Simulations suggest robust microtubule attachment of kinesin and dynein in antagonistic pairs.

Intracellular transport is propelled by kinesin and cytoplasmic dynein motors that carry membrane-bound vesicles and organelles bidirectionally along microtubule tracks. Much is known about these motors at the molecular scale, but many questions remain regarding how kinesin and dynein cooperate and compete during bidirectional cargo transport at the cellular level. The goal of the present study was to use a stochastic stepping model constructed by using published load-dependent properties of kinesin-1 and dynein-dynactin-BicD2 (DDB) to identify specific motor properties that determine the speed, directionality, and transport dynamics of a cargo carried by one kinesin and one dynein motor. Model performance was evaluated by comparing simulations to recently published experiments of kinesin-DDB pairs connected by complementary oligonucleotide linkers. Plotting the instantaneous velocity distributions from kinesin-DDB experiments revealed a single peak centered around zero velocity. In contrast, velocity distributions from simulations displayed a central peak around 100 nm/s, along with two side peaks corresponding to the unloaded kinesin and DDB velocities. We hypothesized that frequent motor detachment events and relatively slow motor reattachment rates resulted in periods in which only one motor is attached. To investigate this hypothesis, we varied specific model parameters and compared the resulting instantaneous velocity distributions, and we confirmed this systematic investigation using a machine learning approach that minimized the residual sum of squares between the experimental and simulation velocity distributions. The experimental data were best recapitulated by a model in which the kinesin and dynein stall forces are matched, motor detachment rates are independent of load, and the kinesin-1 reattachment rate is 50 s-1 . These results provide new insights into motor dynamics during bidirectional transport and put forth hypotheses that can be tested by future experiments.