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Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability, and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal-mode displacements to generate a dynamic disordered ensemble consistent with NMR-derived dynamics parameters, including transverse R2relaxation rates and Lipari–Szabo order parameters (S2values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Förster resonance energy transfer, and small-angle X-ray scattering.

Some of the most potent antifreeze proteins (AFPs) are approximately rigid helical structures that bind with one side in contact with the ice surface at specific orientations. These AFPs take random orientations in solution; however, most orientations become sterically inaccessible as the AFP approaches the ice surface. The effect of these inaccessible orientations on the rate of adsorption of AFP to ice has never been explored. Here, we present a diffusion-controlled theory of adsorption kinetics that accounts for these orientational restrictions to predict a rate constant for adsorption (kon, in m/s) as a function of the length and width of the AFP molecules. We find that kondecreases with length and diameter of the AFP and is almost proportional to the inverse of the area of the binding surface. We demonstrate that the restricted orientations create an entropic barrier to AFP adsorption, which we compute to be approximately 7 kBT for most AFPs and up to 9 kBT for Maxi, the largest known AFP. We compare the entropic resistance 1/konto resistances for diffusion through boundary layers and across typical distances in the extracellular matrix and find that these entropic and diffusion resistances could become comparable in the small confined spaces of biological environments.

Ice recrystallization inhibitors (IRI) are of critical importance in biology, cryopreservation of cells and organs, and frozen foods. Antifreeze glycoproteins (AFGPs) are the most potent IRI. Their cost and cytotoxicity drive the design of synthetic flexible polymers that mimic their function. Poly(vinyl alcohol) (PVA) is the most potent biomimetic found to date, although it is orders of magnitude less potent than AFGPs. A lack of molecular understanding of the factors that limit the IRI efficiency of PVA and other flexible ice-binding polymers hinders the design of more potent IRI. Here, we use molecular and numerical simulations to elucidate how the degree of polymerization (DP) and functionalization of PVA impact its IRI. Our simulations indicate that the onset of IRI activity of PVA occurs for 15 < DP < 20, in agreement with experiments. We predict that polymers with stronger binding to ice per monomer attain IRI activity at lower DP and identify this as a contributor to the higher IRI potency of AFGPs. The simulations reveal that the limiting step for binding of flexible molecules to ice is not the alignment of the molecule to the surface or the initiation of the binding but the propagation to reach its full binding potential. This distinguishes AFGPs and PVA from rigid antifreeze proteins and, we argue, is responsible for their different scaling of efficiencies with molecular size. We use the analysis of PVA to identify the factors that control the IRI activity of flexible polymers and assess the molecular characteristics that endow AFGPs with their exceptional IRI potency.

Clathrate hydrates can spontaneously form under typical conditions found in oil and gas pipelines. The agglomeration of clathrates into large solid masses plugs the pipelines, posing adverse safety, economic, and environmental threats. Surfactants are customarily used to prevent the aggregation of clathrate particles and their coalescence with water droplets. It is generally assumed that a large contact angle between the surfactant-covered clathrate and water is a key predictor of the antiagglomerant performance of the surfactant. Here we use molecular dynamic simulations to investigate the structure and dynamics of surfactant films at the clathrate–oil interface, and their impact on the contact angle and coalescence between water droplets and hydrate particles. In agreement with the experiments, the simulations predict that surfactant-covered clathrate–oil interfaces are oil wet but super-hydrophobic to water. Although the water contact angle determines the driving force for coalescence, we find that a large contact angle is not sufficient to predict good antiagglomerant performance of a surfactant. We conclude that the length of the surfactant molecules, the density of the interfacial film, and the strength of binding of its molecules to the clathrate surface are the main factors in preventing the coalescence and agglomeration of clathrate particles with water droplets in oil. Our analysis provides a molecular foundation to guide the molecular design of effective clathrate antiagglomerants.

Cold acclimatized organisms produce antifreeze proteins that prevent ice growth and recrystallization at subfreezing conditions. Flatness and rigidity of the ice-binding sites of antifreeze proteins are considered key for their recognition of ice. However, the most potent synthetic ice recrystallization inhibitor (IRI) found to date is poly(vinyl alcohol) (PVA), a fully flexible molecule. The ability to tune the architecture and functionalization of PVA makes it a promising candidate to replace antifreeze proteins in industrial applications ranging from cryopreservation of organs to deicing of turbine blades. However, an understanding of how does PVA recognize ice remains elusive, hampering the design of more effective IRIs. Here we use large-scale molecular simulations to elucidate the mechanism by which PVA recognizes ice. We find that the polymer selectively binds to the prismatic faces of ice through a cooperative zipper mechanism. The binding is driven by hydrogen bonding, facilitated by distance matching between the hydroxyl groups of PVA and water molecules at the ice surface. Strong, cooperative binding to ice results from the different scaling of the free energy gains on binding per monomer and the loss of translational and configurational entropy of the chain. We explain why branching of PVA does not improve its IRI activity and use the new molecular understanding to propose principles for the design of macromolecules that bind efficiently to the basal and prismatic planes of ice, producing hyperactive synthetic antifreeze molecules that could compete with the most effective antifreeze proteins.