How water models influence the interfacial organization of oxysterol epimers: A comparative simulation study using TIP3P and OPC.

Major histocompatibility complex class I (MHC-I), a key element of the acquired immune system, plays essential roles in activating CD8+ T cells by recognizing intracellular antigens derived from pa...

Hydrating water molecules are believed to be an inherent part of the RNA structure and have a considerable impact on RNA conformation. However, the magnitude and mechanism of the interplay between water molecules and the RNA structure are still poorly understood. In principle, such hydration effects can be studied by molecular dynamics (MD) simulations. In our recent MD studies, we observed that the choice of water model has a visible impact on the predicted structure and structural dynamics of RNA and, in particular, has a larger effect than type, parametrization, and concentration of the ions. Furthermore, the water model effect is sequence dependent and modulates the sequence dependence of A-RNA helical parameters. Clearly, the sensitivity of A-RNA structural dynamics to the water model parametrization is a rather spurious effect that complicates MD studies of RNA molecules. These results nevertheless suggest that the sequence dependence of the A-RNA structure, usually attributed to base stacking, might be driven by the structural dynamics of specific hydration. Here, we present a systematic MD study that aimed to (i) clarify the atomistic mechanism of the water model sensitivity and (ii) discover whether and to what extent specific hydration modulates the A-RNA structural variability. We carried out an extended set of MD simulations of canonical A-RNA duplexes with TIP3P, TIP4P/2005, TIP5P, and SPC/E explicit water models and found that different water models provided a different extent of water bridging between 2'-OH groups across the minor groove, which in turn influences their distance and consequently also inclination, roll, and slide parameters. Minor groove hydration is also responsible for the sequence dependence of these helical parameters. Our simulations suggest that TIP5P is not optimal for RNA simulations.

This paper employs a combination of all-atom molecular dynamics (MD) simulations and unsupervised machine learning (ML) for studying the water–water hydrogen bonds (HBs) inside the anionic poly acrylic acid (PAA) brushes modeled using all-atom MD simulations. PAA brush layer with different charge fraction (f), namely, f = 0, 0.25, and 1, is considered. Water–water interactions, both inside and outside the brush layer, are represented through distinct clusters of tupules of variables representing distances associated with the interacting water molecules. While clusters representing the HBs are present for water inside and outside the brushes, several clusters representing the long-range water–water interactions are missing for the water molecules inside the highly charged (f = 1) PAA brushes. More importantly, inside highly charged brushes, the edge of the clusters representing the water–water HBs is progressively shortened as compared to that in the bulk. Both of these results stem from the presence of the PAA brushes imparting the steric effect and the charge effect, or the effect associated with enhanced interactions of water molecules with PE charges and counterions, thereby disrupting the water connectivity. This water-charged-species interaction also increases the water–water HB angle, i.e., makes the water–water HBs less stable inside the highly charged PAA brush layer. The narrowing of the clusters representing the HBs and the alteration of the angle characterizing the HBs confirm that the conditions defining the water–water HBs change inside the PAA brush layer as a function of the charges on the PAA brush layer. Furthermore, we show that the use of the generic definition of HBs, as compared to using our simulation-motivated modified definition of water–water HBs, overpredicts the number of water–water HBs inside the PAA brush layer. Finally, we employ this all-atom-MD-ML framework to quantify the effect of other types of screening counterions (Li+, Ca2+, and Y3+ ions) in determining the water–water interactions and water–water HB properties inside the PAA brush layer. The findings of the present study, confirming the weakening of water–water HBs inside the PAA brush layer, point to the possibility that the water molecules will be more available for hydrating the brush layer and counterions, thereby leading to a more pronounced wetting of the PAA brush layer.

Accuracy of MD solvent models in RNA structure refinement assessed via liquid-crystal NMR and spin relaxation data

The configuration of densely grafted charged polyelectrolyte (PE) brushes is strongly dictated by the properties and behavior of the counterions that screen the PE brush charges and the solvent molecules (typically water) that solvate the brush molecules and these screening counterions. Only recently, efforts have been made to study the PE brushes atomistically, thereby shedding light on the properties of brush-supported ions and water molecules. However, even for such efforts, there are limitations associated with using a generic definition to estimate certain properties of water and ions inside the brush layer. For example, water-water hydrogen bonds (HBs) will behave differently for locations outside and inside the brush layer, given the fact that the densely closely grafted PE brush molecules create a soft nanoconfinement where the water connectivity becomes highly disrupted: therefore, using the same definition to quantify the HBs inside and outside the brush layer will be unwise. In this paper, we address this limitation by employing an unsupervised machine learning (ML) approach to predict the water-water hydrogen bonding inside a cationic PE brush layer modeled using all-atom molecular dynamics (MD) simulations. The ML method, which relies on a clustering approach and uses the equilibrium coordinates of the water molecules (obtained from the all-atom MD simulations) as the input, is capable of identifying the structural modification of water-water HBs (revealed through appropriate clustering of the data) inside the PE brush layer induced soft nanoconfinement. Such capabilities would not have been possible by using a generic definition of the HBs. Our calculations lead to four key findings: (1) the clusters formed inside and outside the brush layer are structurally similar; (2) the margin of the cluster is shorter inside the PE brush layer confirming the possible disruption of the HBs inside the PE brush layer; (3) the average "hydrogen-acceptor-oxygen-donor-oxygen" angle that defines the HB is reduced for the HBs formed inside the brush layer; (4) the use of the generic definition (definition usable for characterizing the HBs in brush-free bulk) leads to an overprediction of the number of HBs formed inside the PE brush layer.

The interactions and structures of the urea-water system are studied by an all-atom molecular dynamics (MD) simulation. The hydrogen-bonding network and the radial distribution functions are adopted in MD simulations. The structures of urea-water mixtures can be classified into different regions from the analysis of the hydrogen-bonding network. The urea molecule shows the certain tendency to the self-aggregate with the mole fraction of urea increasing. Moreover, the results of the MD simulations are also compare with the chemical shifts and viscosities of the urea aqueous solutions, and the statistical results of the average number hydrogen bonds in the MD simulations are in agreement with the experiment data such as chemical shifts of the hydrogen atom and viscosity.

Protein conformational transition from alpha-helices to beta-sheets precedes aggregation of proteins implicated in many diseases, including Alzheimer and prion diseases. Direct characterization of such transitions is often hindered by the complicated nature of the interaction network among amino acids. A recently engineered small protein-like peptide with a simple amino acid composition features a temperature-driven alpha-helix to beta-sheet conformational change. Here we studied the conformational transition of this peptide by molecular dynamics simulations. We observed a critical temperature, below which the peptide folds into an alpha-helical coiled-coil state and above which the peptide misfolds into beta-rich structures with a high propensity to aggregate. The structures adopted by this peptide during low temperature simulations have a backbone root mean square deviation less than 2 A from the crystal structure. At high temperatures, this peptide adopts an amyloid-like structure, which is mainly composed of coiled anti-parallel beta-sheets with the cross-beta-signature of amyloid fibrils. Most strikingly, we observed conformational conversions in which an alpha-helix is converted into a beta-strand by proximate stable beta-sheets with exposed hydrophobic surfaces and unsaturated hydrogen bonds. Our study suggested a possible generic molecular mechanism of the template-mediated aggregation process, originally proposed by Prusiner (Prusiner, S. B. (1998) Proc. Natl. Acad. Sci. U. S. A. 95, 13363-13383) to account for prion infectivity.

Experiments of nanoconfined water between graphene sheets at high pressure suggest that it forms a square ice structure (Algara-Siller et al., Nature, 2015, 519, 443). Molecular dynamics (MD) simulations have been used to attempt to recreate this structure, but there have been discrepancies in the structure formed by the confined water depending on the simulation set-up that was employed and particularly on the choice of water model. Here, using classical molecular dynamics simulations, we have systematically investigated the effect that three different water models (SPC/E, TIP4P/2005 and TIP5P) have on the structure of water confined between two rigid graphene sheets with a 0.9 nm separation. We show that the TIP4P/2005 and the TIP5P water models form a hexagonal AA-stacked structure, whereas the SPC/E model forms a rhombic AB-stacked structure. Our work demonstrates that the formation of these structures is driven by differences in the strength of hydrogen bonds predicted by the three water models, and that the nature of the graphene/water interaction only mildly affects the phase diagram. Considering the available experimental data and first-principle simulations we conclude that, among the models tested, the TIP4P/2005 and TIP5P force fields are for now the most reliable when simulating water under confinement. © 2018 Wiley Periodicals, Inc.

<p>The use of computational simulation to study the dynamics and interactions of macromolecules has become an important tool in the field of biochemistry. A common method to perform these simulations is to use all-atom explicit-solvent molecular dynamics (MD). However, due to the limitations in computational power currently available, this method is not practical for simulating large-scale biomolecular systems on long timescales. An alternative is to perform implicit-solvent Brownian dynamics (BD) simulations using a coarse grained (CG) model that allows for increased computational efficiency. However, if simulations using the CG model are not realistic, then the gain in computational efficiency from using a CG model is not worthwhile.</p> <p>This thesis describes the derivation of a set of bonded and nonbonded CG potential functions for use in implicit-solvent BD simulations of proteins derived from all-atom explicit-solvent MD simulations of amino acids. To determine which force field and water model to use in the MD simulations, Chapter II describes 1 Μs all-atom explicit-solvent MD simulations of glycine, asparagine, phenylalanine, and valine solutions at 50, 100, 200 and 300 mg/ml concentrations performed using eight different force field and water model combinations. To evaluate the accuracy of the force fields at high solute concentrations, the density, viscosity, and dielectric increments of the four amino acids were calculated from the simulations and compared to experimental results. Additionally, the change in the strength of hydrophobic and electrostatic interactions with increasing solute concentration was calculated for each force field and water model combination. As a result of this study, the Amber ff99SB-ILDN force field and TIP4P-Ew explicit-solvent water model were chosen for all subsequent MD simulations. Chapter III describes the derivation of CG bonded potential functions from 1 Μs all-atom explicit-solvent MD simulations of each of the twenty amino acids, including a separate simulation for protonated histidine. The angle and dihedral probability distributions sampled during the MD simulations were used to optimize the bonded potential functions using the iterative Boltzmann inversion (IBI) method. Chapter IV describes the derivation of CG nonbonded potential functions from 1 Μs all-atom explicit-solvent MD simulations of every possible pairing of the amino acids (231 different systems). The radial distribution functions calculated from these MD simulations were used to optimize a set of nonbonded CG potential functions using the IBI method. The optimized set of bonded and nonbonded potential functions, which is termed COFFDROP (COarse-grained Force Field for Dynamic Representation Of Proteins), quantitatively reproduced all of the calculated MD distributions. To determine if COFFDROP would be useful for simulations of bimolecular systems, Chapter V describes the testing of the transferability of the force field. First, COFFDROP was used to simulate concentrated amino acid solutions. The clustering of the solutes in these simulations was directly compared with results from corresponding all-atom explicit-solvent MD simulations and found to be in excellent agreement. Next, BD simulations of 9.2 mM solutions of the small protein villin headpiece were performed. The proteins aggregated during these simulations, which is in agreement with results from MD simulation but in disagreement with experiment. After scaling the strength of COFFDROP's nonbonded potential functions by a factor of 0.8 and rerunning the BD simulations, the amount of aggregation was comparable to experimental observations. Based on these results, COFFDROP is likely to be applicable in CG BD simulations of large, highly concentrated, biomolecular systems.</p>

The temperature-dependent structure, hydrogen bonding and other related dynamic properties of the standard TIP3P and CHARMM-modified TIP3P water models

An Internal Water-Retention Site in the Rhomboid Intramembrane Protease GlpG Ensures Catalytic Efficiency

The solubility and lower critical solution temperature (LCST) behaviour of poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA300) in water were comprehensively investigated by all-atom molecular dynamics (MD) simulations for 5-, 20-, 50- and 75-mer homopolymers. According to various structural and dynamic properties, the water-solubility of POEGMA300 below the LCST is mainly provided by hydrophobic hydration around the side chain carbon atoms, which is achieved by cage-like water formations. The LCST phase transition occurs when these cage-like structures are disrupted by increasing the temperature above the LCST. During this process, significant amounts of water molecules are released and the local water-ordering is reduced. Moreover, the number of hydrogen bonds and hydrogen bond lifetime results indicate that the hydrogen bonding between polymers and water molecules has relatively little effect on the phase transition. Also, the diffusion rates of 50- and 75-mer POEGMA300 decrease with increasing temperature, which may be due to the breakage of cage-like water structures when the polymer exceeds a certain chain length. Our atomistic level findings will enhance the understanding of the LCST phase transition of OEGMA based homopolymers and will be helpful to design homo- and co-polymers of OEGMAs with required properties.

The ability of ligands to displace conserved water molecules in protein binding sites is of significant interest in drug design and is particularly pertinent in the case of glycomimetic drugs. This concept was explored in previous work [ Clarke et al. J. Am. Chem. Soc. 2001 , 123 , 12238 - 12247 and Kadirvelraj et al. J. Am. Chem. Soc. 2008 , 130 , 16933 - 16942 ] for a highly conserved water molecule located in the binding site of the prototypic carbohydrate-binding protein Concanavalin A (Con A). A synthetic ligand was designed with the aim of displacing such water. While the synthetic ligand bound to Con A in an analogous manner to that of the natural ligand, crystallographic analysis demonstrated that it did not displace the conserved water. In order to quantify the affinity of this particular water for the Con A surface, we report here the calculated standard binding free energy for this water in both ligand-bound and free Con A, employing three popular water models: TIP3P, TIP4P, and TIP5P. Although each model was developed to perform well in simulations of bulk-phase water, the computed binding energies for the isolated water molecule displayed a high sensitivity to the model. Both molecular dynamics simulation and free energy results indicate that the choice of water model may greatly influence the characterization of surface water molecules as conserved (TIP5P) or not (TIP3P) in protein binding sites, an observation of considerable significance to rational drug design. Structural and theoretical aspects at the basis of the different behaviors are identified and discussed.

SummaryExperimental observation has led to the commonly held view that native state protein topology is the principle determinant of mechanical strength. However, the PKD domains of polycystin-1 challenge this assumption: they are stronger than predicted from their native structure. Molecular dynamics simulations suggest that force induces rearrangement to an intermediate structure, with nonnative hydrogen bonds, that resists unfolding. Here we test this hypothesis directly by introducing mutations designed to prevent formation of these nonnative interactions. We find that these mutations, which only moderately destabilize the native state, reduce the mechanical stability dramatically. The results demonstrate that nonnative interactions impart significant mechanical stability, necessary for the mechanosensor function of polycystin-1. Remarkably, such nonnative interactions result from force-induced conformational change: the PKD domain is strengthened by the application of force.

We have investigated the effect of solvation and confinement on an artificial photosynthetic material, carotenoid-porphyrin-C(60) molecular triad, by a multiscale approach and an enhanced sampling technique. We have developed a combined approach of quantum chemistry, statistical physics, and all-atomistic molecular dynamics simulation to determine the partial atomic charges of the ground-state triad. To fully explore the free energy landscape of triad, the replica exchange method was applied to enhance the sampling efficiency of the simulations. The confinement effects on the triad were modeled by imposing three sizes of spherocylindrical nanocapsules. The triad is structurally flexible under ambient conditions, and its conformation distribution is manipulated by the choice of water models and confinement. Two types of water models (SPC/E and TIP3P) are used for solvation. When solvated by SPC/E water, whose HOH angle follows an ideal tetrahedron, the structural characteristics of triad is compact in the bulk systems. However, under a certain nanosized confinement that drastically disrupts hydrogen bond networks in solvent, the triad favors an extended configuration. By contrast, the triad solvated by TIP3P water shows a set of U-shaped conformations in the confinement. We have shown that a slight structural difference in the two water models with the same dipole moment can have great distinction in water density, water orientation, and the number of hydrogen bonds in the proximity of a large flexible compound such as the triad. Subsequently, it has direct impact on the position of the triad in a confinement as well as the distribution of conformations at the interface of liquid and solid in a finite-size system.