ABSTRACT
F-actin serves as a track for myosin's motor functions and activates its ATPase activity by several orders of magnitude, enabling actomyosin to produce effective force against load. Although actin activation is a ubiquitous property of all myosin isoforms, the molecular mechanism and physiological role of this activation are unclear. Here we describe a conserved actin-binding region of myosin named the 'activation loop', which interacts with the N-terminal segment of actin. We demonstrate by biochemical, biophysical and in vivo approaches using transgenic Caenorhabditis elegans strains that the interaction between the activation loop and actin accelerates the movement of the relay, stimulating myosin's ATPase activity. This interaction results in efficient force generation, but it is not essential for the unloaded motility. We conclude that the binding of actin to myosin's activation loop specifically increases the ratio of mechanically productive to futile myosin heads, leading to efficient muscle contraction.
Subject(s)
Actins/chemistry , Caenorhabditis elegans/chemistry , Dictyostelium/chemistry , Muscle Contraction , Myosins/chemistry , Actins/metabolism , Animals , Binding Sites , Caenorhabditis elegans/metabolism , Dictyostelium/metabolism , Mice , Models, Molecular , Mutation , Myosins/genetics , Myosins/metabolism , Protein Binding , Protein Interaction Domains and Motifs , Protein Structure, QuaternaryABSTRACT
Fluorescence correlation spectroscopy was used to measure the diffusion behavior of a mixture of DMPC or DMPC/DMPG liposomes with human serum albumin (HSA) and mesoporphyrin (MP), which was used as the fluorescent label for liposomes and HSA as well. For decomposing the fluorescence intensity autocorrelation function (ACF) into components corresponding to a liposome population, HSA and MP, we used a maximum entropy procedure that computes a distribution of diffusion times consistent with the ACF data. We found that a simple parametric non-linear fit with a discrete set of decay components did not converge to a stable parameter set. The distribution calculated with the maximum entropy method was stable and the average size of the particles calculated from the effective diffusion time was in good agreement with the data determined using the discrete-component fit.
