Nano-particles carried by multiple dynein motors: A Self-Regulating Nano-Machine

by Prof. Rony Granek

Dept. of Biotechnology Engineering and Dept. of Physics (adjunct), Ben-Gurion University Of The Negev
at Biological and soft-matter physics

Thu, 25 Nov 2021, 12:10
ZOOM only - Meeting ID: 817 2330 9223, Passcode: 345123


Native cargos demonstrate efficient intra-cellular active transport. Here we investigate the motion of spherical nanoparticles (NPs) grafted with flexible polymers, each ending with a nuclear localization signal peptide, thereby allowing recruitment of several mammalian cytoplasmic dynein motor proteins. Bead-motility assays (group of Anne Bernheim), incorporating surface adsorbed microtubules (MTs), show several unique motility features, depending on the number of NP-ligated motors. To elucidate how motor-motor coupling influences these behaviors, we simulate a theoretical model that builds on single mammalian dynein properties, generalized to include motor-motor elastic and excluded-volume interactions. We find, both experimentally and by model simulations, that long-time trajectories exhibit both left-handed and right-handed helical motion, in which the plus-end directed and right-handed motions are correlated. Run-times and run-lengths are enhanced, and mean velocities are somewhat suppressed when the number of NP-ligated motors is increased. The number of motors that bind to the MT and participate in the transport is stochastic along trajectories. It is distributed mainly between one to three motors, with the mean growing as the number of NP-ligated motors increases, but not surpassing two. We propose that this self-regulation and stochastic alternations between one, two, and three transporting motors allow our synthetic NP to achieve both persistent motion and obstacle bypassing, which is beneficial for native cargos in the crowded cellular environment. Our study elucidates the important role of motor-motor interactions in multi-motor nano-systems.

Join Zoom Meeting

Meeting ID: 817 2330 9223
Passcode: 345123

Created on 18-11-2021 by Granek, Rony (rgranek)
Updaded on 18-11-2021 by Granek, Rony (rgranek)