Guanidine-II aptamer conformations and ligand binding modes through the lens of molecular simulation.

Regulation of gene expression via riboswitches is a widespread mechanism in bacteria. Here, we investigate ligand binding of a member of the guanidine sensing riboswitch family, the guanidine-II riboswitch (Gd-II). It consists of two stem-loops forming a dimer upon ligand binding. Using extensive molecular dynamics simulations we have identified conformational states corresponding to ligand-bound and unbound states in a monomeric stem-loop of Gd-II and studied the selectivity of this binding. To characterize these states and ligand-dependent conformational changes we applied a combination of dimensionality reduction, clustering, and feature selection methods. In absence of a ligand, the shape of the binding pocket alternates between the conformation observed in presence of guanidinium and a collapsed conformation, which is associated with a deformation of the dimerization interface. Furthermore, the structural features responsible for the ability to discriminate against closely related analogs of guanidine are resolved. Based on these insights, we propose a mechanism that couples ligand binding to aptamer dimerization in the Gd-II system, demonstrating the value of computational methods in the field of nucleic acids research.

DOPA Residues Endow Collagen with Radical Scavenging Capacity.

Here we uncover collagen, the main structural protein of all connective tissues, as a redox-active material. We identify dihydroxyphenylalanine (DOPA) residues, post-translational oxidation products of tyrosine residues, to be common in collagen derived from different connective tissues. We observe that these DOPA residues endow collagen with substantial radical scavenging capacity. When reducing radicals, DOPA residues work as redox relay: they convert to the quinone and generate hydrogen peroxide. In this dual function, DOPA outcompetes its amino acid precursors and ascorbic acid. Our results establish DOPA residues as redox-active side chains of collagens, probably protecting connective tissues against radicals formed under mechanical stress and/or inflammation.

Remote Perfluoroalkyl Substituents are Key to Living Aqueous Ethylene Polymerization.

In various nickel(II) salicylaldiminato ethylene polymerization catalysts, which are a versatile mechanistic probe for substituent effects, longer perfluoroalkyl groups exert a strong effect on catalytic activities and polymer microstructures compared to the trifluoromethyl group. This effect is accounted for by a reduced electron density on the active sites, and is also supported by electrochemical studies. Thus, ß-hydride elimination, the key step of chain transfer and branching pathways, is disfavored while chain-growth rates are enhanced. This enhancement occurs to an extent that enables living polymerizations in aqueous systems to afford ultra-high-molecular-weight polyethylene for various chelating salicylaldimine motifs. These findings are mechanistically instructive as well as practically useful for illustrating the potential of perfluoroalkyl groups in catalyst design.

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins.

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol-1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Collagen breaks at weak sacrificial bonds taming its mechanoradicals.

Collagen is a force-bearing, hierarchical structural protein important to all connective tissue. In tendon collagen, high load even below macroscopic failure level creates mechanoradicals by homolytic bond scission, similar to polymers. The location and type of initial rupture sites critically decide on both the mechanical and chemical impact of these micro-ruptures on the tissue, but are yet to be explored. We here use scale-bridging simulations supported by gel electrophoresis and mass spectrometry to determine breakage points in collagen. We find collagen crosslinks, as opposed to the backbone, to harbor the weakest bonds, with one particular bond in trivalent crosslinks as the most dominant rupture site. We identify this bond as sacrificial, rupturing prior to other bonds while maintaining the material's integrity. Also, collagen's weak bonds funnel ruptures such that the potentially harmful mechanoradicals are readily stabilized. Our results suggest this unique failure mode of collagen to be tailored towards combatting an early onset of macroscopic failure and material ageing.

Bond dissociation energies of X-H bonds in proteins.

Knowledge of reliable X-H bond dissociation energies (X = C, N, O, S) for amino acids in proteins is key for studying the radical chemistry of proteins. X-H bond dissociation energies of model dipeptides were computed using the isodesmic reaction method at the BMK/6-31+G(2df,p) and G4(MP2)-6X levels of theory. The density functional theory values agree well with the composite-level calculations. By this high level of theory, combined with a careful choice of reference compounds and peptide model systems, our work provides a highly valuable data set of bond dissociation energies with unprecedented accuracy and comprehensiveness. It will likely prove useful to predict protein biochemistry involving radicals, e.g., by machine learning.