Competition of hydrophobic and Coulombic interactions between nano-sized solutes

The solvation of charged, nanometer-sized spherical solutes in water, and the effective, solvent-induced force between two such solutes are investigated by constant temperature and pressure Molecular Dynamics simulations of model solutes carrying various charge patterns. The results for neutral solutes agree well with earlier findings, and with predictions of simple macroscopic considerations: substantial hydrophobic attraction may be traced back to strong depletion (``drying'') of the solvent between the solutes. This hydrophobic attraction is strongly reduced when the solutes are uniformly charged, and the total force becomes repulsive at sufficiently high charge; there is a significant asymmetry between anionic and cationic solute pairs, the latter experiencing a lesser hydrophobic attraction. The situation becomes more complex when the solutes carry discrete (rather than uniform) charge patterns. Due to antagonistic effects of the resulting hydrophilic and hydrophobic ``patches'' on the solvent molecules, water is once more significantly depleted around the solutes, and the effective interaction reverts to being mainly attractive, despite the direct electrostatic repulsion between solutes. Examination of a highly coarse-grained configurational probability density shows that the relative orientation of the two solutes is very different in explicit solvent, compared to the prediction of the crude implicit solvent representation. The present study strongly suggests that a realistic modeling of the charge distribution on the surface of globular proteins, as well as the molecular treatment of water are essential prerequisites for any reliable study of protein aggregation.Comment: 20 pages, 25 figure

How Water's Properties Are Encoded in Its Molecular Structure and Energies.

How are water's material properties encoded within the structure of the water molecule? This is pertinent to understanding Earth's living systems, its materials, its geochemistry and geophysics, and a broad spectrum of its industrial chemistry. Water has distinctive liquid and solid properties: It is highly cohesive. It has volumetric anomalies-water's solid (ice) floats on its liquid; pressure can melt the solid rather than freezing the liquid; heating can shrink the liquid. It has more solid phases than other materials. Its supercooled liquid has divergent thermodynamic response functions. Its glassy state is neither fragile nor strong. Its component ions-hydroxide and protons-diffuse much faster than other ions. Aqueous solvation of ions or oils entails large entropies and heat capacities. We review how these properties are encoded within water's molecular structure and energies, as understood from theories, simulations, and experiments. Like simpler liquids, water molecules are nearly spherical and interact with each other through van der Waals forces. Unlike simpler liquids, water's orientation-dependent hydrogen bonding leads to open tetrahedral cage-like structuring that contributes to its remarkable volumetric and thermal properties

Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin.

Water is often found to mediate interactions between a ligand and a protein. It can play a significant role in orientating the ligand within a binding pocket and contribute to the free energy of binding. It would thus be extremely useful to be able to accurately predict the position and orientation of water molecules within a binding pocket. Recently, we developed the WaterDock protocol that was able to predict 97% of the water molecules in a test set. However, this approach generated false positives at a rate of over 20% in most cases and whilst this might be acceptable for some applications, in high throughput scenarios this is not desirable. Here we tackle this problem via the inclusion of knowledge regarding the solvation structure of ligand functional groups. We call this new protocol WaterDock2 and demonstrate that this protocol maintains a similar true positive rate to the original implementation but is capable of reducing the false-positive rate by over 50%. To improve the usability of the method, we have also developed a plugin for the popular graphics program PyMOL. The plugin also contains an implementation of the original WaterDock.GAR is supported by the Memorial Sloan Kettering Cancer Center, NIH grant P30 CA008748

Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin.

Water is often found to mediate interactions between a ligand and a protein. It can play a significant role in orientating the ligand within a binding pocket and contribute to the free energy of binding. It would thus be extremely useful to be able to accurately predict the position and orientation of water molecules within a binding pocket. Recently, we developed the WaterDock protocol that was able to predict 97% of the water molecules in a test set. However, this approach generated false positives at a rate of over 20% in most cases and whilst this might be acceptable for some applications, in high throughput scenarios this is not desirable. Here we tackle this problem via the inclusion of knowledge regarding the solvation structure of ligand functional groups. We call this new protocol WaterDock2 and demonstrate that this protocol maintains a similar true positive rate to the original implementation but is capable of reducing the false-positive rate by over 50%. To improve the usability of the method, we have also developed a plugin for the popular graphics program PyMOL. The plugin also contains an implementation of the original WaterDock.GAR is supported by the Memorial Sloan Kettering Cancer Center, NIH grant P30 CA008748

Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity

Atomic hydration potentials using a Monte Carlo Reference State (MCRS) for protein solvation modeling

BACKGROUND: Accurate description of protein interaction with aqueous solvent is crucial for modeling of protein folding, protein-protein interaction, and drug design. Efforts to build a working description of solvation, both by continuous models and by molecular dynamics, yield controversial results. Specifically constructed knowledge-based potentials appear to be promising for accounting for the solvation at the molecular level, yet have not been used for this purpose. RESULTS: We developed original knowledge-based potentials to study protein hydration at the level of atom contacts. The potentials were obtained using a new Monte Carlo reference state (MCRS), which simulates the expected probability density of atom-atom contacts via exhaustive sampling of structure space with random probes. Using the MCRS allowed us to calculate the expected atom contact densities with high resolution over a broad distance range including very short distances. Knowledge-based potentials for hydration of protein atoms of different types were obtained based on frequencies of their contacts at different distances with protein-bound water molecules, in a non-redundant training data base of 1776 proteins with known 3D structures. Protein hydration sites were predicted in a test set of 12 proteins with experimentally determined water locations. The MCRS greatly improves prediction of water locations over existing methods. In addition, the contribution of the energy of macromolecular solvation into total folding free energy was estimated, and tested in fold recognition experiments. The correct folds were preferred over all the misfolded decoys for the majority of proteins from the improved Rosetta decoy set based on the structure hydration energy alone. CONCLUSION: MCRS atomic hydration potentials provide a detailed distance-dependent description of hydropathies of individual protein atoms. This allows placement of water molecules on the surface of proteins and in protein interfaces with much higher precision. The potentials provide a means to estimate the total solvation energy for a protein structure, in many cases achieving a successful fold recognition. Possible applications of atomic hydration potentials to structure verification, protein folding and stability, and protein-protein interactions are discussed

Computational Modeling of (De)-Solvation Effects and Protein Flexibility in Protein-Ligand Binding using Molecular Dynamics Simulations

Water is a crucial participant in virtually all cellular functions. Evidently, water molecules in the binding site contribute significantly to the strength of intermolecular interactions in the aqueous phase by mediating protein-ligand interactions, solvating and de-solvating both ligand and protein upon protein-ligand dissociation and association. Recently many published studies use water distributions in the binding site to retrospectively explain and rationalize unexpected trends in structure-activity relationships (SAR). However, traditional approaches cannot quantitatively predict the thermodynamic properties of water molecules in the binding sites and its associated contribution to the binding free energy of a ligand. We have developed and validated a computational method named WATsite to exploit high-resolution solvation maps and thermodynamic profiles to elucidate the water molecules’ potential contribution to protein-ligand and protein-protein binding. We have also demonstrated the utility of the computational method WATsite to help direct medicinal chemistry efforts by using explicit water de-solvation. In addition, protein conformational change is typically involved in the ligand-binding process which may completely change the position and thermodynamic properties of the water molecules in the binding site before or upon ligand binding. We have shown the interplay between protein flexibility and solvent reorganization, and we provide a quantitative estimation of the influence of protein flexibility on desolvation free energy and, therefore, protein-ligand binding. Different ligands binding to the same target protein can induce different conformational adaptations. In order to apply WATsite to an ensemble of different protein conformations, a more efficient implementation of WATsite based on GPU-acceleration and system truncation has been developed. Lastly, by extending the simulation protocol from pure water to mixed water-organic probes simulations, accurate modeling of halogen atom-protein interactions has been achieved

Studying the role of cooperative hydration in stabilizing folded protein states.

Understanding and modelling protein folding remains a key scientific and engineering challenge. Two key questions in protein folding are (1) why many proteins adopt a folded state and (2) how these proteins transition from the random coil ensemble to a folded state. In this paper we employ molecular dynamics simulations to address the first of these questions. Computational methods are well-placed to address this issue due to their ability to analyze systems at atomic-level resolution. Traditionally, the stability of folded proteins has been ascribed to the balance of two types of intermolecular interactions: hydrogen-bonding interactions and hydrophobic contacts. In this study, we explore a third type of intermolecular interaction: cooperative hydration of protein surface residues. To achieve this, we consider multiple independent simulations of the villin headpiece domain to quantify the contributions of different interactions to the energy of the native and fully extended states. In addition, we consider whether these findings are robust with respect to the protein forcefield, the water model, and the presence of salt. In all cases, we identify many cooperatively hydrated interactions that are transient but energetically favor the native state. Whilst further work on additional protein structures, forcefields, and water models is necessary, these results suggest a role for cooperative hydration in protein folding that should be explored further. Rational design of cooperative hydration on the protein surface could be a viable strategy for increasing protein stability.Work in DJH’s lab was supported by the Medical Research Council (MRC) under grant ML/L007266/1. We thank Arno Proeme for porting the Solvaware package to ARCHER as part of the EPSRC embedded computational science and engineering grant eCSE03-3.This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.jsb.2016.09.00