Locating missing water molecules in protein cavities by the three-dimensional reference interaction site model theory of molecular solvation

Prediction of hydrophilic and hydrophobic hydration structure of protein by neural network optimized using experimental data

The hydration structures of proteins, which are necessary for their folding, stability, and functions, were visualized using X-ray and neutron crystallography and transmission electron microscopy. However, complete visualization of hydration structures over the entire protein surface remains difficult. To compensate for this incompleteness, we developed a three-dimensional convolutional neural network to predict the probability distribution of hydration water molecules on the hydrophilic and hydrophobic surfaces, and in the cavities of proteins. The neural network was optimized using the distribution patterns of protein atoms around the hydration water molecules identified in the high-resolution X-ray crystal structures. We examined the feasibility of the neural network using water sites in the protein crystal structures that were not included in the datasets. The predicted distribution covered most of the experimentally identified hydration sites, with local maxima appearing in their vicinity. This computational approach will help to highlight the relevance of hydration structures to the biological functions and dynamics of proteins.

Predicting Conserved Water Molecules in Binding Sites of Proteins Using Machine Learning Methods and Combining Features

Water molecules play an important role in many biological processes in terms of stabilizing protein structures, assisting protein folding, and improving binding affinity. It is well known that, due to the impacts of various environmental factors, it is difficult to identify the conserved water molecules (CWMs) from free water molecules (FWMs) directly as CWMs are normally deeply embedded in proteins and form strong hydrogen bonds with surrounding polar groups. To circumvent this difficulty, in this work, the abundance of spatial structure information and physicochemical properties of water molecules in proteins inspires us to adopt machine learning methods for identifying the CWMs. Therefore, in this study, a machine learning framework to identify the CWMs in the binding sites of the proteins was presented. First, by analyzing water molecules' physicochemical properties and spatial structure information, six features (i.e., atom density, hydrophilicity, hydrophobicity, solvent-accessible surface area, temperature B-factors, and mobility) were extracted. Those features were further analyzed and combined to reach a higher CWM identification rate. As a result, an optimal feature combination was determined. Based on this optimal combination, seven different machine learning models (including support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), discriminant analysis (DA), naïve Bayes (NB), and ensemble learning (EL)) were evaluated for their abilities in identifying two categories of water molecules, i.e., CWMs and FWMs. It showed that the EL model was the desired prediction model due to its comprehensive advantages. Furthermore, the presented methodology was validated through a case study of crystal 3skh and extensively compared with Dowser++. The prediction performance showed that the optimal feature combination and the desired EL model in our method could achieve satisfactory prediction accuracy in identifying CWMs from FWMs in the proteins' binding sites.

GalaxyWater-CNN: Prediction of Water Positions on the Protein Structure by a 3D-Convolutional Neural Network

Proteins interact with numerous water molecules to perform their physiological functions in biological organisms. Most water molecules act as solvent media; hence, their roles may be considered implicitly in theoretical treatments of protein structure and function. However, some water molecules interact intimately with proteins and require explicit treatment to understand their effects. Most physics-based computational methods are limited in their ability to accurately locate water molecules on protein surfaces because of inaccurate energy functions. Instead of relying on an energy function, this study attempts to learn the locations of water molecules from structural data. GalaxyWater-convolutional neural network (CNN) predicts water positions on protein chains, protein-protein interfaces, and protein-compound binding sites using a 3D-CNN model that is trained to generate a water score map on a given protein structure. The training data are compiled from high-resolution protein crystal structures resolved together with water molecules. GalaxyWater-CNN shows improved water prediction performance both in the coverage of crystal water molecules and in the accuracy of the predicted water positions when compared with previous energy-based methods. This method shows a superior performance in predicting water molecules that form hydrogen-bond networks precisely. The web service and the source code of this water prediction method are freely available at https://galaxy.seoklab.org/gwcnn and https://github.com/seoklab/GalaxyWater-CNN, respectively.

Water molecules at protein-drug interfaces: computational prediction and analysis methods

The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

GalaxyWater-wKGB: Prediction of Water Positions on Protein Structure Using wKGB Statistical Potential

Proteins fold and function in water, and protein-water interactions play important roles in protein structure and function. In computational studies on protein structure and interaction, the effect of water is considered either implicitly or explicitly. Implicit water models are frequently used in protein structure prediction and docking because they are computationally much more efficient than explicit water models, which are often employed in molecular dynamics (MD) simulations. However, implicit water models that treat water as a continuous solvent medium cannot account for specific atomistic protein-water interactions that are critical for structure formation and interactions with other molecules. Various methods for predicting water molecules that form specific atomistic interactions with proteins have been developed. Methods involving MD simulations or the integral equation theory tend to produce more accurate results at a higher computational cost than simple geometry- or energy-based methods. Here, we present a novel method for predicting water positions on a protein surface called GalaxyWater-wKGB, which is based on a statistical potential, a water knowledge-based potential based on the generalized Born model (wKGB). This method is accurate and rapid because it does not require conformational sampling or iterative computation owing to the effective statistical treatment employed to derive the potential. The statistical potential describes specific protein atom-water interactions more accurately than conventional potentials by considering the dependence on the degree of solvent accessibility of protein atoms as well as on protein atom-water distances and orientations. The introduction of solvent accessibility allows effective consideration of competing nonspecific protein-water and intraprotein interactions. When tested on high-resolution protein crystal structures, this method could recover similar or larger fractions of crystallographic water 180 times faster than the sophisticated integral equation theory, 3D-RISM. A web service of this water prediction method is freely available at http://galaxy.seoklab.org/wkgb.

aquaPELE: A Monte Carlo-Based Algorithm to Sample the Effects of Buried Water Molecules in Proteins

Water is frequently found inside proteins, carrying out important roles in catalytic reactions or molecular recognition tasks. Therefore, computational models that aim to study protein-ligand interactions usually have to include water effects through explicit or implicit approaches to obtain reliable results. While full explicit models might be too computationally daunting for some applications, implicit models are normally faster but omit some of the most important contributions of water. This is the case of our in-house software, called protein energy landscape exploration (PELE), which uses implicit models to speed up conformational explorations as much as possible; the lack of explicit water sampling, however, limits its model. In this work, we confront this problem with the development of aquaPELE. It is a new algorithm that extends the exploration capabilities while keeping efficiency as it employs a mixed implicit/explicit approach to also take into account the effects of buried water molecules. With an additional Monte Carlo (MC) routine, a set of explicit water molecules is perturbed inside protein cavities and their effects are dynamically adjusted to the current state of the system. As a result, this implementation can be used to predict the principal hydration sites or the rearrangement and displacement of conserved water molecules upon the binding of a ligand. We benchmarked this new tool focusing on estimating ligand binding modes and hydration sites in cavities with important interfacial water molecules, according to crystallographic structures. Results suggest that aquaPELE sets a fast and reliable alternative for molecular recognition studies in systems with a strong water-dependency.

Protein Matrix Control of Reaction Center Excitation in Photosystem II

Photosystem II (PSII) is a multisubunit pigment-protein complex that uses light-induced charge separation to power oxygenic photosynthesis. Its reaction center chromophores, where the charge transfer cascade is initiated, are arranged symmetrically along the D1 and D2 core polypeptides and comprise four chlorophyll (PD1, PD2, ChlD1, ChlD2) and two pheophytin molecules (PheoD1 and PheoD2). Evolution favored productive electron transfer only via the D1 branch, with the precise nature of primary excitation and the factors that control asymmetric charge transfer remaining under investigation. Here we present a detailed atomistic description for both. We combine large-scale simulations of membrane-embedded PSII with high-level quantum-mechanics/molecular-mechanics (QM/MM) calculations of individual and coupled reaction center chromophores to describe reaction center excited states. We employ both range-separated time-dependent density functional theory and the recently developed domain based local pair natural orbital (DLPNO) implementation of the similarity transformed equation of motion coupled cluster theory with single and double excitations (STEOM-CCSD), the first coupled cluster QM/MM calculations of the reaction center. We find that the protein matrix is exclusively responsible for both transverse (chlorophylls versus pheophytins) and lateral (D1 versus D2 branch) excitation asymmetry, making ChlD1 the chromophore with the lowest site energy. Multipigment calculations show that the protein matrix renders the ChlD1 → PheoD1 charge-transfer the lowest energy excitation globally within the reaction center, lower than any pigment-centered local excitation. Remarkably, no low-energy charge transfer states are located within the "special pair" PD1-PD2, which is therefore excluded as the site of initial charge separation in PSII. Finally, molecular dynamics simulations suggest that modulation of the electrostatic environment due to protein conformational flexibility enables direct excitation of low-lying charge transfer states by far-red light.

Applications of water molecules for analysis of macromolecule properties

Water molecules maintain proteins' structures, functions, stabilities and dynamics. They can occupy certain positions or pass quickly via a protein's interior. Regardless of their behaviour, water molecules can be used for the analysis of proteins' structural features and biochemical properties. Here, we present a list of several software programs that use the information provided by water molecules to: i) analyse protein structures and provide the optimal positions of water molecules for protein hydration, ii) identify high-occupancy water sites in order to analyse ligand binding modes, and iii) detect and describe tunnels and cavities. The analysis of water molecules' distribution and trajectories sheds a light on proteins' interactions with small molecules, on the dynamics of tunnels and cavities, on protein composition and also on the functionality, transportation network and location of functionally relevant residues. Finally, the correct placement of water molecules in protein crystal structures can significantly improve the reliability of molecular dynamics simulations.

The role of hydration effects in 5-fluorouridine binding to SOD1: insight from a new 3D-RISM-KH based protocol for including structural water in docking simulations

Misfolded Cu/Zn superoxide dismutase enzyme (SOD1) shows prion-like propagation in neuronal cells leading to neurotoxic aggregates that are implicated in amyotrophic lateral sclerosis (ALS). Tryptophan-32 (W32) in SOD1 is part of a potential site for templated conversion of wild type SOD1. This W32 binding site is located on a convex, solvent exposed surface of the SOD1 suggesting that hydration effects can play an important role in ligand recognition and binding. A recent X-ray crystal structure has revealed that 5-Fluorouridine (5-FUrd) binds at the W32 binding site and can act as a pharmacophore scaffold for the development of anti-ALS drugs. In this study, a new protocol is developed to account for structural (non-displaceable) water molecules in docking simulations and successfully applied to predict the correct docked conformation binding modes of 5-FUrd at the W32 binding site. The docked configuration is within 0.58 Å (RMSD) of the observed configuration. The docking protocol involved calculating a hydration structure around SOD1 using molecular theory of solvation (3D-RISM-KH, 3D-Reference Interaction Site Model-Kovalenko-Hirata) whereby, non-displaceable water molecules are identified for docking simulations. This protocol was also used to analyze the hydrated structure of the W32 binding site and to explain the role of solvation in ligand recognition and binding to SOD1. Structural water molecules mediate hydrogen bonds between 5-FUrd and the receptor, and create an environment favoring optimal placement of 5-FUrd in the W32 binding site.

Solvent Composition Effects on the Structural Properties of the Aβ42 Monomer from the 3D-RISM-KH Molecular Theory of Solvation

Structural characterization of amyloid (A)β peptides implicated in Alzheimer's disease is a challenging problem due to their intrinsically disordered nature and their high propensity for aggregation. Only limited information is currently available from experiments on conformational properties and aggregation pathways of the peptides in cellular environments. In silico modeling complements experimental information, providing atomistic insight into structure and dynamics of different Aβ species. All-atom explicit solvent molecular dynamics (MD) simulations with a properly selected force field can deliver reliable structural and dynamic information. In the case of intrinsically disordered Aβ peptides, enhanced sampling simulations beyond the nanosecond time scale are required to obtain statistically meaningful results even for simple solvent conditions. To overcome the challenges of conformational sampling in crowded cellular environments, alternative approaches have to be used, including postprocessing of MD data. In this study, we employ the statistical-mechanical, three-dimensional reference interaction site model with the Kovalenko-Hirata closure integral equation molecular theory of solvation to describe solvent composition effects on the conformational equilibrium in a structural ensemble of the Aβ42 (covering residues 1-42) monomer based on a statistical reweighting technique. The methodology enables a computationally efficient prediction on how different factors in the cellular environment, such as solvent composition, nonpolar solvation, and macromolecular crowding, affect the structural properties of the monomer. Similarities have been identified between changes in the structural ensemble caused by nonpolar solvation and crowded environments modeled by ionic solution with large negative ions. In particular, both solvent conditions reduce the random coil content and enhance the helical structure content of the monomer. In contrast to the previous studies, which reported increased α-helical content of peptides in crowded environments, this work attributes these structural features to the difference in solvent exposure of hydrophilic residues of the monomer for different secondary structure elements, rather than to (entropic) excluded volume effects.

Importance of a crystalline water network in docking-based virtual screening: a case study of BRD4

As a member of the bromodomain and extra terminal domain (BET) protein family, bromodomain-containing protein 4 (BRD4) is an epigenetic reader and can recognize acetylated lysine residues in histones.

Predicting the Binding Mode of 2-Hydroxypropyl-β-cyclodextrin to Cholesterol by Means of the MD Simulation and the 3D-RISM-KH Theory

It has been found that a cyclodextrin derivative, 2-hydroxypropyl-β-cyclodextrin (HPβCD), has reasonable therapeutic effect on Niemann-Pick disease type C, which is caused by abnormal accumulation of unesterified cholesterol and glycolipids in the lysosomes and shortage of esterified cholesterol in other cellular compartments. We study the binding affinity and mode of HPβCD with cholesterol to elucidate the possible mechanism of HPβCD for removing cholesterol from the lysosomes. The dominant binding mode of HPβCD with cholesterol is found based on the molecular dynamics simulation and a statistical mechanics theory of liquids, or the three-dimensional reference interaction site model theory with Kovalenko-Hirata closure relation. We examine the two types of complexes between HPβCD and cholesterol, namely, one-to-one (1:1) and two-to-one (2:1). It is predicted that the 1:1 complex makes two or three types of stable binding mode in solution, in which the βCD ring tends to be located at the edge of the steroid skeleton. For the 2:1 complex, there are four different types of the complex conceivable, depending on the orientation between the two HPβCDs: head-to-head (HH), head-to-tail (HT), tail-to-head (TH), and tail-to-tail (TT). The HT and HH cyclodextrin dimers show higher affinity to cholesterol compared to the other dimers and to all the binding modes of 1:1 complexes. The physical reason why the HT and HH dimers have higher affinity compared to the other complexes is discussed based on the consistency with the 1:1 complex. On the one hand, in case of the HT and HH dimers, the position of each CD in the dimer along the cholesterol chain comes right on or close to one of the positions where a single CD makes a stable complex. On the other hand, one of the CD molecules is located on unstable region along the cholesterol chain, for the case of TH and TT dimers.

AutoDock-GIST: Incorporating Thermodynamics of Active-Site Water into Scoring Function for Accurate Protein-Ligand Docking

Water plays a significant role in the binding process between protein and ligand. However, the thermodynamics of water molecules are often underestimated, or even ignored, in protein-ligand docking. Usually, the free energies of active-site water molecules are substantially different from those of waters in the bulk region. The binding of a ligand to a protein causes a displacement of these waters from an active site to bulk, and this displacement process substantially contributes to the free energy change of protein-ligand binding. The free energy of active-site water molecules can be calculated by grid inhomogeneous solvation theory (GIST), using molecular dynamics (MD) and the trajectory of a target protein and water molecules. Here, we show a case study of the combination of GIST and a docking program and discuss the effectiveness of the displacing gain of unfavorable water in protein-ligand docking. We combined the GIST-based desolvation function with the scoring function of AutoDock4, which is called AutoDock-GIST. The proposed scoring function was assessed employing 51 ligands of coagulation factor Xa (FXa), and results showed that both scoring accuracy and docking success rate were improved. We also evaluated virtual screening performance of AutoDock-GIST using FXa ligands in the directory of useful decoys-enhanced (DUD-E), thus finding that the displacing gain of unfavorable water is effective for a successful docking campaign.

SAMPL5: 3D-RISM partition coefficient calculations with partial molar volume corrections and solute conformational sampling

Implicit solvent methods for classical molecular modeling are frequently used to provide fast, physics-based hydration free energies of macromolecules. Less commonly considered is the transferability of these methods to other solvents. The Statistical Assessment of Modeling of Proteins and Ligands 5 (SAMPL5) distribution coefficient dataset and the accompanying explicit solvent partition coefficient reference calculations provide a direct test of solvent model transferability. Here we use the 3D reference interaction site model (3D-RISM) statistical-mechanical solvation theory, with a well tested water model and a new united atom cyclohexane model, to calculate partition coefficients for the SAMPL5 dataset. The cyclohexane model performed well in training and testing ([Formula: see text] for amino acid neutral side chain analogues) but only if a parameterized solvation free energy correction was used. In contrast, the same protocol, using single solute conformations, performed poorly on the SAMPL5 dataset, obtaining [Formula: see text] compared to the reference partition coefficients, likely due to the much larger solute sizes. Including solute conformational sampling through molecular dynamics coupled with 3D-RISM (MD/3D-RISM) improved agreement with the reference calculation to [Formula: see text]. Since our initial calculations only considered partition coefficients and not distribution coefficients, solute sampling provided little benefit comparing against experiment, where ionized and tautomer states are more important. Applying a simple [Formula: see text] correction improved agreement with experiment from [Formula: see text] to [Formula: see text], despite a small number of outliers. Better agreement is possible by accounting for tautomers and improving the ionization correction.

Dissecting the Influence of Protein Flexibility on the Location and Thermodynamic Profile of Explicit Water Molecules in Protein-Ligand Binding

Explicit water molecules in the binding site of proteins play a crucial role for protein-ligand association. Recent advances in computer-aided drug discovery methodology allow for an accurate prediction of the localized position and thermodynamic profile of water molecules (i.e., hydration sites) in the binding site. The underlying calculations are based on MD simulations of explicit water molecules in a restrained protein structure. However, the ligand-binding process is typically associated with protein conformational change that influences the position and thermodynamic properties of the hydration site. In this manuscript, we present the developments of two methods to incorporate the influence of protein conformational change on hydration sites either by following the conformational transition step-by-step (method I) or to match the hydration sites of the two transition end states using local coordinate systems (method II). Using these methods, we highlight the difference in the estimated protein desolvation free energy with and without inclusion of protein flexibility. To the best of our knowledge, this is the first study that explicitly studies the influence of protein conformational change on the position and thermodynamic profiles of water molecules and provides methodology to incorporate protein flexibility into the estimation of the desolvation free energy.

Role of Heme Pocket Water in Allosteric Regulation of Ligand Reactivity in Human Hemoglobin

Water molecules can enter the heme pockets of unliganded myoglobins and hemoglobins, hydrogen bond with the distal histidine, and introduce steric barriers to ligand binding. The spectrokinetics of photodissociated CO complexes of human hemoglobin and its isolated α and β chains were analyzed for the effect of heme hydration on ligand rebinding. A strong coupling was observed between heme hydration and quaternary state. This coupling may contribute significantly to the 20-60-fold difference between the R- and T-state bimolecular CO binding rate constants and thus to the modulation of ligand reactivity that is the hallmark of hemoglobin allostery. Heme hydration proceeded over the course of several kinetic phases in the tetramer, including the R to T quaternary transition. An initial 150 ns hydration phase increased the R-state distal pocket water occupancy, nw(R), to a level similar to that of the isolated α (∼60%) and β (∼10%) chains, resulting in a modest barrier to ligand binding. A subsequent phase, concurrent with the first step of the R → T transition, further increased the level of heme hydration, increasing the barrier. The final phase, concurrent with the final step of the allosteric transition, brought the water occupancy of the T-state tetramer, nw(T), even higher and close to full occupancy in both the α and β subunits (∼90%). This hydration level could present an even larger barrier to ligand binding and contribute significantly to the lower iron reactivity of the T state toward CO.

Hydration of proteins and nucleic acids: Advances in experiment and theory. A review

BACKGROUND

Most biological processes involve water, and the interactions of biomolecules with water affect their structure, function and dynamics.

SCOPE OF REVIEW

This review summarizes the current knowledge of protein and nucleic acid interactions with water, with a special focus on the biomolecular hydration layer. Recent developments in both experimental and computational methods that can be applied to the study of hydration structure and dynamics are reviewed, including software tools for the prediction and characterization of hydration layer properties.

MAJOR CONCLUSIONS

In the last decade, important advances have been made in our understanding of the factors that determine how biomolecules and their aqueous environment influence each other. Both experimental and computational methods contributed to the gradually emerging consensus picture of biomolecular hydration.

GENERAL SIGNIFICANCE

An improved knowledge of the structural and thermodynamic properties of the hydration layer will enable a detailed understanding of the various biological processes in which it is involved, with implications for a wide range of applications, including protein-structure prediction and structure-based drug design.

Beyond Membrane Protein Structure: Drug Discovery, Dynamics and Difficulties

Most of the previous content of this book has focused on obtaining the structures of membrane proteins. In this chapter we explore how those structures can be further used in two key ways. The first is their use in structure based drug design (SBDD) and the second is how they can be used to extend our understanding of their functional activity via the use of molecular dynamics. Both aspects now heavily rely on computations. This area is vast, and alas, too large to consider in depth in a single book chapter. Thus where appropriate we have referred the reader to recent reviews for deeper assessment of the field. We discuss progress via the use of examples from two main drug target areas; G-protein coupled receptors (GPCRs) and ion channels. We end with a discussion of some of the main challenges in the area.

A systematic study on RNA NMR chemical shift calculation based on the automated fragmentation QM/MM approach

¹H, ¹³C and ¹⁵N NMR chemical shift calculations on RNAs were performed using the automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) approach.

Prediction of Water Binding to Protein Hydration Sites with a Discrete, Semiexplicit Solvent Model

Buried water molecules are ubiquitous in protein structures and are found at the interface of most protein-ligand complexes. Determining their distribution and thermodynamic effect is a challenging yet important task, of great of practical value for the modeling of biomolecular structures and their interactions. In this study, we present a novel method aimed at the prediction of buried water molecules in protein structures and estimation of their binding free energies. It is based on a semiexplicit, discrete solvation model, which we previously introduced in the context of small molecule hydration. The method is applicable to all macromolecular structures described by a standard all-atom force field, and predicts complete solvent distribution within a single run with modest computational cost. We demonstrate that it indicates positions of buried hydration sites, including those filled by more than one water molecule, and accurately differentiates them from sterically accessible to water but void regions. The obtained estimates of water binding free energies are in fair agreement with reference results determined with the double decoupling method.

Cavities and Excited States in Proteins

Protein cavities or voids are observed as defects in atomic packing. Cavities have long been suggested to play important roles in protein dynamics and function, but the underlying origin and mechanism remains elusive. Here, recent studies about the cavities characterized by high-pressure NMR spectroscopy have been reviewed. Analysis of the pressure-dependent chemical shifts showed both linear and nonlinear response of proteins to pressure. The linear response corresponded to compression within the native ensemble, while the nonlinear response indicated the involvement of low-lying excited states that were different from the native state. The finding of non-linear pressure shifts in various proteins suggested that the existence of the low-lying excited states was common for globular proteins. However, the absolute nonlinear coefficient values varied significantly from protein to protein, and showed a good correlation with the density of cavities. Extensive studies on hen lysozyme as a model system showed that the cavity hydration and water penetration into the interior of proteins was an origin of the conformational transition to the excited states. The importance of cavities for protein function and evolution has also been explained. In addition to these "equilibrium" cavities, there are also "transient" cavities formed in the interior of the protein structure, as manifested by the ring flip motions of aromatic rings. The significance of transient cavities, reflecting an intrinsic dynamic nature within the native state, has also been discussed.

AFNMR: automated fragmentation quantum mechanical calculation of NMR chemical shifts for biomolecules

We evaluate the performance of the automated fragmentation quantum mechanics/molecular mechanics approach (AF-QM/MM) on the calculation of protein and nucleic acid NMR chemical shifts. The AF-QM/MM approach models solvent effects implicitly through a set of surface charges computed using the Poisson-Boltzmann equation, and it can also be combined with an explicit solvent model through the placement of water molecules in the first solvation shell around the solute; the latter substantially improves the accuracy of chemical shift prediction of protons involved in hydrogen bonding with solvent. We also compare the performance of AF-QM/MM on proteins and nucleic acids with two leading empirical chemical shift prediction programs SHIFTS and SHIFTX2. Although the empirical programs outperform AF-QM/MM in predicting chemical shifts, the differences are in some cases small, and the latter can be applied to chemical shifts on biomolecules which are outside the training set employed by the empirical programs, such as structures containing ligands, metal centers, and non-standard residues. The AF-QM/MM described here is implemented in version 5 of the SHIFTS software, and is fully automated, so that only a structure in PDB format is required as input.

Cavity as a source of conformational fluctuation and high-energy state: high-pressure NMR study of a cavity-enlarged mutant of T4 lysozyme

Although the structure, function, conformational dynamics, and controlled thermodynamics of proteins are manifested by their corresponding amino acid sequences, the natural rules for molecular design and their corresponding interplay remain obscure. In this study, we focused on the role of internal cavities of proteins in conformational dynamics. We investigated the pressure-induced responses from the cavity-enlarged L99A mutant of T4 lysozyme, using high-pressure NMR spectroscopy. The signal intensities of the methyl groups in the (1)H/(13)C heteronuclear single quantum correlation spectra, particularly those around the enlarged cavity, decreased with the increasing pressure, and disappeared at 200 MPa, without the appearance of new resonances, thus indicating the presence of heterogeneous conformations around the cavity within the ground state ensemble. Above 200 MPa, the signal intensities of >20 methyl groups gradually decreased with the increasing pressure, without the appearance of new resonances. Interestingly, these residues closely matched those sensing a large conformational change between the ground- and high-energy states, at atmospheric pressure. (13)C and (1)H NMR line-shape simulations showed that the pressure-induced loss in the peak intensity could be explained by the increase in the high-energy state population. In this high-energy state, the aromatic side chain of F114 gets flipped into the enlarged cavity. The accommodation of the phenylalanine ring into the efficiently packed cavity may decrease the partial molar volume of the high-energy state, relative to the ground state. We suggest that the enlarged cavity is involved in the conformational transition to high-energy states and in the volume fluctuation of the ground state.

Hydration in discrete water (II): from neutral to charged solutes

In our previous work, we introduced a solvation model based on discrete solvent representation and demonstrated its ability to estimate hydration free energies for neutral solutes. Here, we present modifications extending the applicability of the model to charged solutes. They include improvements in the representation of the first hydration shell and systematic treatment of long-range interactions. While sharing computational efficiency of implicit solvent models, our approach avoids some of their important limitations, both in the context of electrostatic and nonpolar hydration effects: it naturally captures hydration asymmetry of opposite charges, it relies on solute description by standard all atom force fields instead of utilizing specialized sets of atomic parameters, it predicts solvent distribution in space without the need to geometrically define solvent accessible surface. By combining discrete solvent representation in vicinity of a solute with continuum description of long-range interactions, the model addresses two distinct aspects of biomolecular hydration: complex, short-range effects arising due to molecular nature of aqueous solvent, and bulk contributions. We demonstrate that the model provides good agreement with experimental results for an extensive set of roughly 700 diverse compounds, including neutral and charged solutes with hydration free energies ranging from +3.4 to -536 kcal/mol.

Octanol-Water Partition Coefficient from 3D-RISM-KH Molecular Theory of Solvation with Partial Molar Volume Correction

The octanol-water partition coefficient is an important physical-chemical characteristic widely used to describe hydrophobic/hydrophilic properties of chemical compounds. The partition coefficient is related to the transfer free energy of a compound from water to octanol. Here, we introduce a new protocol for prediction of the partition coefficient based on the statistical-mechanical, 3D-RISM-KH molecular theory of solvation. It was shown recently that with the compound-solvent correlation functions obtained from the 3D-RISM-KH molecular theory of solvation, the free energy functional supplemented with the correction linearly related to the partial molar volume obtained from the Kirkwood-Buff/3D-RISM theory, also called the "universal correction" (UC), provides accurate prediction of the hydration free energy of small compounds, compared to explicit solvent molecular dynamics [ Palmer , D. S. ; J. Phys.: Condens. Matter 2010 , 22 , 492101 ]. Here we report that with the UC reparametrized accordingly this theory also provides an excellent agreement with the experimental data for the solvation free energy in nonpolar solvent (1-octanol) and so accurately predicts the octanol-water partition coefficient. The performance of the Kovalenko-Hirata (KH) and Gaussian fluctuation (GF) functionals of the solvation free energy, with and without UC, is tested on a large library of small compounds with diverse functional groups. The best agreement with the experimental data for octanol-water partition coefficients is obtained with the KH-UC solvation free energy functional.

Quantum calculation of protein NMR chemical shifts based on the automated fragmentation method

The performance of quantum mechanical methods on the calculation of protein NMR chemical shifts is reviewed based on the recently developed automatic fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) approach. By using the Poisson-Boltzmann (PB) model and first solvation water molecules, the influence of solvent effect is also discussed. Benefiting from the fragmentation algorithm, the AF-QM/MM approach is computationally efficient, linear-scaling with a low pre-factor, and thus can be applied to routinely calculate the ab initio NMR chemical shifts for proteins of any size. The results calculated using Density Functional Theory (DFT) show that when the solvent effect is included, this method can accurately reproduce the experimental ¹H NMR chemical shifts, while the ¹³C NMR chemical shifts are less affected by the solvent. However, although the inclusion of solvent effect shows significant improvement for ¹⁵N chemical shifts, the calculated values still have large deviations from the experimental observations. Our study further demonstrates that AF-QM/MM calculated results accurately reflect the dependence of ¹³C(α) NMR chemical shifts on the secondary structure of proteins, and the calculated ¹H chemical shift can be utilized to discriminate the native structure of proteins from decoys.

Role of water in ligand binding to maltose-binding protein: insight from a new docking protocol based on the 3D-RISM-KH molecular theory of solvation

Maltose-binding protein is a periplasmic binding protein responsible for transport of maltooligosaccarides through the periplasmic space of Gram-negative bacteria, as a part of the ABC transport system. The molecular mechanisms of the initial ligand binding and induced large scale motion of the protein's domains still remain elusive. In this study, we use a new docking protocol that combines a recently proposed explicit water placement algorithm based on the 3D-RISM-KH molecular theory of solvation and conventional docking software (AutoDock Vina) to explain the mechanisms of maltotriose binding to the apo-open state of a maltose-binding protein. We confirm the predictions of previous NMR spectroscopic experiments on binding modes of the ligand. We provide the molecular details on the binding mode that was not previously observed in the X-ray experiments. We show that this mode, which is defined by the fine balance between the protein-ligand direct interactions and solvation effects, can trigger the protein's domain motion resulting in the holo-closed structure of the maltose-binding protein with the maltotriose ligand in excellent agreement with the experimental data. We also discuss the role of water in blocking unfavorable binding sites and water-mediated interactions contributing to the stability of observable binding modes of maltotriose.

Analysis of factors influencing hydration site prediction based on molecular dynamics simulations

Water contributes significantly to the binding of small molecules to proteins in biochemical systems. Molecular dynamics (MD) simulation based programs such as WaterMap and WATsite have been used to probe the locations and thermodynamic properties of hydration sites at the surface or in the binding site of proteins generating important information for structure-based drug design. However, questions associated with the influence of the simulation protocol on hydration site analysis remain. In this study, we use WATsite to investigate the influence of factors such as simulation length and variations in initial protein conformations on hydration site prediction. We find that 4 ns MD simulation is appropriate to obtain a reliable prediction of the locations and thermodynamic properties of hydration sites. In addition, hydration site prediction can be largely affected by the initial protein conformations used for MD simulations. Here, we provide a first quantification of this effect and further indicate that similar conformations of binding site residues (RMSD < 0.5 Å) are required to obtain consistent hydration site predictions.

Perturbation of fluid dynamics properties of water molecules during G protein-coupled receptor-ligand recognition: the human A2A adenosine receptor as a key study

Recent advances in structural biology revealed that water molecules play a crucial structural role in the protein architecture and ligand binding of G protein-coupled receptors. In this work, we present an alternative approach to monitor the time-dependent organization of water molecules during the final stage of the ligand-receptor recognition process by means of membrane molecular dynamics simulations. We inspect the variation of fluid dynamics properties of water molecules upon ligand binding with the aim to correlate the results with the binding affinities. The outcomes of this analysis are transferred into a bidimensional graph called water fluid dynamics maps, that allow a fast graphical identification of protein "hot-spots" characterized by peculiar shape and electrostatic properties that can play a critical role in ligand binding. We hopefully believe that the proposed approach might represent a valuable tool for structure-based drug discovery that can be extended to cases where crystal structures are not yet available, or have not been solved at high resolution.

Analysis of biomolecular solvation sites by 3D-RISM theory

We derive, implement, and apply equilibrium solvation site analysis for biomolecules. Our method utilizes 3D-RISM calculations to quickly obtain equilibrium solvent distributions without either necessity of simulation or limits of solvent sampling. Our analysis of these distributions extracts highest likelihood poses of solvent as well as localized entropies, enthalpies, and solvation free energies. We demonstrate our method on a structure of HIV-1 protease where excellent structural and thermodynamic data are available for comparison. Our results, obtained within minutes, show systematic agreement with available experimental data. Further, our results are in good agreement with established simulation-based solvent analysis methods. This method can be used not only for visual analysis of active site solvation but also for virtual screening methods and experimental refinement.

Automated Fragmentation QM/MM Calculation of Amide Proton Chemical Shifts in Proteins with Explicit Solvent Model

We have performed a density functional theory (DFT) calculation of the amide proton NMR chemical shift in proteins using a recently developed automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) approach. Systematic investigation was carried out to examine the influence of explicit solvent molecules, cooperative hydrogen bonding effects, density functionals, size of the basis sets, and the local geometry of proteins on calculated chemical shifts. Our result demonstrates that the predicted amide proton ((1)HN) NMR chemical shift in explicit solvent shows remarkable improvement over that calculated with the implicit solvation model. The cooperative hydrogen bonding effect is also shown to improve the accuracy of (1)HN chemical shifts. Furthermore, we found that the OPBE exchange-correlation functional is the best density functional for the prediction of protein (1)HN chemical shifts among a selective set of DFT methods (namely, B3LYP, B3PW91, M062X, M06L, mPW1PW91, OB98, OPBE), and the locally dense basis set of 6-311++G**/4-31G* is shown to be sufficient for (1)HN chemical shift calculation. By taking ensemble averaging into account, (1)HN chemical shifts calculated by the AF-QM/MM approach can be used to validate the performance of various force fields. Our study underscores that the electronic polarization of protein is of critical importance to stabilizing hydrogen bonding, and the AF-QM/MM method is able to describe the local chemical environment in proteins more accurately than most widely used empirical models.

Biomolecular electrostatics and solvation: a computational perspective

An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view toward describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g. solvent structure, polarization, ion binding, and non-polar behavior) in order to provide a background to understand the different types of solvation models.

3D-RISM-Dock: A New Fragment-Based Drug Design Protocol

We explore a new approach in the rational design of specificity in molecular recognition of small molecules based on statistical-mechanical integral equation theory of molecular liquids in the form of the three-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH). The numerically stable iterative solution of conventional 3D-RISM equations includes the fragmental decomposition of flexible ligands, which are treated as distinct species in solvent mixtures of arbitrary complexity. The computed density functions for solution (including ligand) molecules are obtained as a set of discrete spatial grids that uniquely describe the continuous solvent-site distribution around the protein solute. Potentials of mean force derived from these distributions define the scoring function interfaced with the AutoDock program for an automated ranking of docked conformations. As a case study in terms of solvent composition, we analyze cooperative interactions encountered in the binding of a flexible thiamine molecule to the prion protein at near-physiological conditions. The predicted location and residency times of computed binding modes are in excellent agreement with the available experimental data.

Modified Anderson Method for Accelerating 3D-RISM Calculations Using Graphics Processing Unit

A fast algorithm is proposed to solve the three-dimensional reference interaction site model (3D-RISM) theory on a graphics processing unit (GPU). 3D-RISM theory is a powerful tool for investigating biomolecular processes in solution; however, such calculations are often both memory-intensive and time-consuming. We sought to accelerate these calculations using GPUs, but to work around the problem of limited memory size in GPUs, we modified the less memory-intensive "Anderson method" to give faster convergence to 3D-RISM calculations. Using this method on a Tesla C2070 GPU, we reduced the total computational time by a factor of 8, 1.4 times by the modified Andersen method and 5.7 times by GPU, compared to calculations on an Intel Xeon machine (eight cores, 3.33 GHz) with the conventional method.

Placevent: an algorithm for prediction of explicit solvent atom distribution-application to HIV-1 protease and F-ATP synthase

We have created a simple algorithm for automatically predicting the explicit solvent atom distribution of biomolecules. The explicit distribution is coerced from the three-dimensional (3D) continuous distribution resulting from a 3D reference interaction site model (3D-RISM) calculation. This procedure predicts optimal location of solvent molecules and ions given a rigid biomolecular structure and the solvent composition. We show examples of predicting water molecules near the KNI-272 bound form of HIV-1 protease and predicting both sodium ions and water molecules near the rotor ring of F-adenosine triphosphate (ATP) synthase. Our results give excellent agreement with experimental structure with an average prediction error of 0.39-0.65 Å. Further, unlike experimental methods, this method does not suffer from the partial occupancy limit. Our method can be performed directly on 3D-RISM output within minutes. It is extremely useful for examining multiple specific solvent-solute interactions, as a convenient method for generating initial solvent structures for molecular dynamics calculations, and may assist in refinement of experimental structures. © 2012 Wiley Periodicals, Inc.

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.