Construction and testing of the critical structure (CS) and the changed critical structure (ΔCS) proteins.

<p><b>A)</b> The wild type protein structure is obtained from RCSB Protein Data Bank. <b>B)</b> The WT protein is run through UMS to identify the critical residues (shown in red). <b>C)</b> For the CS protein, the critical residues are kept in place and the remaining residues are mutated according to the rules of the allowed substitutions list (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189064#pone.0189064.s004" target="_blank">S4 Fig</a>). <b>D)</b> For the ΔCS protein, each of the critical residues is mutated to alanine. <b>E)</b> Both the CS and the ΔCS structures were equilibrated in water for 100 ns as described in Methods section. <b>F)</b> The CS structure is run through UMS to identify consistencies in critical residues. <b>G)</b> The ΔCS structure is run through UMS to identify changes in critical residues.</p

Critical residue and foldability comparison across myoglobin for 6 species.

<p><b>A)</b> The output colored structure from UMS analysis of the 6 proteins. The red residues represent the critical residues, while the blue shows the residues that may be substituted with other residues. <b>B)</b> Pairwise comparison of human myoglobin with the 5 other species. The black outlines represent a 95% confidence interval for the data. The statistics of the graph are summarized in <b>C)</b>. <b>D)</b> The density plot shows the distribution of foldabilties in each of the structures.</p

Global computational mutagenesis provides a critical stability framework in protein structures

<div><p>A protein’s amino acid sequence dictates the folds and final structure the macromolecule will form. We propose that by identifying critical residues in a protein’s atomic structure, we can select a critical stability framework within the protein structure essential to proper protein folding. We use global computational mutagenesis based on the unfolding mutation screen to test the effect of every possible missense mutation on the protein structure to identify the residues that cannot tolerate a substitution without causing protein misfolding. This method was tested using molecular dynamics to simulate the stability effects of mutating critical residues in proteins involved in inherited disease, such as myoglobin, p53, and the 15^th sushi domain of complement factor H. In addition we prove that when the critical residues are in place, other residues may be changed within the structure without a stability loss. We validate that critical residues are conserved using myoglobin to show that critical residues are the same for crystal structures of 6 different species and comparing conservation indices to critical residues in 9 eye disease-related proteins. Our studies demonstrate that by using a selection of critical elements in a protein structure we can identify a critical protein stability framework. The frame of critical residues can be used in genetic engineering to improve small molecule binding for drug studies, identify loss-of-function disease-causing missense mutations in genetics studies, and aide in identifying templates for homology modeling.</p></div

The critical residues frame for atomic protein structures.

<p>The <b>A)</b> p53, <b>B)</b> domain S15 of complement factor H, <b>C)</b> alpha-tocopherol transfer proteins are shown. The red residues represent the critical residues within the structures.</p

Comparison of stability between the CS and ΔCS proteins using Ramachandran plots and residue-residue distances.

<p><b>A)</b> The plots for both the CS and ΔCS myoglobin structures. <b>B)</b> The plots for both the CS and ΔCS p53 structures. <b>C)</b> The plots for both the CS and ΔCS sushi domain 15 of complement factor H structures.</p

Comparison of the foldability and conservation index parameters.

<p>Comparison of the foldability and conservation index parameters.</p

Molecular dynamics (MD) were used to simulate the affect of mutating protiens to CS and ΔCS.

<p>Critical residues for each of the structure are red and were calculated independently. <b>A)</b> 52% of noncritical human myoglobin residues were changed. The CS structure is superimposed on top of the WT human myoglobin structure. <b>B)</b> The critical residues of human myoglobin were changed to alanine residues, accounting for 12% of the residues in the structure. The ΔCS protein is superimposed on top of the WT human myoglobin structure. <b>C)</b> The RMSD for CS and ΔCS myoglobin is plotted for the MD simulation. <b>D)</b> The CS p53 with 53% of WT residues changed superimposed on the WT protein. <b>E)</b> The ΔCS p53 with 15% of residues changed superimposed on the WT protein. <b>F)</b> The RMSD for CS and ΔCS p53 is plotted for the MD simulation. <b>G)</b> The CS sushi domain 15 of complement factor H with 47% of WT residues changed superimposed on the WT protein. <b>H)</b> The ΔCS sushi domain 15 of complement factor H with 23% of residues changed superimposed on the WT protein. <b>I)</b> The RMSD for CS and ΔCS sushi domain 15 of complement factor H is plotted for the MD simulation.</p

Overview of the homo- and heteromolecular associations of β-crystallins.

<p>Top panel: βA3, βB1, and βB2 self-associate in a reversible manner to form dimers. The homo-associations of βA3 and βB2 exhibit endothermic enthalpy and are driven by entropy as a result of hydrophobic interactions between protein molecules. In contrast, the self-association of βB1 is driven by exothermic enthalpy due to van der Waals interactions and hydrogen bonds at the dimer interface. Bottom panel: The βB1/βA3 complex is likely formed by the association of hetero-dimers but we cannot rule out that it is formed from homodimers. Similar to that of βB1 alone, the formation of the tetramer is driven by exothermic enthalpy. Structures of βB1 and βB2 were obtained from the protein database RCSB (files: 1 oki and 1 blb, respectively). Closed and open structures of βA3 were modeled as described earlier <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029227#pone.0029227-Sergeev1" target="_blank">[1]</a>. From our results we cannot say which monomer conformation exists within the hetero-tetramer. However, the majority of known crystal structures of β-crystallins (3 of 4) are of the closed monomer type suggesting this is the most stable conformation. Therefore, the structure of the hypothetical tetrameric βB1/βA3 complex was generated using the crystal packing of βB1 crystallin as a template (PDB file: 1 oki).</p

The temperature dependence of changes in the free energies for the dimeric association of βB1 and βA3 and the tetrameric association of βB1/βA3.

<p>Asssociation of βB1, βA3 and βB/βA3 are shown by blue open triangles, black open squares and red open circles, respectively. Panels A and B: van't Hoff plots where <i>ln(K_d/C_o)</i> is plotted as function of the reciprocal of absolute temperature (<i>1000/T</i>), <i>K_d's</i> are the dissociation constants obtained from analytical ultracentrifugation, and <i>C_o</i> is the µM concentration. Panel A: the difference in heat capacity (<i>ΔC_p</i>) is constrained to be 0, resulting in a linear function; Panel B: <i>ΔC_p</i> is not constrained and has a nonzero value. Panel C: temperature dependence of Gibbs free energy gained in formation of βB1/βA3. <i>ΔΔG_d (βB1/βA3)</i> is defined as a difference between Gibbs free energy changes of tetrameric βB1/βA3 and that of individual components (βB1 and βA3). Concentrations for βB1, βA3, and βB1/βA3 crystallins were each 0.5 mg/ml.</p

Thermodynamic profiles for the associations of homodimeric βB1 and βA3 and tetrameric βB1/βA3.

<p>Thermodynamics parameters, enthalpy <i>ΔH_a</i>, and entropy <i>ΔS_a</i> changes were determined using linear (<i>ΔC_p = 0</i>) and nonlinear (<i>ΔC_p≠0</i>) fitting functions into van't Hoff plots. The Gibbs free energy changes <i>ΔG_a</i> were calculated using formula <i>ΔG_a = ΔH_a−TΔS_a</i>, where <i>T</i> is temperature in K; e.u. = 1 cal/(deg mol).</p