecules. In this scenario, Model 1 folds most efficiently.</p

Coarse-Grained Model for Colloidal Protein Interactions, <i>B</i>₂₂, and Protein Cluster Formation

Reversible protein cluster formation is an important initial step in the processes of native and non-native protein aggregation, but involves relatively long time and length scales for detailed atomistic simulations and extensive mapping of free energy landscapes. A coarse-grained (CG) model is presented to semiquantitatively characterize the thermodynamics and key configurations involved in the landscape for protein oligomerization, as well as experimental measures of interactions such as the osmotic second virial coefficient (<i>B</i>₂₂). Based on earlier work (Grüenberger et al., <i>J. Phys. Chem. B</i> <b>2013</b>, <i>117</i>, 763), this CG model treats proteins as rigid bodies composed of one bead per amino acid, with each amino acid having specific parameters for its size, hydrophobicity, and charge. The net interactions are a combination of steric repulsions, short-range attractions, and screened long-range charge–charge interactions. Model parametrization was done by fitting simulation results against experimental value of <i>B</i>₂₂ as a function of solution ionic strength for α-chymotrypsinogen A and γD-Crystallin (gD-Crys). The CG model is applied to characterize the pairwise interactions and dimerization of gD-Crys and the dependence on temperature, protein concentration, and ionic strength. The results illustrate that at experimentally relevant conditions where stable dimers do not form, the entropic contributions are predominant in the free-energy of protein cluster formation and colloidal protein interactions, arguing against interpretations that treat <i>B</i>₂₂ primarily from energetic considerations alone. Additionally, the results suggest that electrostatic interactions help to modulate the population of the different stable configurations for protein nearest-neighbor pairs, while short-range attractions determine the relative orientations of proteins within these configurations. Finally, simulation results are combined with Principal Component Analysis to identify those amino-acids/surface patches that form interprotein contacts at conditions that favor dimerization of gD-Crys. The resulting regions agree with previously found aggregation-prone sites, as well as suggesting new ones that may be important