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Polyethylene glycol (PEG) conjugation provides a protective modification that enhances the pharmacokinetics and solubility of proteins for therapeutic use. A knowledge of the structural ensemble of these PEGylated proteins is necessary to understand the molecular details that contribute to their hydrodynamic and colligative properties. Because of the large size and dynamic flexibility of pharmaceutically important PEGylated proteins, the determination of structure is challenging. In addi- tion, the hydration of these conjugates that contain large polymers is difficult to determine with traditional methods that identify only first shell hydration water, which does not account for the complete hydrodynamic volume of a macromolecule. Here, we demonstrate that structural ensembles, generated by coarse-grained simulations, can be analyzed with HullRad and used to predict sedimentation coefficients and concentration-dependent hydrodynamic and diffusion nonideality coefficients of PEGylated proteins. A knowledge of these concentration-dependent properties enhances the ability to design and analyze new modified protein therapeutics. HullRad accomplishes this analysis by effectively accounting for the complete hydration of a macromolecule, including that of flexible polymers. 

Outer membrane proteins (OMPs) must exist as an unfolded ensemble while interacting with a chaperone network in the periplasm of Gram-negative bacteria. Here, we developed a method to model unfolded OMP (uOMP) conformational ensembles using the experimental properties of two well-studied OMPs. The overall sizes and shapes of the unfolded ensembles in the absence of a denaturant were experimentally defined by measuring the sedimentation coefficient as a function of urea concentration. We used these data to model a full range of unfolded conformations by parameterizing a targeted coarse-grained simulation protocol. The ensemble members were further refined by short molecular dynamics simulations to reflect proper torsion angles. The final conformational ensembles have polymer properties different from unfolded soluble and intrinsically disordered proteins and reveal inherent differences in the unfolded states that necessitate further investigation. Building these uOMP ensembles advances the understanding of OMP biogenesis and provides essential information for interpreting structures of uOMP-chaperone complexes. 

Hydration of biological macromolecules is important for their stability and function. Historically, attempts have been made to describe the degree of macromolecular hydration using a single parameter over a narrow range of values. Here, we describe a method to calculate two types of hydration: surface shell water and entrained water. A consideration of these two types of hydration helps to explain the “hydration problem” in hydrodynamics. The combination of these two types of hydration allows accurate calculation of hydrodynamic volume and related macromolecular properties such as sedimentation and diffusion coefficients, intrinsic viscosities, and the concentration-dependent non-ideality identified with sedimentation velocity experiments. 

The periplasmic chaperone network ensures the biogenesis of bacterial outer membrane proteins (OMPs) and has recently been identified as a promising target for antibiotics. SurA is the most important member of this network, both due to its genetic interaction with the β-barrel assembly machinery complex as well as its ability to prevent unfolded OMP (uOMP) aggregation. Using only binding energy, the mechanism by which SurA carries out these two functions is not well-understood. Here, we use a combination of photo-crosslinking, mass spectrometry, solution scattering, and molecular modeling techniques to elucidate the key structural features that define how SurA solubilizes uOMPs. Our experimental data support a model in which SurA binds uOMPs in a groove formed between the core and P1 domains. This binding event results in a drastic expansion of the rest of the uOMP, which has many biological implications. Using these experimental data as restraints, we adopted an integrative modeling approach to create a sparse ensemble of models of a SurA•uOMP complex. We validated key structural features of the SurA•uOMP ensemble using independent scattering and chemical crosslinking data. Our data suggest that SurA utilizes three distinct binding modes to interact with uOMPs and that more than one SurA can bind a uOMP at a time. This work demonstrates that SurA operates in a distinct fashion compared to other chaperones in the OMP biogenesis network.