Directing Stem Cell Commitment by Amorphous Calcium Phosphate Nanoparticles Incorporated in PLGA: Relevance of the Free Calcium Ion Concentration.

The microenvironment of mesenchymal stem cells (MSCs) is responsible for the modulation in MSC commitment. Nanocomposites with an inorganic and an organic component have been investigated, and osteogenesis of MSCs has been attributed to inorganic phases such as calcium phosphate under several conditions. Here, electrospun meshes and two-dimensional films of poly(lactic-co-glycolic acid) (PLGA) or nanocomposites of PLGA and amorphous calcium phosphate nanoparticles (PLGA/aCaP) seeded with human adipose-derived stem cells (ASCs) were analyzed for the expression of selected marker genes. In a two-week in vitro experiment, osteogenic commitment was not found to be favored on PLGA/aCaP compared to pure PLGA. Analysis of the medium revealed a significant reduction of the Ca2+ concentration when incubated with PLGA/aCaP, caused by chemical precipitation of hydroxyapatite (HAp) on aCaP seeds of PLGA/aCaP. Upon offering a constant Ca2+ concentration, however, the previously observed anti-osteogenic effect was reversed: alkaline phosphatase, an early osteogenic marker gene, was upregulated on PLGA/aCaP compared to pristine PLGA. Hence, in addition to the cell-material interaction, the material-medium interaction was also important for the stem cell commitment here, affecting the cell-medium interaction. Complex in vitro models should therefore consider all factors, as coupled impacts might emerge.

Comparison of the Performance of Machine Learning Models in Representing High-Dimensional Free Energy Surfaces and Generating Observables.

Free energy surfaces of chemical and physical systems are often generated using a popular class of enhanced sampling methods that target a set of collective variables (CVs) chosen to distinguish the characteristic features of these surfaces. While some of these approaches are typically limited to low (∼1-3)-dimensional CV subspaces, methods such as driven adiabatic free-energy dynamics/temperature-accelerated molecular dynamics have been shown to be capable of generating free energy surfaces of quite high dimension by sampling the associated marginal probability distribution via full sweeps over the CV landscape. These approaches repeatedly visit conformational basins, producing a scattering of points within the basins on each visit. Consequently, they are particularly amenable to synergistic combination with regression machine learning methods for filling in the surfaces between the sampled points and for providing a compact and continuous (or semicontinuous) representation of the surfaces that can be easily stored and used for further computation of observable properties. Given the central role of machine learning techniques in this combined approach, it is timely to provide a detailed comparison of the performance of different machine learning strategies and models, including neural networks, kernel ridge regression, support vector machines, and weighted neighbor schemes, for their ability to learn these high-dimensional surfaces as a function of the amount of sampled training data and, once trained, to subsequently generate accurate ensemble averages corresponding to observable properties of the systems. In this article, we perform such a comparison on a set of oligopeptides, in both gas and aqueous phases, corresponding to CV spaces of 2-10 dimensions and assess their ability to provide a global representation of the free energy surfaces and to generate accurate ensemble averages.

Neural-Network-Based Path Collective Variables for Enhanced Sampling of Phase Transformations.

The investigation of the microscopic processes underlying structural phase transformations in solids is extremely challenging for both simulation and experiment. Atomistic simulations of solid-solid phase transitions require extensive sampling of the corresponding high-dimensional and often rugged energy landscape. Here, we propose a rigorous construction of a 1D path collective variable that is used in combination with enhanced sampling techniques for efficient exploration of the transformation mechanisms. The path collective variable is defined in a space spanned by global classifiers that are derived from local structural units. A reliable identification of the local structural environments is achieved by employing a neural-network-based classification scheme. The proposed path collective variable is generally applicable and enables the investigation of both transformation mechanisms and kinetics.

Endpoint-restricted adiabatic free energy dynamics approach for the exploration of biomolecular conformational equilibria.

A method for calculating the free energy difference between two structurally defined conformational states of a chemical system is developed. A path is defined using a previously reported collective variable that interpolates between two or more conformations, and a restraint is introduced in order to keep the system close to the path. The evolution of the system along the path, which typically presents a high free energy barrier, is generated using enhanced sampling schemes. Although the formulation of the method in terms of a path is quite general, an important advance in this work is the demonstration that prior knowledge of the path is, in fact, not needed and that the free energy difference can be obtained using a simplified definition of the path collective variable that only involves the endpoints. We first validate this method on cyclohexane isomerization. The method is then tested for an extensive conformational change in a realistic molecular system by calculating the free energy difference between the α-helix and ß-hairpin conformations of deca-alanine in solution. Finally, the method is applied to a biologically relevant system to calculate the free energy difference of an observed and a hypothetical conformation of an antigenic peptide bound to a major histocompatibility complex.

Water dispersible surface-functionalized platinum/carbon nanorattles for size-selective catalysis.

Selective dealloying of metal nanoparticles results in rattle-type hollow carbon nanoshells enclosing platinum nanoparticles, which are able to perform size-selective catalysis. Selective functionalization of the outer graphene-like carbon surface prevents agglomeration and leads to well dispersible nanocatalysts in aqueous solutions. The synthesis starts with the production of nanoparticles with a cobalt-platinum-alloy core surrounded by graphene-like carbon via reducing flame spray synthesis. After surface functionalization, simultaneous pore formation in the shell-wall and dissolution of the cobalt results in platinum encapsulated in hollow carbon nanospheres. Catalytic oxidation of differently sized sugars (glucose and maltoheptaose) reveales size-selective catalytic properties of these platinum nanorattles.

Selective Low-Energy Carbon Dioxide Adsorption Using Monodisperse Nitrogen-Rich Hollow Carbon Submicron Spheres.

Monodisperse, nitrogen-doped hollow carbon spheres of submicron size were synthesized using hexamethoxymethylmelamine as both a carbon and nitrogen source in a short (1 h) microwave-assisted synthesis. After carbonization at 550 °C, porous carbon spheres with a remarkably high nitrogen content of 37.1% were obtained, which consisting mainly of highly basic pyridinic moieties. The synthesized hollow spheres exhibited high selectivity for carbon dioxide (CO2) over nitrogen and oxygen gases, with a capture capacity up to 1.56 mmol CO2 g-1. The low adsorption enthalpy of the synthesized hollow carbon spheres permits good adsorbent regeneration. Evaluation of the feasibility of scaling up shows their potential for large-scale applications.

Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces.

The generation of free energy landscapes corresponding to conformational equilibria in complex molecular systems remains a significant computational challenge. Adding to this challenge is the need to represent, store, and manipulate the often high-dimensional surfaces that result from rare-event sampling approaches employed to compute them. In this Letter, we propose the use of artificial neural networks as a solution to these issues. Using specific examples, we discuss network training using enhanced-sampling methods and the use of the networks in the calculation of ensemble averages.

Powder diffraction and crystal structure prediction identify four new coumarin polymorphs.

Coumarin, a simple, commodity chemical isolated from beans in 1820, has, to date, only yielded one solid state structure. Here, we report a rich polymorphism of coumarin grown from the melt. Four new metastable forms were identified and their crystal structures were solved using a combination of computational crystal structure prediction algorithms and X-ray powder diffraction. With five crystal structures, coumarin has become one of the few rigid molecules showing extensive polymorphism at ambient conditions. We demonstrate the crucial role of advanced electronic structure calculations including many-body dispersion effects for accurate ranking of the stability of coumarin polymorphs and the need to account for anharmonic vibrational contributions to their free energy. As such, coumarin is a model system for studying weak intermolecular interactions, crystallization mechanisms, and kinetic effects.

Protein Reduction and Dialysis-Free Work-Up through Phosphines Immobilized on a Magnetic Support: TCEP-Functionalized Carbon-Coated Cobalt Nanoparticles.

Tris(2-carboxyethyl)phosphine (TCEP) is an often-used reducing agent in biochemistry owing to its selectivity towards disulfide bonds. As TCEP causes undesired consecutive side reactions in various analytical methods (e.g., gel electrophoresis, protein labeling), it is usually removed by means of dialysis or gel filtration. Here, an alternative method of separation is presented, namely the immobilization of TCEP on magnetic nanoparticles. This magnetic reagent provides a simple and rapid approach to remove the reducing agent after successful reduction. A reduction capacity of 70 µmol per gram of particles was achieved by using surface-initiated atom transfer polymerization.

Exploring polymorphism of benzene and naphthalene with free energy based enhanced molecular dynamics.

Prediction and exploration of possible polymorphism in organic crystal compounds are of great importance for industries ranging from organic electronics to pharmaceuticals to high-energy materials. Here we apply our crystal structure prediction procedure and the enhanced molecular dynamics based sampling approach called the Crystal-Adiabatic Free Energy Dynamics (Crystal-AFED) method to benzene and naphthalene. Crystal-AFED allows the free energy landscape of structures to be explored efficiently at any desired temperature and pressure. For each system, we successfully predict the most stable crystal structures at atmospheric pressure and explore the relative Gibbs free energies of predicted polymorphs at high pressures. Using Crystal-AFED sampling, we find that mixed structures, which typically cannot be discovered by standard crystal structure prediction methods, are prevalent in the solid forms of these compounds at high pressure.

Click and release: fluoride cleavable linker for mild bioorthogonal separation.

Herein, we present a water dispersable, magnetic nanoparticle supported "click and release" system. The cleavable linker has been synthesized by using a strain-promoted copper-free "click" reagent to establish the specific link and a fluoride cleavable silane moiety for mild cleavage. Small organic molecules, azide-bearing dyes and functionalized enzymes have been bound to the magnetic particle and released in a bioorthogonal way.

Magnetic superbasic proton sponges are readily removed and permit direct product isolation.

Workup in organic synthesis can be very time-consuming, particularly when using reagents with both a solubility similar to that of the desired products and a tendency not to crystallize. In this respect, reactions involving organic bases would strongly benefit from a tremendously simplified separation process. Therefore, we synthesized a derivative of the superbasic proton sponge 1,8-bis(dimethylamino)naphthalene (DMAN) and covalently linked it to the strongest currently available nanomagnets based on carbon-coated cobalt metal nanoparticles. The immobilized magnetic superbase reagent was tested in Knoevenagel- and Claisen-Schmidt-type condensations and showed conversions of up to 99%. High yields of up to 97% isolated product could be obtained by simple recrystallization without using column chromatography. Recycling the catalyst was simple and fast with an insignificant decrease in catalytic activity.