Unravelling Constant pH Molecular Dynamics in Oligopeptides with Explicit Solvation Model

An accurate description of the protonation state of amino acids is essential to correctly simulate the conformational space and the mechanisms of action of proteins or other biochemical systems. The pH and the electrochemical environments are decisive factors to define the effective pKa of amino acids and, therefore, the protonation state. However, they are poorly considered in Molecular Dynamics (MD) simulations. To deal with this problem, constant pH Molecular Dynamics (cpHMD) methods have been developed in recent decades, demonstrating a great ability to consider the effective pKa of amino acids within complex structures. Nonetheless, there are very few studies that assess the effect of these approaches in the conformational sampling. In a previous work of our research group, we detected strengths and weaknesses of the discrete cpHMD method implemented in AMBER when simulating capped tripeptides in implicit solvent. Now, we progressed this assessment by including explicit solvation in these peptides. To analyze more in depth the scope of the reported limitations, we also carried out simulations of oligopeptides with distinct positions of the titratable amino acids. Our study showed that the explicit solvation model does not improve the previously noted weaknesses and, furthermore, the separation of the titratable amino acids in oligopeptides can minimize them, thus providing guidelines to improve the conformational sampling in the cpHMD simulations.

Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning

Stacking interactions play a crucial role in drug design, as we can find aromatic cores or scaffolds in almost any available small molecule drug. To predict optimal binding geometries and enhance stacking interactions, usually high-level quantum mechanical calculations are performed. These calculations have two major drawbacks: they are very time consuming, and solvation can only be considered using implicit solvation. Therefore, most calculations are performed in vacuum. However, recent studies have revealed a direct correlation between the desolvation penalty, vacuum stacking interactions and binding affinity, making predictions even more difficult. To overcome the drawbacks of quantum mechanical calculations, in this study we use neural networks to perform fast geometry optimizations and molecular dynamics simulations of heteroaromatics stacked with toluene in vacuum and in explicit solvation. We show that the resulting energies in vacuum are in good agreement with high-level quantum mechanical calculations. Furthermore, we show that using explicit solvation substantially influences the favored orientations of heteroaromatic rings thereby emphasizing the necessity to include solvation properties starting from the earliest phases of drug design.

Ensemble Effects of Explicit Solvation on Allylic Oxidation

Umbrella-sampling density functional theory molecular dynamics (DFT-MD) has been employed to study the full catalytic cycle of the allylic oxidation of cyclohexene using a Cu(II) 7-amino-6-((2-hydroxybenzylidene)amino)quinoxalin-2-ol complex in acetonitrile to create cyclohexenone and H$_2$O as products. In comparison to gas-phase DFT, the solvent effect is observed as the rate determining allylic H-atom abstraction step has a free energy barrier of 12.1 $\pm$ 0.2 kcal/mol in solution. During the cycle, the explicit solvation and ensemble sampling of solvent configurations reveals important dehydrogenation and re-hydrogenation steps of the -NH$_2$ group bound to the Cu-site that are essential to catalyst recovery. This work illustrates the importance of ensemble solvent configurational sampling to reveal the breadth of processes that underpin the full catalytic cycle.<br>

First-Principles Density Functional Theory Characterisation of the Adsorption Complexes of H3AsO3 on Cobalt Ferrite (Fe2CoO4) Surfaces

The mobility of arsenic in aqueous systems can be controlled by its adsorption onto the surfaces of iron oxide minerals such as cobalt ferrite (Fe2CoO4). In this work, the adsorption energies, geometries, and vibrational properties of the most common form of As(III), arsenous acid (H3AsO3), onto the low-index (001), (110), and (111) surfaces of Fe2CoO4 have been investigated under dry and aqueous conditions using periodic density functional theory (DFT) calculations. The dry and hydroxylated surfaces of Fe2CoO4 steadily followed an order of increasing surface energy, and thus decreasing stability, of (001) < (111) < (110). Consequently, the favourability of H3AsO3 adsorption increased in the same order, favouring the least stable (110) surface. However, by analysis of the equilibrium crystal morphologies, this surface is unlikely to occur naturally. The surfaces were demonstrated to be further stabilised by the introduction of H2O/OH species, which coordinate the surface cations, providing a closer match to the bulk coordination of the surface species. The adsorption complexes of H3AsO3 on the hydroxylated Fe2CoO4 surfaces with the inclusion of explicit solvation molecules are found to be generally more stable than on the dry surfaces, demonstrating the importance of hydrogen-bonded interactions. Inner-sphere complexes involving bonds between the surface cations and molecular O atoms were strongly favoured over outer-sphere complexes. On the dry surfaces, deprotonated bidentate binuclear configurations were most thermodynamically favoured, whereas monodentate mononuclear configurations were typically more prevalent on the hydroxylated surfaces. Vibrational frequencies were analysed to ascertain the stabilities of the different adsorption complexes and to assign the As-O and O-H stretching modes of the adsorbed arsenic species. Our results highlight the importance of cobalt as a potential adsorbent for arsenic contaminated water treatment.

Ensemble Effects of Explicit Solvation on Allylic Oxidation

<div>Umbrella-sampling density functional theory molecular dynamics (DFT-MD) has been employed to study the full catalytic cycle of the allylic oxidation of cyclohexene</div><div>using a Cu(II) (E)-6-amino-7-((2-hydroxybenzylidene)amino)quinoxalin-2-ol complex in acetonitrile, which creates the desired cyclohexenone and H 2 O as products. In comparison to prior study using gas-phase DFT, a significant solvent effect is observed on the rate determining allylic H-atom abstraction step (which has a free energy barrier of 12.1 ± 0.2 kcal/mol). During the cycle, the explicit solvation and ensemble sampling of solvent configurations reveals an important dehydrogenation and re-hydrogenation step of the -NH 2 ligand that is essential to catalyst recovery. This work illustrates the importance of ensemble solvent configurational sampling to reveal the breadth of processes that underpin the full catalytic cycle.</div>

Ensemble Effects of Explicit Solvation on Allylic Oxidation

<div>Umbrella-sampling density functional theory molecular dynamics (DFT-MD) has been employed to study the full catalytic cycle of the allylic oxidation of cyclohexene</div><div>using a Cu(II) (E)-6-amino-7-((2-hydroxybenzylidene)amino)quinoxalin-2-ol complex in acetonitrile, which creates the desired cyclohexenone and H 2 O as products. In comparison to prior study using gas-phase DFT, a significant solvent effect is observed on the rate determining allylic H-atom abstraction step (which has a free energy barrier of 12.1 ± 0.2 kcal/mol). During the cycle, the explicit solvation and ensemble sampling of solvent configurations reveals an important dehydrogenation and re-hydrogenation step of the -NH 2 ligand that is essential to catalyst recovery. This work illustrates the importance of ensemble solvent configurational sampling to reveal the breadth of processes that underpin the full catalytic cycle.</div>