Structural and functional characterization of membrane proteins (MPs) requires extraction from the native lipid bilayer, which generally necessitates the formation of water-soluble MPs structures or complexes suitable for biochemical assays. These structures are often detergent micelles that form protein-detergent complexes (PDCs), due to detergents being straightforward in implementation and providing homogenous yields. However, obtaining physiologically relevant PDCs remains a challenge, because most detergents do not keep the native structure/function of the protein intact, jeopardizing its stability, and disrupting the proton pressure and electric gradient even after reincorporation in artificial lipid membranes. To achieve native-like physiology (i.e. to maintain native protein-associated lipids and structurally relevant ligands or cofactors), it is essential to choose the right extraction parameters. Nevertheless, the field of membrane biochemistry is still missing a holistic, interdependent understanding of these systems, too often neglecting the evident, key role that detergents play. In this review, we aim to obtain a more fundamental understanding of detergent-solubilized membrane proteins by pinpointing the most relevant assay parameters (i.e. the most influential on the biochemical properties) involving MPs.

Gas vesicles, micrometer-scale protein structures that function as bacterial buoyancy providers, encapsulate gas in a highly optimized manner. While their atomic structure has been elucidated through single-particle analysis of cryo-EM images, certain structural and functional details remain uncertain. Its biogenesis - the formation and growth mechanisms - consequently remains elusive. Here we apply automated segmentation methods originating from cell imaging to cryo-EM images of gas vesicles to analyze the positions of gas vesicle features in a context-preserving matter. Subsequent whole gas vesicle processing is able to transform accurate gas vesicle segmentations into high-confidence structural feature location picks and statistics. This enables the formation of a sizeable data set containing 86k whole gas vesicles, improved resolution 2D class averages, and the potential for improved structural modeling. Combining high sample number contextual information enables inference on the dynamical properties of gas vesicle growth. Our findings validate recent atomic structure propositions and lend support to a stochastic monomer insertion growth model.