The Multiple Roles of Waters in Protein Solvation

Multi-order graph attention network for water solubility prediction and interpretation

The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders and merged them by attention mechanism to generate a final graph embedding. MoGAT can provide the atomic-specific importance scores of a molecule that indicate which atoms significantly influence the prediction so that it can interpret the predicted results chemically. It also improves prediction performance because the graph representations of all neighboring orders, which contain diverse range of information, are employed for the final prediction. Through extensive experiments, we demonstrated that MoGAT showed better performance than the state-of-the-art methods, and the predicted results were consistent with well-known chemical knowledge.

Probing the solvation of the α-helix with extended amide III bands in Raman optical activity

Experimental and theoretical Raman optical activity (ROA) study of α-helical peptides and proteins has suggested that the relative intensity of two extended amide III ROA bands at ∼1340 cm^-1 (I band) and ∼1300 cm^-1 (II band) can be used to monitor the permittivity of the surrounding medium of the α-helix. So far, the ROA intensity ratio, I_I/I_II, has been interpreted from two different viewpoints. The first one is in terms of a direct effect of permittivity around the α-helix. The second one is based on a structural equilibrium of two types of α-helical structures, "hydrated" and "unhydrated" ones. In the present study, temperature- and solvent-dependences of I_I/I_II are measured for highly-α-helical peptides and compared to the theoretical spectra while varying the permittivity or the type of α-helical structure. A fragment method with partial optimization in the normal modes is adopted in density functional theory calculations. The main features of the experimental spectra and a trend of the observed I_I/I_II are well reproduced by the simulations, which leads us to a conclusion that the I_I/I_II is dominantly governed by a direct influence of the permittivity of the environment and just accessorily by the equilibrium of the two types of α-helices. The simulations also opposed the conventional assignments of the I and II bands to "hydrated" and "unhydrated" α-helical structures, respectively. In the case of α-helical proteins, solvent exposure of the α-helix may be monitored by the ROA ratio.

Reversible processes in collagen dehydration: A molecular dynamics study

Collagen dehydration is an unavoidable damaging process that causes the lack of fibers' physical properties and it is usually irreversible. However, the identification of low hydration conditions that permit a recovering of initial collagen features after a rehydration treatment is particularly of interest. Monitoring structural changes by means of MD simulations, we investigated the hydration-dehydration-rehydration cycle of two microfibril models built on different fragments of the sequence of rat tail collagen type I. The microfibrils have different hydropathic features, to investigate the influence of amino acid composition on the whole process. We showed that with low hydration at a level corresponding to the first shell, microfibril gains in compactness and tubularity. Crucially, some water molecules remain trapped inside the fibrils, allowing, by rehydrating, a recovery of the initial collagen structural features. Water rearranges in cluster around the protein, and its first layer is more anchored to the surface. However, these changes in distribution and mobility in low hydration conditions get back with rehydration.

Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks

Solvation free energy is a fundamental property that influences various chemical and biological processes, such as reaction rates, protein folding, drug binding, and bioavailability of drugs. In this work, we present a deep learning method based on graph networks to accurately predict solvation free energies of small organic molecules. The proposed model, comprising three phases, namely, message passing, interaction, and prediction, is able to predict solvation free energies in any generic organic solvent with a mean absolute error of 0.16 kcal/mol. In terms of accuracy, the current model outperforms all of the proposed machine learning-based models so far. The atomic interactions predicted in an unsupervised manner are able to explain the trends of free energies consistent with chemical wisdom. Further, the robustness of the machine learning-based model has been tested thoroughly, and its capability to interpret the predictions has been verified with several examples.

Surface Topography Effects of Globular Biomolecules on Hydration Water

Hydration water serves as a microscopic manifestation of structural stability and functions of biomolecules. To develop bio-nanomaterials in applications, it is important to study how the surface topography and heterogeneity of biomolecules result in their diversity of the hydration dynamics and energetics. We here performed molecular dynamics simulations combined with the steered molecular dynamics and umbrella sampling to investigate the dynamics and escape process associated with the free energy change of water molecules close to a globular biomolecule, i.e., hemoglobin (Hb) and G-quadruplex DNA (GDNA). The residence time, power of long-time tail, and dipole relaxation time were found to display drastic changes within the averaged hydration shell of 3.0-5.0 Å. Compared with bulk water, in the inner hydration shell, the water dipole moment displays a slower relaxation process and is more oriented toward GDNA than toward Hb, forming a hedgehog-like structure when it surrounds GDNA. In particular, a spine water structure is observed in the GDNA narrow groove. The water isotope effect not only prolongs the dynamic time scales of libration motion in the inner hydration shell and the dipole relaxation processes in the bulk but also strengthens the DNA spine water structure. The potential of the mean force profile reflects the integrity of the hydration shell structure and enables us to obtain detailed insights into the structures formed by water, such as the caged H-bond network and the edge bridge structures; it also reveals that local hydration shell free energy (LHSFE) depends on H-bond rupture processes and ranges from 0.2 to 4.2 kcal/mol. Our results demonstrate that the surface topography of a biomolecule influences the integrity of the hydration shell structure and LHSFE. Our studies are able to identify various further applications in the areas of microfluid devices and nano-dewetting on bioinspired surfaces.

The geometry of protein hydration

Based on molecular dynamics simulations of four globular proteins in dilute aqueous solution, with three different water models, we examine several, essentially geometrical, aspects of the protein-water interface that remain controversial or incompletely understood. First, we compare different hydration shell definitions, based on spatial or topological proximity criteria. We find that the best method for constructing monolayer shells with nearly complete coverage is to use a 5 Šwater-carbon cutoff and a 4 Šwater-water cutoff. Using this method, we determine a mean interfacial water area of 11.1 Ų which appears to be a universal property of the protein-water interface. We then analyze the local coordination and packing density of water molecules in the hydration shells and in subsets of the first shell. The mean polar water coordination number in the first shell remains within 1% of the bulk-water value, and it is 5% lower in the nonpolar part of the first shell. The local packing density is obtained from additively weighted Voronoi tessellation, arguably the most physically realistic method for allocating space between protein and water. We find that water in all parts of the first hydration shell, including the nonpolar part, is more densely packed than in the bulk, with a shell-averaged density excess of 6% for all four proteins. We suggest reasons why this value differs from previous experimental and computational results, emphasizing the importance of a realistic placement of the protein-water dividing surface and the distinction between spatial correlation and packing density. The protein-induced perturbation of water coordination and packing density is found to be short-ranged, with an exponential decay "length" of 0.6 shells. We also compute the protein partial volume, analyze its decomposition, and argue against the relevance of electrostriction.

Ergodicity breaking of iron displacement in heme proteins

We present a model of the dynamical transition of atomic displacements in proteins. Increased mean-square displacement at higher temperatures is caused by the softening of the force constant for atomic/molecular displacements by electrostatic and van der Waals forces from the protein-water thermal bath. Displacement softening passes through a nonergodic dynamical transition when the relaxation time of the force-force correlation function enters, with increasing temperature, the instrumental observation window. Two crossover temperatures are identified. The lower crossover, presently connected to the glass transition, is related to the dynamical unfreezing of rotations of water molecules within nanodomains polarized by charged surface residues of the protein. The higher crossover temperature, usually assigned to the dynamical transition, marks the onset of water translations. All crossovers are ergodicity breaking transitions depending on the corresponding observation windows. Allowing stretched exponential relaxation of the protein-water thermal bath significantly improves the theory-experiment agreement when applied to solid protein samples studied by Mössbauer spectroscopy.

In Silico Studies of Small Molecule Interactions with Enzymes Reveal Aspects of Catalytic Function

Small molecules, such as solvent, substrate, and cofactor molecules, are key players in enzyme catalysis. Computational methods are powerful tools for exploring the dynamics and thermodynamics of these small molecules as they participate in or contribute to enzymatic processes. In-depth knowledge of how small molecule interactions and dynamics influence protein conformational dynamics and function is critical for progress in the field of enzyme catalysis. Although numerous computational studies have focused on enzyme-substrate complexes to gain insight into catalytic mechanisms, transition states and reaction rates, the dynamics of solvents, substrates, and cofactors are generally less well studied. Also, solvent dynamics within the biomolecular solvation layer play an important part in enzyme catalysis, but a full understanding of its role is hampered by its complexity. Moreover, passive substrate transport has been identified in certain enzymes, and the underlying principles of molecular recognition are an area of active investigation. Enzymes are highly dynamic entities that undergo different conformational changes, which range from side chain rearrangement of a residue to larger-scale conformational dynamics involving domains. These events may happen nearby or far away from the catalytic site, and may occur on different time scales, yet many are related to biological and catalytic function. Computational studies, primarily molecular dynamics (MD) simulations, provide atomistic-level insight and site-specific information on small molecule interactions, and their role in conformational pre-reorganization and dynamics in enzyme catalysis. The review is focused on MD simulation studies of small molecule interactions and dynamics to characterize and comprehend protein dynamics and function in catalyzed reactions. Experimental and theoretical methods available to complement and expand insight from MD simulations are discussed briefly.