Simulation of the Positive Inotropic Peptide S100A1ct in Aqueous Environment by Gaussian Accelerated Molecular Dynamics.

The S100A1ct peptide, consisting of the C-terminal 20 residues of the S100A1 protein fused to an N-terminal 6-residue hydrophilic tag, has been found to exert a positive inotropic effect, resulting in improved contractile performance of failing cardiac and skeletal muscle without arrhythmic side-effects. The S100A1ct peptide thus has high potential for the treatment of acute heart failure. As a step toward understanding its molecular mechanism of action, and to provide a basis for peptidomimetic design to optimize its properties, we here describe de novo structure predictions and molecular dynamics simulations to characterize the conformational landscape of S100A1ct in aqueous environment. In S100A1, the C-terminal 20 residues form an α-helix, but de novo peptide structure predictions indicate that other conformations are also possible. Conventional molecular dynamics simulations in implicit and explicit solvent corroborated this finding. To ensure adequate sampling, we performed simulations of a tagged 10-residue segment of S100A1ct, and we carried out Gaussian accelerated molecular dynamics simulations of the peptides. These simulations showed that although the helical conformation of S100A1ct was the most energetically stable, the peptide can adopt a range of kinked conformations, suggesting that its activity may be related to its ability to act as a conformational switch.

Glutarate Hydroxylation by the Carbon Starvation-Induced Protein D: A Computational Study into the Stereo- and Regioselectivities of the Reaction.

The carbon starvation-induced protein D (CsiD) is a recently characterized iron(II)/α-ketoglutarate-dependent oxygenase that activates a glutarate molecule as substrate at the C2 position to exclusively produce (S)-2-hydroxyglutarate products. This selective hydroxylation reaction by CsiD is an important component of the lysine biodegradation pathway in Escherichia coli; however, little is known on the details and the origin of the selectivity of the reaction. So far, experimental studies failed to trap and characterize any short-lived catalytic cycle intermediates. As no computational studies have been reported on this enzyme either, we decided to investigate the chemical reaction mechanism of glutarate activation by an iron(IV)-oxo model of the CsiD enzyme. In this work, we present a density functional theory study on a large active site cluster model of CsiD and investigate the glutarate hydroxylation pathways by a high-valent iron(IV)-oxo species leading to (S)-2-hydroxyglutarate, (R)-2-hydroxyglutarate, and 3-hydroxyglutarate. In agreement with experimental observation, the favorable product channel leads to pro-S C2-H hydrogen atom abstraction to form (S)-2-hydroxyglutarate. The reaction is stepwise with a hydrogen atom abstraction by an iron(IV)-oxo species followed by OH rebound from a radical intermediate. The work presented in this paper shows that despite the fact that the C-H bond strengths at the C2 and C3 positions of glutarate are similar in the gas phase, substrate binding and positioning guide the reaction to an enantioselective reaction process by destabilizing the hydrogen atom abstraction pathways for the pro-R C2-H and C3-H positions. Our studies predict the chemical properties of the iron(IV)-oxo species and its rate constants with glutarate and deuterated-glutarate. Moreover, the work shows little protein motions during the catalytic process, while the substrate entrance into the substrate binding pocket appears to be guided by three active site arginine residues that position the substrate for pro-S C2-H hydrogen atom abstraction. Finally, the calculations show that irrespective of the position of the substrate and what C-H bond is closest to the metal center, the lowest energy pathway is for a selective pro-S C2-H hydrogen atom abstraction.

Druggability Assessment in TRAPP Using Machine Learning Approaches.

Accurate protein druggability predictions are important for the selection of drug targets in the early stages of drug discovery. Because of the flexible nature of proteins, the druggability of a binding pocket may vary due to conformational changes. We have therefore developed two statistical models, a logistic regression model (TRAPP-LR) and a convolutional neural network model (TRAPP-CNN), for predicting druggability and how it varies with changes in the spatial and physicochemical properties of a binding pocket. These models are integrated into TRAnsient Pockets in Proteins (TRAPP), a tool for the analysis of binding pocket variations along a protein motion trajectory. The models, which were trained on publicly available and self-augmented datasets, show equivalent or superior performance to existing methods on test sets of protein crystal structures and have sufficient sensitivity to identify potentially druggable protein conformations in trajectories from molecular dynamics simulations. Visualization of the evidence for the decisions of the models in TRAPP facilitates identification of the factors affecting the druggability of protein binding pockets.