bioRxivBiophysicsPreprint
Molecular Determinants of Ion Selectivity in Heavy-Metal P-type ATPases
Abstract
P-type ATPases maintain cellular metal homeostasis by coupling ATP hydrolysis to the active translocation of ions across biological membranes. Within this superfamily, P1B -type ATPases play a central role in the transport and detoxification of transition metals such as Cu+ and Ag+. Despite the availability of several crystallographic structures for bacterial ATPases, molecular determinants governing ion selectivity, coordination dynamics, and transport efficiency are not yet fully resolved, particularly for plant homologs, whose conformational landscapes have not been explored in detail at atomistic resolution. In this study, we combine molecular dynamics (MD) simulations with enhanced sampling through metadynamics to characterize the conformational dynamics and ion-binding mechanisms of representative heavy-metal P-type ATPases. By comparing bacterial and plant homologs in phosphorylated and dephosphorylated states, we examine how phosphorylation is associated with changes in structural plasticity, channel accessibility and ion coordination environments. The simulations indicate phosphorylation-dependent rearrangements of the cytosolic domains and transmembrane helices, which may alter access to the metal-binding pathway. Enhanced sampling identifies distinct conformational basins and possible transition pathways along functionally relevant collective variables, allowing characterization of the free-energy landscape associated with ion displacement within the transmembrane region. Together, these results provide an atomistic perspective on the relationship between metal coordination and protein conformational dynamics. Overall, our findings support a model in which phosphorylation state, structural dynamics, and local coordination environments collectively contribute to the differential behavior of metal ions in P1B -type ATPases.
- organism: bacteria (other)
- organism: plant (other)
- assay: protein structure
- research focus: protein structure prediction
- approach: statistical model
- Citations
- 0
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