Negative interference dominates collective transport of kinesin motors in the absence of load

Literature Information

Publication Date 2009-04-20
DOI 10.1039/B900964G
Impact Factor 3.676
Authors

Arthur R. Rogers, Jonathan W. Driver, Pamela E. Constantinou, D. Kenneth Jamison


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Abstract

The collective function of motor proteins is known to be important for the directed transport of many intracellular cargos. However, understanding how multiple motors function as a group remains challenging and requires new methods that enable determination of both the exact number of motors participating in motility and their organization on subcellular cargos. Here we present a biosynthetic method that enables exactly two kinesin-1 molecules to be organized on linear scaffolds that separate the motors by a distance of 50 nm. Tracking the motions of these complexes revealed that while two motors produce longer average run lengths than single kinesins, the system effectively behaves as though a single-motor attachment state dominates motility. It is proposed that negative motor interference derived from asynchronous motor stepping and the communication of forces between motors leads to this behavior by promoting the rapid exchange between different microtubule-bound configurations of the assemblies.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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