bioRxivBiophysics
Structural basis for polarized elongation of actin filaments
V. Zsolnay, H. H. Katkar, S. Z. Chou, T. D. Pollard, G. A. Voth
Abstract
Actin filaments elongate and shorten much faster at their barbed end than their pointed end, but the molecular basis of this difference has not been understood. We use all-atom molecular dynamics simulations to investigate the properties of subunits at both ends of the filament. The terminal subunits tend towards conformations that resemble actin monomers in solution, while contacts with neighboring subunits progressively flatten the conformation of internal subunits. At the barbed end the terminal subunit is loosely tethered by its DNase-1 loop to the third subunit, because its monomer-like conformation precludes stabilizing contacts with the penultimate subunit. The motions of the terminal subunit make the partially flattened penultimate subunit accessible for binding monomers. At the pointed end, unique contacts between the penultimate and terminal subunits are consistent with existing cryo-EM maps, limit binding to incoming monomers, and flatten the terminal subunit, which likely promotes ATP hydrolysis and rapid phosphate release. These structures explain the distinct polymerization kinetics of the two ends. Significance StatementEukaryotic cells utilize actin filaments to move, change shape, divide, and transport cargo. Decades of experiments have established that actin filaments elongate and shorten significantly faster from one end than the other, but the underlying mechanism for this asymmetry has not been explained. We used molecular dynamics simulations to investigate the structures of the actin filament ends in the ATP, ADP plus{gamma} -phosphate, and ADP nucleotide states. We characterize the structures of actin subunits at both ends of the filament, explain the mechanisms leading to these differences, and connect the divergent structural properties of the two ends to their distinct polymerization rate constants.
- assay: structural biology
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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