Coupling of lever arm swing and biased Brownian motion in actomyosin.
An important unresolved problem associated with actomyosin motors is the role of Brownian motion in the process of force generation. On the basis of structural observations of myosins and actins, the widely held lever-arm hypothesis has been proposed, in which proteins are assumed to show sequential structural changes among observed and hypothesized structures to exert mechanical force. An alternative hypothesis, the Brownian motion hypothesis, has been supported by single-molecule experiments and emphasizes more on the roles of fluctuating protein movement. In this study, we address the long-standing controversy between the lever-arm hypothesis and the Brownian motion hypothesis through in silico observations of an actomyosin system. We study a system composed of myosin II and actin filament by calculating free-energy landscapes of actin-myosin interactions using the molecular dynamics method and by simulating transitions among dynamically changing free-energy landscapes using the Monte Carlo method. The results obtained by this combined multi-scale calculation show that myosin with inorganic phosphate (Pi) and ADP weakly binds to actin and that after releasing Pi and ADP, myosin moves along the actin filament toward the strong-binding site by exhibiting the biased Brownian motion, a behavior consistent with the observed single-molecular behavior of myosin. Conformational flexibility of loops at the actin-interface of myosin and the N-terminus of actin subunit is necessary for the distinct bias in the Brownian motion. Both the 5.5-11 nm displacement due to the biased Brownian motion and the 3-5 nm displacement due to lever-arm swing contribute to the net displacement of myosin. The calculated results further suggest that the recovery stroke of the lever arm plays an important role in enhancing the displacement of myosin through multiple cycles of ATP hydrolysis, suggesting a unified movement mechanism for various members of the myosin family.
Department of Computational Science and Engineering, Nagoya University, Nagoya, Japan; Institute for Molecular Science, Okazaki, Japan; Department of Applied Physics, Zhejiang University of Technology, Hangzhou, P. R. China.
Qing-Miao
QM
Nie
Department of Computational Science and Engineering, Nagoya University, Nagoya, Japan.
Akio
A
Togashi
Department of Human Informatics, Aichi Shukutoku University, Aichi, Japan.
Takeshi N
TN
Sasaki
Department of Physics, Waseda University, Ohkubo, Shinjuku-ku, Tokyo, Japan.
Mitsunori
M
Takano
Department of Computational Science and Engineering, Nagoya University, Nagoya, Japan; School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea.
Masaki
M
Sasai
Department of Computational Science and Engineering, Nagoya University, Nagoya, Japan.
Tomoki P
TP
Terada
D000205:Actomyosin / Q000737:chemistry*; D003102:Colloids; D009010:Monte Carlo Method; D011487:Protein Conformation; D055672:Static Electricity
D003102:Colloids; D000205:Actomyosin