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Asymmetric protonation of glutamate residues drives a preferred transport pathway in EmrE.

Jianping LiAmpon Sae HerNathaniel J Traaseth
Published in: Proceedings of the National Academy of Sciences of the United States of America (2021)
EmrE is an Escherichia coli multidrug efflux pump and member of the small multidrug resistance (SMR) family that transports drugs as a homodimer by harnessing energy from the proton motive force. SMR family transporters contain a conserved glutamate residue in transmembrane 1 (Glu14 in EmrE) that is required for binding protons and drugs. Yet the mechanism underlying proton-coupled transport by the two glutamate residues in the dimer remains unresolved. Here, we used NMR spectroscopy to determine acid dissociation constants (p K a ) for wild-type EmrE and heterodimers containing one or two Glu14 residues in the dimer. For wild-type EmrE, we measured chemical shifts of the carboxyl side chain of Glu14 using solid-state NMR in lipid bilayers and obtained unambiguous evidence on the existence of asymmetric protonation states. Subsequent measurements of p K a values for heterodimers with a single Glu14 residue showed no significant differences from heterodimers with two Glu14 residues, supporting a model where the two Glu14 residues have independent p K a values and are not electrostatically coupled. These insights support a transport pathway with well-defined protonation states in each monomer of the dimer, including a preferred cytoplasmic-facing state where Glu14 is deprotonated in monomer A and protonated in monomer B under pH conditions in the cytoplasm of E. coli Our findings also lead to a model, hop-free exchange, which proposes how exchangers with conformation-dependent p K a values reduce proton leakage. This model is relevant to the SMR family and transporters comprised of inverted repeat domains.
Keyphrases
  • solid state
  • wild type
  • escherichia coli
  • magnetic resonance
  • molecular dynamics simulations
  • molecularly imprinted
  • electron transfer
  • high resolution
  • machine learning
  • pseudomonas aeruginosa