TY - JOUR
T1 - Parallel path mechanisms lead to nonmonotonic force-velocity curves and an optimum load for molecular motor function
AU - Mallimadugula, Upasana L.
AU - Galburt, Eric A.
N1 - Funding Information:
This work was supported by the Biochemistry and Molecular Biophysics department (E.A.G.) and the Biochemistry, Biophysics, and Structural Biology program within the Division of Biology and Biomedical Sciences graduate program (U.L.M.), both at the Washington University School of Medicine. We thank Eric Tomko for discussions that motivated the work and Greg Bowman for additional financial support (U.L.M.) during the project.
Publisher Copyright:
© 2022 American Physical Society.
PY - 2022/3
Y1 - 2022/3
N2 - Molecular motors convert chemical potential energy into mechanical work and perform a great number of critical biological functions. Examples include the polymerization and manipulation of nucleic acids, the generation of cellular motility and contractility, the formation and maintenance of cell shape, and the transport of materials within cells. The mechanisms underlying these molecular machines are varied, but are almost always considered in the context of a single kinetic pathway that describes motor stepping. However, the multidimensional nature of protein energy landscapes suggests the possibility of multiple reaction pathways connecting two states. Here we investigate the properties of a hypothetical molecular motor able to utilize parallel translocation mechanisms. We explore motor velocity and force dependence as a function of the energy landscape of each path and reveal the potential for such a mechanism to result in negative differential conductance. More specifically, regimes exist where increasing opposing force leads to increased velocity and an optimum load for motor function. We explore how the presence of this optimum depends on the rates of the individual paths and show that the distribution of stepping times characterized by the randomness parameter may be used to test for parallel path mechanisms. Last, we caution that experimental data consisting solely of measurements of velocity as a function of ATP concentration and force cannot be used to eliminate the possibility of such a parallel path mechanism.
AB - Molecular motors convert chemical potential energy into mechanical work and perform a great number of critical biological functions. Examples include the polymerization and manipulation of nucleic acids, the generation of cellular motility and contractility, the formation and maintenance of cell shape, and the transport of materials within cells. The mechanisms underlying these molecular machines are varied, but are almost always considered in the context of a single kinetic pathway that describes motor stepping. However, the multidimensional nature of protein energy landscapes suggests the possibility of multiple reaction pathways connecting two states. Here we investigate the properties of a hypothetical molecular motor able to utilize parallel translocation mechanisms. We explore motor velocity and force dependence as a function of the energy landscape of each path and reveal the potential for such a mechanism to result in negative differential conductance. More specifically, regimes exist where increasing opposing force leads to increased velocity and an optimum load for motor function. We explore how the presence of this optimum depends on the rates of the individual paths and show that the distribution of stepping times characterized by the randomness parameter may be used to test for parallel path mechanisms. Last, we caution that experimental data consisting solely of measurements of velocity as a function of ATP concentration and force cannot be used to eliminate the possibility of such a parallel path mechanism.
UR - http://www.scopus.com/inward/record.url?scp=85126647930&partnerID=8YFLogxK
U2 - 10.1103/PhysRevE.105.034405
DO - 10.1103/PhysRevE.105.034405
M3 - Article
C2 - 35428051
AN - SCOPUS:85126647930
SN - 2470-0045
VL - 105
JO - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
JF - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
IS - 3
M1 - 034405
ER -