Structural basis of mitochondrial receptor binding and constriction by DRP1.

Mitochondrial inheritance, genome maintenance and metabolic adaptation depend on organelle fission by dynamin-related protein 1 (DRP1) and its mitochondrial receptors. DRP1 receptors include the paralogues mitochondrial dynamics proteins of 49 and 51 kDa (MID49 and MID51) and mitochondrial fission factor (MFF); however, the mechanisms by which these proteins recruit and regulate DRP1 are unknown. Here we present a cryo-electron microscopy structure of full-length human DRP1 co-assembled with MID49 and an analysis of structure- and disease-based mutations. We report that GTP induces a marked elongation and rotation of the GTPase domain, bundle-signalling element and connecting hinge loops of DRP1. In this conformation, a network of multivalent interactions promotes the polymerization of a linear DRP1 filament with MID49 or MID51. After co-assembly, GTP hydrolysis and exchange lead to MID receptor dissociation, filament shortening and curling of DRP1 oligomers into constricted and closed rings. Together, these views of full-length, receptor- and nucleotide-bound conformations reveal how DRP1 performs mechanical work through nucleotide-driven allostery

Modeling Disordered Regions in Proteins Using Rosetta

Protein structure prediction methods such as Rosetta search for the lowest energy conformation of the polypeptide chain. However, the experimentally observed native state is at a minimum of the free energy, rather than the energy. The neglect of the missing configurational entropy contribution to the free energy can be partially justified by the assumption that the entropies of alternative folded states, while very much less than unfolded states, are not too different from one another, and hence can be to a first approximation neglected when searching for the lowest free energy state. The shortcomings of current structure prediction methods may be due in part to the breakdown of this assumption. Particularly problematic are proteins with significant disordered regions which do not populate single low energy conformations even in the native state. We describe two approaches within the Rosetta structure modeling methodology for treating such regions. The first does not require advance knowledge of the regions likely to be disordered; instead these are identified by minimizing a simple free energy function used previously to model protein folding landscapes and transition states. In this model, residues can be either completely ordered or completely disordered; they are considered disordered if the gain in entropy outweighs the loss of favorable energetic interactions with the rest of the protein chain. The second approach requires identification in advance of the disordered regions either from sequence alone using for example the DISOPRED server or from experimental data such as NMR chemical shifts. During Rosetta structure prediction calculations the disordered regions make only unfavorable repulsive contributions to the total energy. We find that the second approach has greater practical utility and illustrate this with examples from de novo structure prediction, NMR structure calculation, and comparative modeling

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Atomic structure of Hsp90-Cdc37-Cdk4 reveals that Hsp90 traps and stabilizes an unfolded kinase

The Hsp90 molecular chaperone and its Cdc37 cochaperone help stabilize and activate more than half of the human kinome. However, both the mechanism by which these chaperones assist their "client" kinases and the reason why some kinases are addicted to Hsp90 while closely related family members are independent are unknown. Our structural understanding of these interactions is lacking, as no full-length structures of human Hsp90, Cdc37, or either of these proteins with a kinase have been elucidated. Here we report a 3.9 angstrom cryo-electron microscopy structure of the Hsp90-Cdc37-Cdk4 kinase complex. Surprisingly, the two lobes of Cdk4 are completely separated with the β4-β5 sheet unfolded. Cdc37 mimics part of the kinase N lobe, stabilizing an open kinase conformation by wedging itself between the two lobes. Finally, Hsp90 clamps around the unfolded kinase β5 strand and interacts with exposed N- and C-lobe interfaces, protecting the kinase in a trapped unfolded state. On the basis of this structure and an extensive amount of previously collected data, we propose unifying conceptual and mechanistic models of chaperone-kinase interactions

Structural basis of mitochondrial receptor binding and constriction by DRP1.

Mitochondrial inheritance, genome maintenance and metabolic adaptation depend on organelle fission by dynamin-related protein 1 (DRP1) and its mitochondrial receptors. DRP1 receptors include the paralogues mitochondrial dynamics proteins of 49 and 51 kDa (MID49 and MID51) and mitochondrial fission factor (MFF); however, the mechanisms by which these proteins recruit and regulate DRP1 are unknown. Here we present a cryo-electron microscopy structure of full-length human DRP1 co-assembled with MID49 and an analysis of structure- and disease-based mutations. We report that GTP induces a marked elongation and rotation of the GTPase domain, bundle-signalling element and connecting hinge loops of DRP1. In this conformation, a network of multivalent interactions promotes the polymerization of a linear DRP1 filament with MID49 or MID51. After co-assembly, GTP hydrolysis and exchange lead to MID receptor dissociation, filament shortening and curling of DRP1 oligomers into constricted and closed rings. Together, these views of full-length, receptor- and nucleotide-bound conformations reveal how DRP1 performs mechanical work through nucleotide-driven allostery

Comparison of energy versus rmsd and free energy versus rmsd plots for case with disordered internal loop (2k0J).

<p>A) Rosetta all atom energy and B) free energy computed using Eq. (1) with predicted disordered regions (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022060#pone-0022060-g003" target="_blank">Fig 3B</a>-2k0j). The energy shown in A is calculated using the Rosetta all-atom energy. In A and B, the x-axis is the RMSD to the folded portion of the native structure. The 10 lowest energy/free energy decoys are shown in black. The dashed orange lines are provided to aid comparison of the two plots. (<b>C)</b>. Compensation between the entropic and energetic contributions to the free energy (Eq. (1)).</p

Protein sequences used to test the prediction of disordered termini.

<p>Protein sequences used to test the prediction of disordered termini.</p

Test cases for 2^nd approach.

a<p>Residues predicted to be disordered are shown in bold font.</p>b<p>Assumed from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022060#pone-0022060-t001" target="_blank">Table 1</a>, tails of 1enh are constructed based on the gene sequence recovered from the gene sequence, in which we assumed these regions likely to be disordered, and was mostly consistent with the prediction results using the DISOPRED2.</p>c<p>http://bioinf.cs.ucl.ac.uk/disopred/ <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022060#pone.0022060-Ward1" target="_blank">[8]</a>.</p>d<p>Disordered regions were predicted using “Predicted order parameter (S²)” calculated from backbone chemical shifts data with BMRB accession number 6571 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022060#pone.0022060-Berjanskii1" target="_blank">[11]</a>.</p>e<p>This is the target T0460 in CASP8 directly downloaded from <a href="http://predictioncenter.org/download_area/CASP8/targets/" target="_blank">http://predictioncenter.org/download_area/CASP8/targets/</a>.</p>f<p>The same method as described on ^d with BMRB accession number 15805.</p>g<p>This is the target T0482 in CASP8 directly downloaded from <a href="http://predictioncenter.org/download_area/CASP8/targets/" target="_blank">http://predictioncenter.org/download_area/CASP8/targets/</a>.</p

Results of disordered internal loop predictions.

<p>(<b>A</b>) Comparisons of prediction accuracy using the free energy function with optimized parameters (<i>β</i> = 1.5 and <i>L₀</i> = 0.3) with that of a null model. The y-axis shows disorder prediction accuracy over the benchmark set using Eq. (2). The x-axis shows the prediction of the null model, which assumes all residues are ordered. (<b>B</b>) Examples of successful prediction of disordered internal loops. Blue line: the actual disordered regions assessed from the residue deviations in the NMR structure. Red line: frequency of disorder assignment by optimization of Eq. (1) over decoy population.</p