Molecular simulation is one example where large amounts of high-dimensional (high-d) data are generated. To extract useful information, e.g., about relevant states and important conformational transitions, a form of dimensionality reduction is required. Dimensionality reduction algorithms diଏer in their ability to eଏciently project large amounts of data to an informative low-dimensional (low-d) representation and the way the low and high-d representations are linked. We propose a dimensionality reduction algorithm called EncoderMap that is based on a neural network autoencoder in combination with a nonlinear distance metric. A key advantage of this method is that it establishes a functional link from the high-d to the low-d representation and vice versa. This allows us not only to eଏciently project data points to the low-d representation but also to generate high-d representatives for any point in the low-d map. The potential of the algorithm is demonstrated for molecular simulation data of a small, highly ଏexible peptide as well as for folding simulations of the 20-residue Trp-cage protein. We demonstrate that the algorithm is able to eଏciently project the ensemble of high-d structures to a low-d map where major states can be identiଏed and important conformational transitions are revealed. We also show that molecular conformations can be generated for any point or any connecting line between points on the low-d map. This ability of inverse mapping from the low-d to the high-d representation is particularly relevant for the use in algorithms that enhance the exploration of conformational space or the sampling of transitions between conformational states.

Coarse-grained (CG) simulation models have become very popular tools to study complex molecular systems with great computational efficiency on length and time scales that are inaccessible to simulations at atomistic resolution. In so-called bottom-up coarse-graining strategies, the interactions in the CG model are devised such that an accurate representation of an atomistic sampling of configurational phase space is achieved. This means the coarse-graining methods use the underlying multibody potential of mean force (i.e., free-energy surface) derived from the atomistic simulation as parametrization target. Here, we present a new method where a neural network (NN) is used to extract high-dimensional free energy surfaces (FES) from molecular dynamics (MD) simulation trajectories. These FES are used for simulations on a CG level of resolution. The method is applied to simulating homo-oligo-peptides (oligo-glutamic-acid (oligo-glu) and oligo-aspartic-acid (oligo-asp)) of different lengths. We show that the NN not only is able to correctly describe the free-energy surface for oligomer lengths that it was trained on but also is able to predict the conformational sampling of longer chains.

Aliphatic polyamides with so far inaccessibly low amide contents were prepared by acyclic diene metathesis (ADMET) copolymerization of N-(undec-10-en-1-yl)undec-10-enamide (1) and undeca-1,10-diene (2) applying different Grubbs and Hoveyda−Grubbs type olefin metathesis catalyst precursors, followed by exhaustive postpolymerization hydrogenation to yield saturated copolymers. These polyamides, containing between 1.0 and ca. 50.5 amide groups per 1000 methylene units, fill the gap between polyamides from classical polymerization approaches, like polycondensation of diamines with diacids, and linear polyethylene. With reduced amide concentrations the melting points of polyamides converge toward polyethylene, passing through a distinct melting point minimum observed around 110°C for polyamides with ca. 35 amide groups per 1000 methylene units. The minimum goes in hand with a change in the crystal structure related to the different ratios of intersegment interactions from hydrogen bonding and nonpolar van der Waals forces depending on the amide group content in the crystalline state. Furthermore, the influence of hydrogen bonds between amide and ester groups has been quantified for polyesteramides with various amide/ester ratios, prepared by ADMET copolymerization of N-(undec-10-en-1-yl)undec-10-enamide (1) with undec-10-en-1-yl undec-10-enoate (3) and postpolymerization hydrogenation.

Iron(III) hydrolysis in the presence of chloride ions yields akaganéite, an iron oxyhydroxide mineral with a tunnel structure stabilized by the inclusion of chloride. Yet, the interactions of this anion with the iron oxyhydroxide precursors occurring during the hydrolysis process, as well as its mechanistic role during the formation of a solid phase are debated. Using a potentiometric titration assay in combination with a chloride ion-selective electrode, we have monitored the binding of chloride ions to nascent iron oxyhydroxides. Our results are consistent with earlier studies reporting that chloride ions bind to early occurring iron complexes. In addition, the data suggests that they are displaced with the onset of oxolation. Chloride ions in the akaganéite structure must be considered as remnants from the early stages of precipitation, as they do not influence the basic mechanism, or the kinetics of the hydrolysis reactions. The structure-directing role of chloride is based upon the early stages of the reaction. The presence of chloride in the tunnel-structure of akagenéite is due to a relatively strong binding to the earliest iron oxyhydroxide precursors, whereas it plays a rather passive role during the later stages of precipitation.

Understanding the role of polymers rich in aspartic acid (Asp) and glutamic acid (Glu) is the key to gaining precise control over mineralization processes. Despite their chemical similarity, experiments revealed a surprisingly different influence of Asp and Glu sequences. We conducted molecular dynamics simulations of Asp and Glu peptides in the presence of calcium and chloride ions to elucidate the underlying phenomena. In line with experimental differences, in our simulations, we indeed find strong differences in the way the peptides interact with ions in solution. The investigated Asp pentapeptide tends to pull a lot of ions into its vicinity, and many structures with clusters of calcium and chloride ions on the surface of the peptide can be observed. Under the same conditions, comparatively fewer ions can be found in proximity of the investigated Glu pentapeptide, and the structures are characterized by single calcium ions bound to multiple carboxylate groups. Based on our simulation data, we identified three reasons contributing to these differences, leading to a new level of understanding additive−ion interactions.