Understanding the impact of ammonium ion substitutions on heterogeneous ice nucleation.

Understanding the mechanisms underpinning heterogeneous ice nucleation in the presence of ionic inclusions is important for fields such as cryopreservation and for improved models of climate and weather prediction. Feldspar and ammonium are both present in significant quantities in the atmosphere, and experimental evidence has shown that feldspar can nucleate ice from ammonium-containing solutions at temperatures warmer than water alone. In recent work, Whale hypothesised that this increase in nucleation temperature is due to an increase in configurational entropy when an ammonium ion is included in the ice hydrogen bond network (T. F. Whale, J. Chem. Phys., 2022, 156, 144503). In this work, we investigate the impact of the inclusion of an ammonium ion on the hydrogen bond network by direct enumeration of the number of structures found using Rick's algorithm. We also determine the energy of these systems and thus compare the effects of enthalpy and entropy to test Whale's hypothesis. We find that the inclusion of an ammonium ion increases the total number of configurations under conditions consistent with a realistic surface charge. We also find that the enthalpic contribution is dominant in determining the location of the ammonium ion within the structure, although we note that this neglects other practicalities of ice nucleation.

Combining machine learning and molecular simulations to predict the stability of amorphous drugs.

Amorphous drugs represent an intriguing option to bypass the low solubility of many crystalline formulations of pharmaceuticals. The physical stability of the amorphous phase with respect to the crystal is crucial to bring amorphous formulations into the market-however, predicting the timescale involved with the onset of crystallization a priori is a formidably challenging task. Machine learning can help in this context by crafting models capable of predicting the physical stability of any given amorphous drug. In this work, we leverage the outcomes of molecular dynamics simulations to further the state-of-the-art. In particular, we devise, compute, and use "solid state" descriptors that capture the dynamical properties of the amorphous phases, thus complementing the picture offered by the "traditional," "one-molecule" descriptors used in most quantitative structure-activity relationship models. The results in terms of accuracy are very encouraging and demonstrate the added value of using molecular simulations as a tool to enrich the traditional machine learning paradigm for drug design and discovery.

Interplay of multiple clusters and initial interface positioning for forward flux sampling simulations of crystal nucleation.

Forward flux sampling (FFS) is a path sampling technique widely used in computer simulations of crystal nucleation from the melt. In such studies, the order parameter underpinning the progress of the FFS algorithm is often the size of the largest crystalline nucleus. In this work, we investigate the effects of two computational aspects of FFS simulations, using the prototypical Lennard-Jones liquid as our computational test bed. First, we quantify the impact of the positioning of the liquid basin and first interface in the space of the order parameter. In particular, we demonstrate that these choices are key to ensuring the consistency of the FFS results. Second, we focus on the frequently encountered scenario where the population of crystalline nuclei is such that there are multiple clusters of size comparable to the largest one. We demonstrate the contribution of clusters other than the largest cluster to the initial flux; however, we also show that they can be safely ignored for the purposes of converging a full FFS calculation. We also investigate the impact of different clusters merging, a process that appears to be facilitated by substantial spatial correlations-at least at the supercooling considered here. Importantly, all of our results have been obtained as a function of system size, thus contributing to the ongoing discussion on the impact of finite size effects on simulations of crystal nucleation. Overall, this work either provides or justifies several practical guidelines for performing FFS simulations that can also be applied to more complex and/or computationally expensive models.

Generating Protein Folding Trajectories Using Contact-Map-Driven Directed Walks.

Recent advances in machine learning methods have had a significant impact on protein structure prediction, but accurate generation and characterization of protein-folding pathways remains intractable. Here, we demonstrate how protein folding trajectories can be generated using a directed walk strategy operating in the space defined by the residue-level contact-map. This double-ended strategy views protein folding as a series of discrete transitions between connected minima on the potential energy surface. Subsequent reaction-path analysis for each transition enables thermodynamic and kinetic characterization of each protein-folding path. We validate the protein-folding paths generated by our discretized-walk strategy against direct molecular dynamics simulations for a series of model coarse-grained proteins constructed from hydrophobic and polar residues. This comparison demonstrates that ranking discretized paths based on the intermediate energy barriers provides a convenient route to identifying physically sensible folding ensembles. Importantly, by using directed walks in the protein contact-map space, we circumvent several of the traditional challenges associated with protein-folding studies, namely, long time scales required and the choice of a specific order parameter to drive the folding process. As such, our approach offers a useful new route for studying the protein-folding problem.

Understanding the emergence of the boson peak in molecular glasses.

A common feature of glasses is the "boson peak", observed as an excess in the heat capacity over the crystal or as an additional peak in the terahertz vibrational spectrum. The microscopic origins of this peak are not well understood; the emergence of locally ordered structures has been put forward as a possible candidate. Here, we show that depolarised Raman scattering in liquids consisting of highly symmetric molecules can be used to isolate the boson peak, allowing its detailed observation from the liquid into the glass. The boson peak in the vibrational spectrum matches the excess heat capacity. As the boson peak intensifies on cooling, wide-angle x-ray scattering shows the simultaneous appearance of a pre-peak due to molecular clusters consisting of circa 20 molecules. Atomistic molecular dynamics simulations indicate that these are caused by over-coordinated molecules. These findings represent an essential step toward our understanding of the physics of vitrification.

Minimalistic ice recrystallisation inhibitors based on phenylalanine.

Ice recrystallisation inhibition (IRI) is typically associated with ice binding proteins, but polymers and other mimetics are emerging. Here we identify phenylalanine as a minimalistic, yet potent, small-molecule IRI capable of inhibiting ice growth at just 1 mg mL-1. Facial amphiphilicity is shown to be a crucial structural feature, with para-substituents enhancing (hydrophobic) or decreasing (hydrophilic) IRI activity. Both amino and acid groups were found to be essential. Solution-phase self-assembly of Phenylalanine was not observed, but the role of self-assembly at the ice/water interface could not be ruled out as a contributing factor.

The role of structural order in heterogeneous ice nucleation.

The freezing of water into ice is a key process that is still not fully understood. It generally requires an impurity of some description to initiate the heterogeneous nucleation of the ice crystals. The molecular structure, as well as the extent of structural order within the impurity in question, both play an essential role in determining its effectiveness. However, disentangling these two contributions is a challenge for both experiments and simulations. In this work, we have systematically investigated the ice-nucleating ability of the very same compound, cholesterol, from the crystalline (and thus ordered) form to disordered self-assembled monolayers. Leveraging a combination of experiments and simulations, we identify a "sweet spot" in terms of the surface coverage of the monolayers, whereby cholesterol maximises its ability to nucleate ice (which remains inferior to that of crystalline cholesterol) by enhancing the structural order of the interfacial water molecules. These findings have practical implications for the rational design of synthetic ice-nucleating agents.

Lipid bilayers as potential ice nucleating agents.

Cellular damage is a key issue in the context of cryopreservation. Much of this damage is believed to be caused by extracellular ice formation at temperatures well above the homogeneous freezing point of pure water. Hence the question: what initiates ice nucleation during cryopreservation? In this paper, we assess whether cellular membranes could be responsible for facilitating the ice nucleation process, and what characteristics would make them good or bad ice nucleating agents. By means of molecular dynamics simulations, we investigate a number of phospholipids and lipopolysaccharide bilayers at the interface with supercooled liquid water. While these systems certainly appear to act as ice nucleating agents, it is likely that other impurities might also play a role in initiating extracellular ice nucleation. Furthermore, we elucidate the factors which affect a bilayer's ability to act as an ice nucleating agent; these are complex, with specific reference to both chemical and structural factors. These findings represent a first attempt to pinpoint the origin of extracellular ice nucleation, with important implications for the cryopreservation process.

Ice Recrystallization Inhibition by Amino Acids: The Curious Case of Alpha- and Beta-Alanine.

Extremophiles produce macromolecules which inhibit ice recrystallization, but there is increasing interest in discovering and developing small molecules that can modulate ice growth. Realizing their potential requires an understanding of how these molecules function at the atomistic level. Here, we report the discovery that the amino acid l-α-alanine demonstrates ice recrystallization inhibition (IRI) activity, functioning at 100 mM (∼10 mg/mL). We combined experimental assays with molecular simulations to investigate this IRI agent, drawing comparison to ß-alanine, an isomer of l-α-alanine which displays no IRI activity. We found that the difference in the IRI activity of these molecules does not originate from their ice binding affinity, but from their capacity to (not) become overgrown, dictated by the degree of structural (in)compatibility within the growing ice lattice. These findings shed new light on the microscopic mechanisms of small molecule cryoprotectants, particularly in terms of their molecular structure and overgrowth by ice.

Recovering local structure information from high-pressure total scattering experiments.

High pressure is a powerful thermodynamic tool for exploring the structure and the phase behaviour of the crystalline state, and is now widely used in conventional crystallographic measurements. High-pressure local structure measurements using neutron diffraction have, thus far, been limited by the presence of a strongly scattering, perdeuterated, pressure-transmitting medium (PTM), the signal from which contaminates the resulting pair distribution functions (PDFs). Here, a method is reported for subtracting the pairwise correlations of the commonly used 4:1 methanol:ethanol PTM from neutron PDFs obtained under hydro-static compression. The method applies a molecular-dynamics-informed empirical correction and a non-negative matrix factorization algorithm to recover the PDF of the pure sample. Proof of principle is demonstrated, producing corrected high-pressure PDFs of simple crystalline materials, Ni and MgO, and benchmarking these against simulated data from the average structure. Finally, the first local structure determination of α-quartz under hydro-static pressure is presented, extracting compression behaviour of the real-space structure.

The seven deadly sins: When computing crystal nucleation rates, the devil is in the details.

The formation of crystals has proven to be one of the most challenging phase transformations to quantitatively model-let alone to actually understand-be it by means of the latest experimental technique or the full arsenal of enhanced sampling approaches at our disposal. One of the most crucial quantities involved with the crystallization process is the nucleation rate, a single elusive number that is supposed to quantify the average probability for a nucleus of critical size to occur within a certain volume and time span. A substantial amount of effort has been devoted to attempt a connection between the crystal nucleation rates computed by means of atomistic simulations and their experimentally measured counterparts. Sadly, this endeavor almost invariably fails to some extent, with the venerable classical nucleation theory typically blamed as the main culprit. Here, we review some of the recent advances in the field, focusing on a number of perhaps more subtle details that are sometimes overlooked when computing nucleation rates. We believe it is important for the community to be aware of the full impact of aspects, such as finite size effects and slow dynamics, that often introduce inconspicuous and yet non-negligible sources of uncertainty into our simulations. In fact, it is key to obtain robust and reproducible trends to be leveraged so as to shed new light on the kinetics of a process, that of crystal nucleation, which is involved into countless practical applications, from the formulation of pharmaceutical drugs to the manufacturing of nano-electronic devices.

A minimalistic cyclic ice-binding peptide from phage display.

Developing molecules that emulate the properties of naturally occurring ice-binding proteins (IBPs) is a daunting challenge. Rather than relying on the (limited) existing structure-property relationships that have been established for IBPs, here we report the use of phage display for the identification of short peptide mimics of IBPs. To this end, an ice-affinity selection protocol is developed, which enables the selection of a cyclic ice-binding peptide containing just 14 amino acids. Mutational analysis identifies three residues, Asp8, Thr10 and Thr14, which are found to be essential for ice binding. Molecular dynamics simulations reveal that the side chain of Thr10 hydrophobically binds to ice revealing a potential mechanism. To demonstrate the biotechnological potential of this peptide, it is expressed as a fusion ('Ice-Tag') with mCherry and used to purify proteins directly from cell lysate.

Microscopic Kinetics Pathway of Salt Crystallization in Graphene Nanocapillaries.

The fundamental understanding of crystallization, in terms of microscopic kinetic and thermodynamic details, remains a key challenge in the physical sciences. Here, by using in situ graphene liquid cell transmission electron microscopy, we reveal the atomistic mechanism of NaCl crystallization from solutions confined within graphene cells. We find that rock salt NaCl forms with a peculiar hexagonal morphology. We also see the emergence of a transitory graphitelike phase, which may act as an intermediate in a two-step pathway. With the aid of density functional theory calculations, we propose that these observations result from a delicate balance between the substrate-solute interaction and thermodynamics under confinement. Our results highlight the impact of confinement on both the kinetics and thermodynamics of crystallization, offering new insights into heterogeneous crystallization theory and a potential avenue for materials design.

The atomistic details of the ice recrystallisation inhibition activity of PVA.

Understanding the ice recrystallisation inhibition (IRI) activity of antifreeze biomimetics is crucial to the development of the next generation of cryoprotectants. In this work, we bring together molecular dynamics simulations and quantitative experimental measurements to unravel the microscopic origins of the IRI activity of poly(vinyl)alcohol (PVA)-the most potent of biomimetic IRI agents. Contrary to the emerging consensus, we find that PVA does not require a "lattice matching" to ice in order to display IRI activity: instead, it is the effective volume of PVA and its contact area with the ice surface which dictates its IRI strength. We also find that entropic contributions may play a role in the ice-PVA interaction and we demonstrate that small block co-polymers (up to now thought to be IRI-inactive) might display significant IRI potential. This work clarifies the atomistic details of the IRI activity of PVA and provides novel guidelines for the rational design of cryoprotectants.

Combining high-resolution scanning tunnelling microscopy and first-principles simulations to identify halogen bonding.

Scanning tunnelling microscopy (STM) is commonly used to identify on-surface molecular self-assembled structures. However, its limited ability to reveal only the overall shape of molecules and their relative positions is not always enough to fully solve a supramolecular structure. Here, we analyse the assembly of a brominated polycyclic aromatic molecule on Au(111) and demonstrate that standard STM measurements cannot conclusively establish the nature of the intermolecular interactions. By performing high-resolution STM with a CO-functionalised tip, we clearly identify the location of rings and halogen atoms, determining that halogen bonding governs the assemblies. This is supported by density functional theory calculations that predict a stronger interaction energy for halogen rather than hydrogen bonding and by an electron density topology analysis that identifies characteristic features of halogen bonding. A similar approach should be able to solve many complex 2D supramolecular structures, and we predict its increasing use in molecular nanoscience at surfaces.