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

Kawsar SM. Marine-Derived Compounds as Potential Inhibitors of Hsp90 for Anticancer and Antimicrobial Drug Development: A Comprehensive In Silico Study

Marine compounds constitute a diverse and invaluable resource for the discovery of bioactive substances with promising applications in the pharmaceutical development of anti-inflammatory and antibacterial agents. In this study, a comprehensive methodology was employed, encompassing pharmacophore modeling, virtual screening, in silico ADMET assessment (encompassing aspects of absorption, distribution, metabolism, excretion, and toxicity), and molecular dynamics simulations. These methods were applied to identify new inhibitors targeting the Hsp90 protein (heat shock protein 90), commencing with a diverse assembly of compounds sourced from marine origins. During the virtual screening phase, an extensive exploration was conducted on a dataset comprising 31,488 compounds sourced from the CMNPD database, characterized by a wide array of molecular structures. The principal objective was the development of structure-based pharmacophore models, a valuable approach when the pool of known ligands is limited. The pharmacophore model DDRRR was successfully constructed within the active sites of the Hsp90 crystal structure. Subsequent docking studies led to the identification of six compounds (CMNPD 22591, 9335, 10015, 360799, 15115, and 20988) demonstrating substantial binding affinities, each with values below -8.3 kcal/mol. In the realm of in silico ADMET predictions, five of these compounds exhibited favorable pharmacokinetic properties. Furthermore, molecular dynamics simulations and total binding energy calculations using MM-PBSA indicated that these marine-derived compounds formed exceptionally stable complexes with the Hsp90 receptor over a 100-nanosecond simulation period. These findings underscore the considerable potential of these novel marine compounds as promising candidates for anticancer and antimicrobial drug development.

Total free energy analysis of fully hydrated proteins

The total free energy of a hydrated biomolecule and its corresponding decomposition of energy and entropy provides detailed information about regions of thermodynamic stability or instability. The free energies of four hydrated globular proteins with different net charges are calculated from a molecular dynamics simulation, with the energy coming from the system Hamiltonian and entropy using multiscale cell correlation. Water is found to be most stable around anionic residues, intermediate around cationic and polar residues, and least stable near hydrophobic residues, especially when more buried, with stability displaying moderate entropy-enthalpy compensation. Conversely, anionic residues in the proteins are energetically destabilized relative to singly solvated amino acids, while trends for other residues are less clear-cut. Almost all residues lose intraresidue entropy when in the protein, enthalpy changes are negative on average but may be positive or negative, and the resulting overall stability is moderate for some proteins and negligible for others. The free energy of water around single amino acids is found to closely match existing hydrophobicity scales. Regarding the effect of secondary structure, water is slightly more stable around loops, of intermediate stability around β strands and turns, and least stable around helices. An interesting asymmetry observed is that cationic residues stabilize a residue when bonded to its N-terminal side but destabilize it when on the C-terminal side, with a weaker reversed trend for anionic residues.

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.

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.

Instantaneous generation of protein hydration properties from static structures

Complex molecular simulation methods are typically required to calculate the thermodynamic properties of biochemical systems. One example thereof is the thermodynamic profiling of (de)solvation of proteins, which is an essential driving force for protein-ligand and protein-protein binding. The thermodynamic state of water molecules depends on its enthalpic and entropic components; the latter is governed by dynamic properties of the molecule. Here, we developed, to the best of our knowledge, two novel machine learning methods based on deep neural networks that are able to generate the converged thermodynamic state of dynamic water molecules in the heterogeneous protein environment based solely on the information of the static protein structure. The applicability of our machine learning methods to predict the hydration information is demonstrated in two different studies, the qualitative analysis and quantitative prediction of structure-activity relationships, and the prediction of protein-ligand binding modes.

Role of Displacing Confined Solvent in the Conformational Equilibrium of β-Cyclodextrin

This study investigates the role of hydration and its relationship to the conformational equilibrium of the host molecule β-cyclodextrin. Molecular dynamics simulations indicate that the unbound β-cyclodextrin exhibits two state behavior in explicit solvent due to the opening and closing of its cavity. In implicit solvent, these transitions are not observed, and there is one dominant conformation of β-cyclodextrin with an open cavity. Based on these observations, we investigate the hypothesis that the expulsion of thermodynamically unfavorable water molecules into the bulk plays an important role in controlling the accessibility of the closed macrostate at room temperature. We compare the results of the molecular mechanics analytical generalized Born plus nonpolar solvation approach to those obtained through grid inhomogeneous solvation theory analysis with explicit solvation to elucidate the thermodynamic forces at play. The work illustrates the use of continuum solvent models to tease out solvation effects related to the inhomogeneity and the molecular nature of water and demonstrates the key role of the thermodynamics of enclosed hydration in driving the conformational equilibrium of molecules in solution.

Calculation of Thermodynamic Properties of Bound Water Molecules

Water molecules in the binding site of a protein significantly influence protein structure and function, for example, by mediating protein-ligand interactions or in form of desolvation as driving force for ligand binding. The knowledge about location and thermodynamic properties of water molecules in the binding site is crucial to the understanding of protein function. This chapter describes the method of calculating the location and thermodynamic properties of bound water molecules from molecular dynamics (MD) simulation trajectories. Thermodynamic profiles of water molecules can be calculated either with or without the presence of a bound ligand based on the scientific problem. The location and thermodynamic profile of hydration sites mediating the protein-ligand interactions is important for understanding protein-ligand binding. The protein desolvation free energy can be estimated for any ligand by summation of the hydration site free energies of the displaced hydration sites. The WATsite program with an easy-to-use graphical user interface (GUI) based on PyMOL was developed for those calculations and is discussed in this chapter. The WATsite program and its PyMOL plugin are available free of charge from http://people.pharmacy.purdue.edu/~mlill/software/watsite/version3.shtml .

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.

"Solvent hydrogen-bond occlusion": A new model of polar desolvation for biomolecular energetics

Water engages in two important types of interactions near biomolecules: it forms ordered "cages" around exposed hydrophobic regions, and it participates in hydrogen bonds with surface polar groups. Both types of interaction are critical to biomolecular structure and function, but explicitly including an appropriate number of solvent molecules makes many applications computationally intractable. A number of implicit solvent models have been developed to address this problem, many of which treat these two solvation effects separately. Here, we describe a new model to capture polar solvation effects, called SHO ("solvent hydrogen-bond occlusion"); our model aims to directly evaluate the energetic penalty associated with displacing discrete first-shell water molecules near each solute polar group. We have incorporated SHO into the Rosetta energy function, and find that scoring protein structures with SHO provides superior performance in loop modeling, virtual screening, and protein structure prediction benchmarks. These improvements stem from the fact that SHO accurately identifies and penalizes polar groups that do not participate in hydrogen bonds, either with solvent or with other solute atoms ("unsatisfied" polar groups). We expect that in future, SHO will enable higher-resolution predictions for a variety of molecular modeling applications. © 2017 Wiley Periodicals, Inc.

WATsite2.0 with PyMOL Plugin: Hydration Site Prediction and Visualization

Water molecules in the binding site of a protein significantly influence protein structure and function, for example, by mediating protein-ligand interactions or due to water displacement as driving force for ligand binding. The knowledge about location and thermodynamic contributions of binding site water molecules is crucial for understanding protein function. WATsite is a hydration site analysis program that was developed together with an easy-to-use graphical user interface (GUI) based on PyMOL. WATsite identifies hydration sites from a molecular dynamics (MD) simulation trajectory with four different types of explicit water molecules. Hydration sites can be identified with or without the presence of a bound ligand dependent on the scientific problem. The protein desolvation free energy can be estimated for any ligand by summation of the hydration site free energies of the displaced hydration sites. The location and thermodynamic profile of hydration sites mediating the protein-ligand interactions is important for understanding protein-ligand binding. The WATsite program and GUI are available free of charge from http://people.pharmacy.purdue.edu/~mlill/software/watsite/version2.shtml .

Current status and future prospects for enabling chemistry technology in the drug discovery process

This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of "dangerous" reagents. Also featured are advances in the "computer-assisted drug design" area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities.

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.

The solvation structure of alprazolam

Alprazolam is a benzodiazepine that is commonly prescribed for the treatment of anxiety and other related disorders.