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Novel Kinetic Intermediates Populated along the Folding Pathway of the Transmembrane β-Barrel OmpA.

Emily J DanoffKaren G Fleming
Published in: Biochemistry (2016)
We examined the folding of the β-barrel membrane protein OmpA from Escherichia coli. Although previous studies identified several intermediate states followed by a concerted translocation mechanism across the bilayer, some aspects of the pathway were still unclear, including the extent of secondary structure formation in the intermediate states and how the mechanism gave rise to multiple exponential phases in the folding kinetics. We addressed these questions by investigating the folding kinetics of the OmpA transmembrane β-barrel domain over a range of bilayer thicknesses, allowing us to observe different regions of the folding pathway. The fastest folding into the thinnest bilayers provided information about the later stages of the process, and the slowest folding into thicker bilayers revealed early kinetic steps. Folding was monitored using sodium dodecyl sulfate-polyacrylamide gel electrophoresis and circular dichroism spectroscopy, which provide complementary information about tertiary and secondary structure formation. We globally fit the folding data to kinetic schemes and found that the same core pathway was followed under all lipid conditions. We propose a multistep folding mechanism for OmpA that includes unstructured surface-adsorbed states converting through a partially inserted state with substantial β-sheet structure to the final natively inserted barrel. Kinetic models show that all steps of the main folding pathway are accelerated by membrane defects that occur as a result of thinning the bilayer or incubation of lipids at the phase transition temperature. In addition to suppressing off-pathway states, β-barrel assembly machinery-catalyzed folding in vivo could accelerate any or all of these main folding steps to ensure efficient outer membrane protein biogenesis in vivo.
Keyphrases
  • single molecule
  • molecular dynamics simulations
  • escherichia coli
  • fatty acid
  • machine learning
  • big data
  • klebsiella pneumoniae