Mapping an Aggregation Nucleus One Protein at a Time

Small transient protein aggregates are the bottleneck through which partly unfolded protein monomers must funnel before they can form large stable aggregates. The exact size of the “thermodynamically stable nucleus” for aggregation is difficult to determine, as are the free-energy changes for addition or subtraction of individual monomers from the nucleus. Here, we measured the thermodynamic nucleus size and free energy for a well-defined protein construct. We used tethered trimers and tetramers of the aggregation-prone protein U1A. The protein’s folding kinetics served as a “clock” for aggregation dynamics. As shown previously, at <i>n</i> = 2, the transient aggregate is least stable compared to the native state. At <i>n</i> = 4, the aggregate state finally becomes thermodynamically more stable than the native state. Quantitative aggregation nucleus data provide key input for the next generation of coarse-grained and all-atom simulations of early stages of protein aggregation

Detection-Dependent Kinetics as a Probe of Folding Landscape Microstructure

The folding landscapes of polypeptides and proteins exhibit a hierarchy of local minima. The causes range from proline isomerization all the way down to microstructure in the free energy caused by residual frustration inherent in even the best 20 amino acid design. The corresponding time scales range from hours to submicroseconds. The smallest microstructures are difficult to detect. We have measured the folding/unfolding kinetics of the engineered trpzip2 peptide at different tryptophan fluorescence wavelengths, each yielding a different rate. Wavelength-dependent folding kinetics on 0.1−2 μs time scales show that different microstructures with a range of solvent exposure and local dynamics are populated. We estimate a lower limit for the roughness of the free energy surface based on the range of rates observed

The Fast and the Slow: Folding and Trapping of λ_6–85

Molecular dynamics simulations combining many microsecond trajectories have recently predicted that a very fast folding protein like lambda repressor fragment λ6–85 D14A could have a slow millisecond kinetic phase. We investigated this possibility by detecting temperature-jump relaxation to 5 ms. While λ6–85 D14A has no significant slow phase, two even more stable mutants do. A slow phase of λ6–85 D14A does appear in mild denaturant. The experimental data and computational modeling together suggest the following hypothesis: λ6–85 takes only microseconds to reach its native state from an extensively unfolded state, while the latter takes milliseconds to reach compact β-rich traps. λ6–85 is not only thermodynamically but also kinetically protected from reaching such “intramolecular amyloids” while folding

A Learning Algorithm to Discover Soluble Vesicle-Binding Helical Peptides

Membrane peptide folding studies require peptides that bind to lipid vesicles while remaining water-soluble. Currently available peptides are either artificial designs, or they have membrane-disrupting antimicrobial or venomous activity. As a first step to derive new soluble membrane-binding peptides from naturally occurring membrane proteins, we trained a learning algorithm on several water-soluble and insoluble helical peptides by comparing its predictions with experimental solubility and fluorescence vesicle binding assays. The algorithm yielded an easily computed score S to discover soluble peptides in databases of transmembrane helical proteins. To validate the algorithm, we selected four helices based on a good S score. Experiments showed that all four are soluble at >25 μM, and that three bind to vesicles. We illustrate with an example that the vesicle binding of such peptides can be temperature-tuned. Finally, we predict four additional peptides that should be water-soluble and able to bind to lipid vesicles

Coupled Protein Diffusion and Folding in the Cell

<div><p>When a protein unfolds in the cell, its diffusion coefficient is affected by its increased hydrodynamic radius and by interactions of exposed hydrophobic residues with the cytoplasmic matrix, including chaperones. We characterize protein diffusion by photobleaching whole cells at a single point, and imaging the concentration change of fluorescent-labeled protein throughout the cell as a function of time. As a folded reference protein we use green fluorescent protein. The resulting region-dependent anomalous diffusion is well characterized by 2-D or 3-D diffusion equations coupled to a clustering algorithm that accounts for position-dependent diffusion. Then we study diffusion of a destabilized mutant of the enzyme phosphoglycerate kinase (PGK) and of its stable control inside the cell. Unlike the green fluorescent protein control's diffusion coefficient, PGK's diffusion coefficient is a non-monotonic function of temperature, signaling ‘sticking’ of the protein in the cytosol as it begins to unfold. The temperature-dependent increase and subsequent decrease of the PGK diffusion coefficient in the cytosol is greater than a simple size-scaling model suggests. Chaperone binding of the unfolding protein inside the cell is one plausible candidate for even slower diffusion of PGK, and we test the plausibility of this hypothesis experimentally, although we do not rule out other candidates.</p></div

The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog

<div><p>How simple is the underlying control mechanism for the complex locomotion of vertebrates? We explore this question for the swimming behavior of zebrafish larvae. A parameter-independent method, similar to that used in studies of worms and flies, is applied to analyze swimming movies of fish. The motion itself yields a natural set of fish "eigenshapes" as coordinates, rather than the experimenter imposing a choice of coordinates. Three eigenshape coordinates are sufficient to construct a quantitative "postural space" that captures >96% of the observed zebrafish locomotion. Viewed in postural space, swim bouts are manifested as trajectories consisting of cycles of shapes repeated in succession. To classify behavioral patterns quantitatively and to understand behavioral variations among an ensemble of fish, we construct a "behavioral space" using multi-dimensional scaling (MDS). This method turns each cycle of a trajectory into a single point in behavioral space, and clusters points based on behavioral similarity. Clustering analysis reveals three known behavioral patterns—scoots, turns, rests—but shows that these do not represent discrete states, but rather extremes of a continuum. The behavioral space not only classifies fish by their behavior but also distinguishes fish by age. With the insight into fish behavior from postural space and behavioral space, we construct a two-channel neural network model for fish locomotion, which produces strikingly similar postural space and behavioral space dynamics compared to real zebrafish.</p></div

Dodine as a Protein Denaturant: The Best of Two Worlds?

Traditional denaturants such as urea and guanidinium ion unfold proteins in a cooperative “all-or-none” fashion. However, their high working concentration in combination with their strong absorption in the far ultraviolet region make it impossible to measure high quality circular dichroism or infrared spectra, which are commonly used to detect changes in protein secondary structure. On the other hand, detergents such as dodecyl sulfate destabilize native protein conformation at low millimolar concentrations and are UV transparent, but they denature proteins more gradually than guanidinium or urea. In this work, we studied the denaturation properties of the fungicide dodecylguanidinium acetate (dodine), which combines both denaturants into one. We show that dodine unfolds some small proteins at millimolar concentrations, facilitates temperature denaturation, and is transparent enough at its working concentration, unlike guanidinium, to measure full range circular dichroism spectra. Our results also suggest that dodine allows fine-tuning of the protein’s unfolded state, unlike traditional “all-or-none” denaturants

Comparison of the diffusion coefficients of the GFP, ltPGK-GFP and ltPGK-FRET measured from 22°C to 37°C.

<p>All proteins diffuse faster at higher temperatures while folded. The “lt” proteins show accelerated diffusion followed by a turnaround at <i>T</i> near <i>T</i>_m. Global model fits are to equation 1 (solid thick lines) and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113040#pone.0113040.e002" target="_blank">equation 2</a> (dotted thick lines).</p

2-D model fits for GFP diffusion inside the cell shown in Figures 2-4.

<p>The color names in fits C and D refer to the regions of the cell in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113040#pone-0113040-g004" target="_blank">Figure 4E</a>.</p><p>* Anomalous factor α fixed at 1 in all regions for multi domain, normal diffusion simulations.</p>†<p>Mean squared deviation calculated across all regions in multi domain simulations.</p><p>2-D model fits for GFP diffusion inside the cell shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113040#pone-0113040-g002" target="_blank">Figures 2</a>-<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113040#pone-0113040-g004" target="_blank">4</a>.</p

Donor over acceptor (D/A) ratios for protein unfolding and protein-protein interaction.

<p>PGK-FRET unfolding (gray) has a midpoint at approximately 42°C for the U2OS cell data shown here, unfolding begins at 37°C. ltPGK-FRET starts interacting with Hsp70-mCherry extensively above 35°C, whereas htPGK-FRET simply continues the room temperature linear trend up to 45°C.</p