Cytoplasmic dynein binding, run length, and velocity are guided by long-range electrostatic interactions

Abstract

Dyneins are important molecular motors involved in many essential biological processes, including cargo transport along microtubules, mitosis, and in cilia. Dynein motility involves the coupling of microtubule binding and unbinding to a change in the configuration of the linker domain induced by ATP hydrolysis, which occur some 25 nm apart. This leaves the accuracy of dynein stepping relatively inaccurate and susceptible to thermal noise. Using multi-scale modeling with a computational focusing technique, we demonstrate that the microtubule forms an electrostatic funnel that guides the dynein's microtubule binding domain (MTBD) as it finally docks to the precise, keyed binding location on the microtubule. Furthermore, we demonstrate that electrostatic component of the MTBD's binding free energy is linearly correlated with the velocity and run length of dynein, and we use this linearity to predict the effect of mutating each glutamic and aspartic acid located in MTBD domain to alanine. Lastly, we show that the binding of dynein to the microtubule is associated with conformational changes involving several helices, and we localize flexible hinge points within the stalk helices. Taken all together, we demonstrate that long range electrostatic interactions bring a level of precision to an otherwise noisy dynein stepping process.

Figure 1. Electrostatic potential mapped onto the MTBD and tubulin dimer.

(a,d) show the side view and the top view of the electrostatic potential distribution on the surface of tubulin, respectively (filled space, red = negative, blue = positive). (b,c) show the side view of the electrostatic potential distribution on the microtubule binding interface of the low- and high-affinity MTBD structures, respectively. (e,f) show the bottom view of the electrostatic potential distribution on the microtubule binding interface of the low- and high-affinity MTBD structures, respectively. In (a,d) the MTBD (cyan ribbon structure) and in (b,c,e,f) the tubulins (α-tubulin, orange ribbon structure and β-tubulin, yellow ribbon structure) are shown for reference, although the potentials are calculated without them.

Figure 2. Electrostatic binding energy map of the low-affinity MTBD structure and microtubule.

(a) 2D representation of the binding energy map in the vicinity of a single tubulin dimer on the microtubule. The reported binding energy (color map) is the binding energy of the most favorable orientation at that particular location for the MTBD on the microtubule surface (see Methods). The magnitude of the binding energy is shown with blue indicating the most and red indicating the least favorable binding energy. (b) Detail of the entrance to the binding funnel in a 3D representation using the color map from a. and plotting the magnitude of the binding energy in the vertical axis.

Figure 3. Electrostatic forces between tubulin and the MTBD.

(a) Electrostatic field lines around and between the MTBD and tubulin dimer calculated with the MTBD separated from tubulin by 20 Å. Proteins are shown as space filling, and the red color indicates negative polarity and the blue color indicates positive polarity. The white arrows, F_α and F_β, indicate the total electrostatic forces between the MTBD and the α- and β-tubulin, respectively. (b) Diagram of the distance between the MTBD (blue) and tubulin (orange and yellow), which is varied from 4 to 20 Å with respect to original crystallographic position of MTBD on microtubule. (c) The magnitude of the electrostatic forces between α-tubulin and the MTBD (circles, red) and β-tubulin and the MTBD (triangles, blue) as a function of the distance between the MTBD the tubulin.

Figure 4. The relationship between electrostatic binding energy and velocity and run length for both high-and low-affinity configurations.

In each panel, the mean calculated electrostatic binding energy is plotted as a function of the mean velocity and run length of the respective mutant (five red circles; n = 50 representative structures; error bars represent the standard error of mean; blue dash line is the least square regression linear fit to the five red circles). Eight additional charged residues were mutated in silico to Ala (Methods), and their corresponding binding energies were calculated and plotted to predict the mean velocity and run length of the respective mutant (black triangles; error bars represent the standard error of mean).

Figure 5. HingeDetector identified E3278 and R3411 as the hinge points in the coiled coil stalk in mouse cytoplasmic dynein.

The deviation of angle is the standard deviation of the segment-angles as calculated for all structures in the 5 ns of simulations calculated. The first 10 and last 10 residues of the structure cannot be taken into account, because the segment length was selected to be 10 residues long.

Figure 6. Illustration of the interplay between ATP-hydrolysis induced conformational changes and role of electrostatic forces in dynein’s stepping.

For simplicity, this figure only shows two states of a dynein monomer during the stepping process. In the stepping process, electrostatic forces play important role in the final alignment of the MTBD binding as it docks to the correct binding position.

Figure 7. Structures and sampling strategy used in MSSP computations.

(a) Structure of the tubulin-MTBD complex, consisting of an α-tubulin (orange), a β-tubulin (yellow), and either a low-affinity MTBD (blue) or a high-affinity MTBD (pink). (b) Cross-sectional view of the microtubule structure. (c) The side view of the microtubule structure with MTBDs on the top. (d) Multiple MTBD positions (various colors) with respect to the microtubule. For clarity, only a fraction of the generated positions is shown. The blue arrows indicate that the MTBD position was varied within a ± 20 Å window along the microtubule, up to 20 Å above the microtubule surface, and within a ± 12 degree window around to the microtubule.

Figure 8. Illustration of HingeDetector algorithm.

HingeDetector generates two adjacent structural segments of length 10 residues defined by residues i-10, i, and i-10 (red). It slides these structural segments over the protein (blue) in one residue steps and calculates the segment angle between the vectors (green) formed by the structural segments. The residue, i, with the largest standard deviation in segment angle calculated over all snap-shots is identified as a hinge point.