SUMMARY Cytoplasmic dynein is responsible for many aspects of cellular and subcellular movement. LIS1, NudE, and NudEL are dynein interactors initially implicated in brain developmental disease, but now known to be required in cell migration, nuclear, centrosomal, and microtubule transport, mitosis, and growth cone motility. Identification of a specific role for these proteins in cytoplasmic dynein motor regulation has remained elusive. We find that NudE stably recruits LIS1 to the dynein holoenzyme molecule, where LIS1 interacts with the motor domain during the prepowerstroke state of the dynein crossbridge cycle. NudE abrogates dynein force production, whereas LIS1 alone or with NudE induces a persistent-force dynein state that improves ensemble function of multiple dyneins for transport under high-load conditions. These results likely explain the requirement for LIS1 and NudE in transport of nuclei, centrosomes, chromosomes, the microtubule cytoskeleton, and the particular sensitivity of migrating neurons to reduced LIS1 expression.

Summary Background Intra-cellular transport via processive Kinesin, Dynein and Myosin molecular motors plays an important role in maintaining cell structure and function. In many cases, cargos move distances longer than expected for single motors; there is significant evidence that this increased travel is in part due to multiple motors working together to move the cargos. While we understand single motors experimentally and theoretically, our understanding of multiple motors working together is less developed. Results We theoretically investigate how multiple Kinesin motors function. Our model includes stochastic fluctuations of each motor as it proceeds through its enzymatic cycle. Motors dynamically influence each other and function in the presence of thermal noise and viscosity. We test the theory via comparison with the experimentally observed distribution of step sizes for two motors moving a cargo, and by predicting slightly sub-additive stalling force for two motors relative to one. In the presence of load, our predictions for travel distances and mean velocities are different from the steady-state model: with high motor-motor coupling, we predict a form of strain-gating, where—due to the underlying motor’s dynamics—the motors share load unevenly, leading to increased mean travel distance of the multiple-motor system under load. Surprisingly, we predict that in the presence of small load, two motor cargos move slightly slower than do single-motor cargos. A companion paper confirms this prediction in vivo. Conclusions When only a few motors are active, fluctuations and unequal load sharing between motors can result in significant alterations of ensemble function.

Intracellular transport via the microtubule motors kinesin and dynein plays an important role in maintaining cell structure and function. Often, multiple kinesin or dynein motors move the same cargo. Their collective function depends critically on the single motors' detachment kinetics under load, which we experimentally measure here. This experimental constraint-combined with other experimentally determined parameters-is then incorporated into theoretical stochastic and mean-field models. Comparison of modeling results and in vitro data shows good agreement for the stochastic, but not mean-field, model. Many cargos in vivo move bidirectionally, frequently reversing course. Because both kinesin and dynein are present on the cargos, one popular hypothesis explaining the frequent reversals is that the opposite-polarity motors engage in unregulated stochastic tugs-of-war. Then, the cargos' motion can be explained entirely by the outcome of these opposite-motor competitions. Here, we use fully calibrated stochastic and mean-field models to test the tug-of-war hypothesis. Neither model agrees well with our in vivo data, suggesting that, in addition to inevitable tugs-of-war between opposite motors, there is an additional level of regulation not included in the models. Bidirectional motion of subcellular cargos such as mRNA particles, virus particles, endosomes, and lipid droplets is quite common (1), driven by plus-end kinesin and minus-end dynein. Bidirectional motion emerges when frequent switches occur between travel directions, and travel direction reflects which motor (s) dominates. Cells can regulate the switching frequency to control "net" transport, but the physical mechanism(s) underlying this control remains open. Two mechanisms have been proposed. The first suggests that plus-end and minus-end motors always engage in stochastic unregulated tugs-of-war, and overall cargo motion is explained by the outcomes of these mechanical tugsof-war. This model was proposed theoretically to explain lipiddroplet motion (2) but has been adopted to explain endosome motion (3,4). An alternative model suggests that in addition to competition between opposite-polarity motors, there is a "switch" mechanism or mechanisms that achieve further coordination between the motors. Such regulation may be dynamic (5), static (6), or a combination of the two. The crucial question is this: Can tug-of-war models, which exclusively consider cargos with fixed distributions of motors moving along microtubules unaffected by regulatory pathways, explain the characteristics of motility in vivo? Alternatively, are there significant motility characteristics not captured by tug-of-war models, pointing to a richer transport subsystem with important regulatory contributions?There are two theoretical approaches to modeling collective motor transport. The mean-field approach (Fig. 1A) assumes all engaged motors share load equally (7). The stochastic model (Fig. 1B) simulates individual motors going through their mechanochemical cycle (8), where each mo...

Transport by processive molecular motors plays an important role in many cell biological phenomena. In many cases, motors work together to transport cargos in the cell, so it is important to understand the mechanics of the multiple motors. Based on earlier modeling efforts, here we study effects of nonlinear force–velocity relations and stochastic load sharing on multiple motor transport. We find that when two or three motors transport the cargo, then the nonlinear and stochastic effects compensate so that the mechanical properties of the transport are robust. Similarly, the transport is insensitive to compliance of the cargo-motor links. Furthermore, the rate of movement against moderate loads is not improved by increasing the small number of motors. When the motor number is greater than 4, correlations between the motors become negligible, and the earlier analytical mean-field theory of the multiple motor transport holds. We predict that the effective diffusion of the cargo driven by the multiple motors under load increases by an order of magnitude compared to that for the single motor. Finally, our simulations predict that the stochastic effects are responsible for a significant dispersion of velocities generated by the ‘tug-of-war’ of the multiple opposing motors.

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We develop a unified model that describes both "micro" and "macro" evolutions within a single theoretical framework. The ecosystem is described as a dynamic network; the population dynamics at each node of this network describes the "microevolution" over ecological time scales (i.e., birth, ageing, and natural death of individual organisms), while the appearance of new nodes, the slow changes of the links, and the disappearance of existing nodes accounts for the "macroevolution" over geological time scales (i.e., the origination, evolution, and extinction of species). In contrast to several earlier claims in the literature, we observe strong deviations from power law in the regime of long lifetimes.

Tubulin isotypes are found to play an important role in regulating microtubule dynamics. The isotype composition is also thought to contribute in the development of drug resistance as tubulin isotypes show differential binding affinities for various anti-cancer agents. Tubulin isotypes αβII, αβIII and αβIV show differential binding affinity for colchicine. However, the origin of differential binding affinity is not well understood at the molecular level. Here, we investigate the origin of differential binding affinity of a colchicine analogue N-deacetyl-N-(2-mercaptoacetyl)-colchicine (DAMA-colchicine) for human αβII, αβIII and αβIV isotypes, employing sequence analysis, homology modeling, molecular docking, molecular dynamics simulation and MM-GBSA binding free energy calculations. The sequence analysis study shows that the residue compositions are different in the colchicine binding pocket of αβII and αβIII, whereas no such difference is present in αβIV tubulin isotypes. Further, the molecular docking and molecular dynamics simulations results show that residue differences present at the colchicine binding pocket weaken the bonding interactions and the correct binding of DAMA-colchicine at the interface of αβII and αβIII tubulin isotypes. Post molecular dynamics simulation analysis suggests that these residue variations affect the structure and dynamics of αβII and αβIII tubulin isotypes, which in turn affect the binding of DAMA-colchicine. Further, the binding free-energy calculation shows that αβIV tubulin isotype has the highest binding free-energy and αβIII has the lowest binding free-energy for DAMA-colchicine. The order of binding free-energy for DAMA-colchicine is αβIV ≃ αβII >> αβIII. Thus, our computational approaches provide an insight into the effect of residue variations on differential binding of αβII, αβIII and αβIV tubulin isotypes with DAMA-colchicine and may help to design new analogues with higher binding affinities for tubulin isotypes.

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