Ice formation on kaolinite: Insights from molecular dynamics simulations

The formation of ice affects many aspects of our everyday life as well as important technologies such as cryotherapy and cryopreservation. Foreign substances almost always aid water freezing through heterogeneous ice nucleation, but the molecular details of this process remain largely unknown. In fact, insight into the microscopic mechanism of ice formation on different substrates is difficult to obtain even if state-of-the-art experimental techniques are used. At the same time, atomistic simulations of heterogeneous ice nucleation frequently face extraordinary challenges due to the complexity of the water-substrate interaction and the long time scales that characterize nucleation events. Here, we have investigated several aspects of molecular dynamics simulations of heterogeneous ice nucleation considering as a prototypical ice nucleating material the clay mineral kaolinite, which is of relevance in atmospheric science. We show via seeded molecular dynamics simulations that ice nucleation on the hydroxylated (001) face of kaolinite proceeds exclusively via the formation of the hexagonal ice polytype. The critical nucleus size is two times smaller than that obtained for homogeneous nucleation at the same supercooling. Previous findings suggested that the flexibility of the kaolinite surface can alter the time scale for ice nucleation within molecular dynamics simulations. However, we here demonstrate that equally flexible (or non flexible) kaolinite surfaces can lead to very different outcomes in terms of ice formation, according to whether or not the surface relaxation of the clay is taken into account. We show that very small structural changes upon relaxation dramatically alter the ability of kaolinite to provide a template for the formation of a hexagonal overlayer of water molecules at the water-kaolinite interface, and that this relaxation therefore determines the nucleation ability of this mineral

Computational evaluation of the diffusion mechanisms for C8 aromatics in porous organic cages

The development of adsorption and membrane-based separation technologies toward more energy and cost-efficient processes is a significant engineering problem facing the world today. An example of a process in need of improvement is the separation of C8 aromatics to recover para-xylene, which is the precursor to the widely used monomer terephthalic acid. Molecular simulations were used to investigate whether the separation of C8 aromatics can be carried out by the porous organic cages CC3 and CC13, both of which have been previously used in the fabrication of amorphous thin-film membranes. Metadynamics simulations showed significant differences in the energetic barriers to the diffusion of different C8 aromatics through the porous cages, especially for CC3. These differences imply that meta-xylene and ortho-xylene will take significantly longer to enter or leave the cages. Therefore, it may be possible to use membranes composed of these materials to separate ortho- and meta-xylene from para-xylene by size exclusion. Differences in the C8 aromatics’ diffusion barriers were caused by their different diffusion mechanisms, while the lower selectivity of CC13 was largely down to its more significant pore breathing. These observations will aid the future design of adsorbents and membrane systems with improved separation performance

Analyzing and Driving Cluster Formation in Atomistic Simulations

In this paper a new method for identifying the phases contained in a system composed of atoms/molecules is introduced. The method is rooted in graph theory and combines atom centered symmetry functions, adjacency matrices and clustering algorithms to identify regions of space where the properties of the system constituents can be considered uniform. We show how this method can be used to define collective variables and how these collective variables can be used to enhance the sampling of nucleation events. We then show how this method can be used to analyze simulations of crystal nucleation and growth by using it to analyze simulations of the nucleation of the molecular crystal urea and simulations of nucleation in a semiconducting alloy. The semiconducting alloy example we discuss is particular challenging as multiple nucleation centers are formed. We show, however, that our algorithm is able to detect the grain boundaries in the resulting polycrystal

Estimating the intrinsic dimension of datasets by a minimal neighborhood information

Analyzing large volumes of high-dimensional data is an issue of fundamental importance in data science, molecular simulations and beyond. Several approaches work on the assumption that the important content of a dataset belongs to a manifold whose Intrinsic Dimension (ID) is much lower than the crude large number of coordinates. Such manifold is generally twisted and curved; in addition points on it will be non-uniformly distributed: two factors that make the identification of the ID and its exploitation really hard. Here we propose a new ID estimator using only the distance of the first and the second nearest neighbor of each point in the sample. This extreme minimality enables us to reduce the effects of curvature, of density variation, and the resulting computational cost. The ID estimator is theoretically exact in uniformly distributed datasets, and provides consistent measures in general. When used in combination with block analysis, it allows discriminating the relevant dimensions as a function of the block size. This allows estimating the ID even when the data lie on a manifold perturbed by a high-dimensional noise, a situation often encountered in real world data sets. We demonstrate the usefulness of the approach on molecular simulations and image analysis

Analyzing and Biasing Simulations with PLUMED

This chapter discusses how the PLUMED plugin for molecular dynamics can be used to analyze and bias molecular dynamics trajectories. The chapter begins by introducing the notion of a collective variable and by then explaining how the free energy can be computed as a function of one or more collective variables. A number of practical issues mostly around periodic boundary conditions that arise when these types of calculations are performed using PLUMED are then discussed. Later parts of the chapter discuss how PLUMED can be used to perform enhanced sampling simulations that introduce simulation biases or multiple replicas of the system and Monte Carlo exchanges between these replicas. This section is then followed by a discussion on how free-energy surfaces and associated error bars can be extracted from such simulations by using weighted histogram and block averaging techniques

Using metadynamics to explore complex free-energy landscapes

Metadynamics is an atomistic simulation technique that allows, within the same framework, acceleration of rare events and estimation of the free energy of complex molecular systems. It is based on iteratively \u2018filling\u2019 the potential energy of the system by a sum of Gaussians centred along the trajectory followed by a suitably chosen set of collective variables (CVs), thereby forcing the system to migrate from one minimum to the next. The power of metadynamics is demonstrated by the large number of extensions and variants that have been developed. The first scope of this Technical Review is to present a critical comparison of these variants, discussing their advantages and disadvantages. The effectiveness of metadynamics, and that of the numerous alternative methods, is strongly influenced by the choice of the CVs. If an important variable is neglected, the resulting estimate of the free energy is unreliable, and predicted transition mechanisms may be qualitatively wrong. The second scope of this Technical Review is to discuss how the CVs should be selected, how to verify whether the chosen CVs are sufficient or redundant, and how to iteratively improve the CVs using machine learning approaches

A practical guide to the simultaneous determination of protein structure and dynamics using metainference

Accurate protein structural ensembles can be determined with metainference, a Bayesian inference method that integrates experimental information with prior knowledge of the system and deals with all sources of uncertainty and errors as well as with system heterogeneity. Furthermore, metainference can be implemented using the metadynamics approach, which enables the computational study of complex biological systems requiring extensive conformational sampling. In this chapter, we provide a step-by-step guide to perform and analyse metadynamic metainference simulations using the ISDB module of the open-source PLUMED library, as well as a series of practical tips to avoid common mistakes. Specifically, we will guide the reader in the process of learning how to model the structural ensemble of a small disordered peptide by combining state-of-the-art molecular mechanics force fields with nuclear magnetic resonance data, including chemical shifts, scalar couplings and residual dipolar couplings.Comment: 49 pages, 9 figure

The evolution of multiple active site configurations in a designed enzyme

Developments in computational chemistry, bioinformatics, and laboratory evolution have facilitated the de novo design and catalytic optimization of enzymes. Besides creating useful catalysts, the generation and iterative improvement of designed enzymes can provide valuable insight into the interplay between the many phenomena that have been suggested to contribute to catalysis. In this work, we follow changes in conformational sampling, electrostatic preorganization, and quantum tunneling along the evolutionary trajectory of a designed Kemp eliminase. We observe that in the Kemp Eliminase KE07, instability of the designed active site leads to the emergence of two additional active site configurations. Evolutionary conformational selection then gradually stabilizes the most efficient configuration, leading to an improved enzyme. This work exemplifies the link between conformational plasticity and evolvability and demonstrates that residues remote from the active sites of enzymes play crucial roles in controlling and shaping the active site for efficient catalysis

Homochirality in biomineral suprastructures induced by assembly of single-enantiomer amino acids from a nonracemic mixture

© 2019, The Author(s). Since Pasteur first successfully separated right-handed and left-handed tartrate crystals in 1848, the understanding of how homochirality is achieved from enantiomeric mixtures has long been incomplete. Here, we report on a chirality dominance effect where organized, three-dimensional homochiral suprastructures of the biomineral calcium carbonate (vaterite) can be induced from a mixed nonracemic amino acid system. Right-handed (counterclockwise) homochiral vaterite helicoids are induced when the amino acid l-Asp is in the majority, whereas left-handed (clockwise) homochiral morphology is induced when d-Asp is in the majority. Unexpectedly, the Asp that incorporates into the homochiral vaterite helicoids maintains the same enantiomer ratio as that of the initial growth solution, thus showing chirality transfer without chirality amplification. Changes in the degree of chirality of the vaterite helicoids are postulated to result from the extent of majority enantiomer assembly on the mineral surface. These mechanistic insights potentially have major implications for high-level advanced materials synthesis

Allosteric effects in cyclophilin mutants may be explained by changes in nano-microsecond time scale motions

The relationship between molecular motion and catalysis in enzymes is debated. Here, simulations of cyclophilin A and three catalytically-impaired mutants reveal a nanosecond-scale interconversion between active and inactive conformations, orders of magnitude faster than previously suggested