Classification of water molecules in protein binding sites

Halogen Bonding on Water─A Drop in the Ocean?

Halogen bonding is a valuable interaction in drug design, offering an unconventional way to influence affinity and selectivity by leveraging the halogen atoms' ability to form directional bonds. The present study evaluates halogen-water interactions within protein binding sites, demonstrating that targeting a water molecule via halogen bonding can in specific cases contribute beneficially to ligand binding. In solving and examining the crystal structure of 2-cyclopentyl-7-iodo-1H-indole-3-carbonitrile bound to DYRK1a kinase, we identified a notable iodine-water interaction, where water accepts a halogen bond with good geometric and energetic features. This starting point triggered further investigations into the prevalence of such interactions across various halogen-bearing ligands (chlorine, bromine, iodine) in the PDB. Using QM calculations (MP2/TZVPP), we highlight the versatility and potential benefits of such halogen-water interactions, particularly when the water molecule is a stable part of the binding site's structured environment. While the interaction energies with water are lower compared to other typical halogen bond acceptors, we deem this different binding strength essential for reducing desolvation costs. We suggest that "interstitial" water molecules, as stable parts of the binding site engaging in multiple strong interactions, could be prime targets for halogen bonding. Further systematic studies, combining high-resolution crystal structures and quantum chemistry, are required to scrutinize whether halogen bonding on water is more than a "drop in the ocean".

Enthalpic Classification of Water Molecules in Target-Ligand Binding

Water molecules play various roles in target-ligand binding. For example, they can be replaced by the ligand and leave the surface of the binding pocket or stay conserved in the interface and form bridges with the target. While experimental techniques supply target-ligand complex structures at an increasing rate, they often have limitations in the measurement of a detailed water structure. Moreover, measurements of binding thermodynamics cannot distinguish between the different roles of individual water molecules. However, such a distinction and classification of the role of individual water molecules would be key to their application in drug design at atomic resolution. In this study, we investigate a quantitative approach for the description of the role of water molecules during ligand binding. Starting from complete hydration structures of the free and ligand-bound target molecules, binding enthalpy scores are calculated for each water molecule using quantum mechanical calculations. A statistical evaluation showed that the scores can distinguish between conserved and displaced classes of water molecules. The classification system was calibrated and tested on more than 1000 individual water positions. The practical tests of the enthalpic classification included important cases of antiviral drug research on HIV-1 protease inhibitors and the Influenza A ion channel. The methodology of classification is based on open source program packages, Gromacs, Mopac, and MobyWat, freely available to the scientific community.

Insight into binding of endogenous neurosteroid ligands to the sigma-1 receptor

The sigma-1 receptor (σ1R) is a non-opioid membrane receptor, which responds to a diverse array of synthetic ligands to exert various pharmacological effects. Meanwhile, candidates for endogenous ligands of σ1R have also been identified. However, how endogenous ligands bind to σ1R remains unknown. Here, we present crystal structures of σ1R from Xenopus laevis (xlσ1R) bound to two endogenous neurosteroid ligands, progesterone (a putative antagonist) and dehydroepiandrosterone sulfate (DHEAS) (a putative agonist), at 2.15-3.09  Å resolutions. Both neurosteroids bind to a similar location in xlσ1R mainly through hydrophobic interactions, but surprisingly, with opposite binding orientations. DHEAS also forms hydrogen bonds with xlσ1R, whereas progesterone interacts indirectly with the receptor through water molecules near the binding site. Binding analyses are consistent with the xlσ1R-neurosteroid complex structures. Furthermore, molecular dynamics simulations and structural data reveal a potential water entry pathway. Our results provide insight into binding of two endogenous neurosteroid ligands to σ1R.

Efficient Sampling of Cavity Hydration in Proteins with Nonequilibrium Grand Canonical Monte Carlo and Polarizable Force Fields

Prediction of the hydration levels of protein cavities and active sites is important to both mechanistic analysis and ligand design. Due to the unique microscopic environment of these buried water molecules, a polarizable model is expected to be crucial for an accurate treatment of protein internal hydration in simulations. Here we adapt a nonequilibrium candidate Monte Carlo approach for conducting grand canonical Monte Carlo simulations with the Drude polarizable force field. The GPU implementation enables the efficient sampling of internal cavity hydration levels in biomolecular systems. We also develop an enhanced sampling approach referred to as B-walking, which satisfies detailed balance and readily combines with grand canonical integration to efficiently calculate quantitative binding free energies of water to protein cavities. Applications of these developments are illustrated in a solvent box and the polar ligand binding site in trypsin. Our simulation results show that including electronic polarization leads to a modest but clear improvement in the description of water position and occupancy compared to the crystal structure. The B-walking approach enhances the range of water sampling in different chemical potential windows and thus improves the accuracy of water binding free energy calculations.

Improved Protein Dynamics and Hydration in the Martini3 Coarse-Grain Model

The Martini coarse-grain force-field has emerged as an important framework to probe cellular processes at experimentally relevant time- and length-scales. However, the recently developed version, the Martini3 force-field with the implemented Go̅ model (Martini3Go̅), as well as previous variants of the Martini model have not been benchmarked and rigorously tested for globular proteins. In this study, we consider three globular proteins, ubiquitin, lysozyme, and cofilin, and compare protein dynamics and hydration with observables from experiments and all-atom simulations. We show that the Martini3Go̅ model is able to accurately model the structural and dynamic features of small globular proteins. Overall, the structural integrity of the proteins is maintained, as validated by contact maps, radii of gyration (Rg), and SAXS profiles. The chemical shifts predicted from the ensemble sampled in the simulations are consistent with the experimental data. Further, a good match is observed in the protein-water interaction energetics, and the hydration levels of the residues are similar to atomistic simulations. However, the protein-water interaction dynamics is not accurately represented and appears to depend on the protein structural complexity, residue specificity, and water dynamics. Our work is a step toward testing and assessing the Martini3Go̅ model and provides insights into future efforts to refine Martini models with improved solvation effects and better correspondence to the underlying all-atom systems.

Water network chemistry to exploit the nature of catalytic water molecules in Mtb DNA gyrase: a computational study to understand the binding mechanism of fluoroquinolones

The dynamics of DNA gyrase and mutants of DNA gyrA such as G88A, A90V, S91P, D94A, D94G, D94N, D94Y; and double-point mutant (S91P-D94G), are meticulously investigated using computational approaches. Molecular dynamics (MD) and hydration thermodynamics have shed light on the fundamental, mechanistic basis of mutations on the conformational stability of Quinolone Binding Pocket (QBP) of DNA gyrase. Analysis of MD results revealed the displacement of a single crystal water molecule (HOH201) from the catalytic site of wild-type (WT) and mutants of DNA gyrA. This prompted our research group to probe the five crystal water molecules present in the QBP of the enzyme using water thermodynamics. Hydration thermodynamics analysis revealed the displacement of HOH201 due to unstable thermodynamic signatures. Further, the analysis highlighted significant changes in thermodynamic signatures and locations of five crystal water hydration sites upon mutation. Integrated MD simulations and water thermodynamics provided promising insights into the conformational changes and inaccessibility of the catalytic water molecule that can influence the design of DNA gyrase inhibitors.Communicated by Ramaswamy H. Sarma.

Accelerating Alchemical Free Energy Prediction Using a Multistate Method: Application to Multiple Kinases

Alchemical free-energy methods based on molecular dynamics (MD) simulations have become important tools to identify modifications of small organic molecules that improve their protein binding affinity during lead optimization. The routine application of pairwise free-energy methods to rank potential binders from best to worst is impacted by the combinatorial increase in calculations to perform when the number of molecules to assess grows. To address this fundamental limitation, our group has developed replica-exchange enveloping distribution sampling (RE-EDS), a pathway-independent multistate method, enabling the calculation of alchemical free-energy differences between multiple ligands (N > 2) from a single MD simulation. In this work, we apply the method to a set of four kinases with diverse binding pockets and their corresponding inhibitors (42 in total), chosen to showcase the general applicability of RE-EDS in prospective drug design campaigns. We show that for the targets studied, RE-EDS is able to model up to 13 ligands simultaneously with high sampling efficiency, leading to a substantial decrease in computational cost when compared to pairwise methods.

A practical guide to machine-learning scoring for structure-based virtual screening

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.

The Advances and Limitations of the Determination and Applications of Water Structure in Molecular Engineering

Water is a key actor of various processes of nature and, therefore, molecular engineering has to take the structural and energetic consequences of hydration into account. While the present review focuses on the target-ligand interactions in drug design, with a focus on biomolecules, these methods and applications can be easily adapted to other fields of the molecular engineering of molecular complexes, including solid hydrates. The review starts with the problems and solutions of the determination of water structures. The experimental approaches and theoretical calculations are summarized, including conceptual classifications. The implementations and applications of water models are featured for the calculation of the binding thermodynamics and computational ligand docking. It is concluded that theoretical approaches not only reproduce or complete experimental water structures, but also provide key information on the contribution of individual water molecules and are indispensable tools in molecular engineering.

3-deazaadenosine: A promising novel p38γ antagonist with potential as a breast cancer therapeutic agent

Human p38γ protein kinase, or MAPK12, is a crucial signaling protein that is important in channelizing membrane signals to the nucleus in the MAPK cascade pathway, associated with breast and colorectal cancer, besides other forms of malignancies and atherosclerotic lesions too. P38γ has a significant contribution to the progression of breast carcinoma due to its multifaceted functions. Targeting p38γ for defining potent antagonists against p38γ can turn out to be an attractive and novel means of breast cancer therapeutics. Novel and potent lead molecules were designed utilizing computational drug design methodologies. Using high-throughput virtual screening, 1909 geometrically similar analogs of known inhibitors were generated, primarily using BIRB796, SB202190, ANP, CHEBI: 620708, and CHEBI: 524699. Chemical correctness was ensured using LigPrep for the standalone library, and Prep Wizard for p38γ using Maestro v.11.5. Using the Glide v5.5 flexible docking procedure on a standalone library of p38γ binding sites, we defined 18 potential leads and assessed their ADMET properties. Lead "1", among the proposed four p38γ antagonists with high-scoring and favorable interactions, was considered for 100 ns molecular dynamics simulations. Among the four proposed leads, Lead '1' displayed consistent and stable bonding interactions with p38γ throughout the 100 ns molecular dynamics (MD) simulations. Additionally, it formed water bridges, contributing to its strong association with the protein. Notably, Lead '1' (3-deazaadenosine) exhibited favorable root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) within the acceptable range of pharmacological properties. Thus, 3-deazaadenosine and its mimetic might be promising new directions for developing a novel class of antagonists for breast cancer treatment.

Molecular docking in organic, inorganic, and hybrid systems: a tutorial review

Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review.

Graphical abstract

WaterKit: Thermodynamic Profiling of Protein Hydration Sites

Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.

Molecular dynamics simulations suggest Thiosemicarbazones can bind p53 cancer mutant R175H

Cancer pathologies are associated with the unfolding and aggregation of most recurring mutations in the DNA Binding Domain (DBD) of p53 that coordinate the destabilization of protein. Substitution at the 175th codon with arginine to histidine (R175H, a mutation of large to small side-chain amino acid) destabilizes the DBD by 3 kcal/mol and triggers breasts, lung cancer, etc. Stabilizing the p53 mutant by small molecules offers an attractive drug-targeted anti-cancer therapy. The thiosemicarbazone (TSC) molecules NPC and DPT are known to act as zinc-metallochaperones to reactivate p53R175H. Here, a combination of LESMD simulations for 10 TSC conformations with a p53R175H receptor, single ligand-protein conformation MD, and ensemble docking with multiple p53R175H conformations observed during simulations is suggested to identify the potential binding site of the target protein in light of their importance for the direct TSC - p53R175H binding. NPC binds mutant R175H in the loop region L2-L3, forming pivotal hydrogen bonds with HIS175, pi‑sulfur bonds with TYR163, and pi-alkyl linkages with ARG174 and PRO190, all of which are contiguous to the zinc-binding native site on p53DBD. DPT, on the other hand, was primarily targeting alternative binding sites such as the loop-helix L1/H2 region and the S8 strand. The similar structural characteristics of TSC-bound p53R175H complexes with wild-type p53DBD are thought to be attributable to involved interactions that favour binding free energy contributions of TSC ligands. Our findings may be useful in the identification of novel pockets with druggable properties.

Accounting for Solvation Correlation Effects on the Thermodynamics of Water Networks in Protein Cavities

Macromolecular recognition and ligand binding are at the core of biological function and drug discovery efforts. Water molecules play a significant role in mediating the protein-ligand interaction, acting as more than just the surrounding medium by affecting the thermodynamics and thus the outcome of the binding process. As individual water contributions are impossible to measure experimentally, a range of computational methods have emerged to identify hydration sites in protein pockets and characterize their energetic contributions for drug discovery applications. Even though several methods model solvation effects explicitly, they focus on determining the stability of specific water sites independently and neglect solvation correlation effects upon replacement of clusters of water molecules, which typically happens in hit-to-lead optimization. In this work, we rigorously determine the conjoint effects of replacing all combinations of water molecules in protein binding pockets through the use of the RE-EDS multistate free-energy method, which combines Hamiltonian replica exchange (RE) and enveloping distribution sampling (EDS). Applications on the small bovine pancreatic trypsin inhibitor and four proteins of the bromodomain family illustrate the extent of solvation correlation effects on water thermodynamics, with the favorability of replacement of the water sites by pharmacophore probes highly dependent on the composition of the water network and the pocket environment. Given the ubiquity of water networks in biologically relevant protein targets, we believe our approach can be helpful for computer-aided drug discovery by providing a pocket-specific and a priori systematic consideration of solvation effects on ligand binding and selectivity.

An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties

The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol^-1 to 2.45 kcal mol^-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.

Enhanced Grand Canonical Sampling of Occluded Water Sites Using Nonequilibrium Candidate Monte Carlo

Water molecules play a key role in many biomolecular systems, particularly when bound at protein-ligand interfaces. However, molecular simulation studies on such systems are hampered by the relatively long time scales over which water exchange between a protein and solvent takes place. Grand canonical Monte Carlo (GCMC) is a simulation technique that avoids this issue by attempting the insertion and deletion of water molecules within a given structure. The approach is constrained by low acceptance probabilities for insertions in congested systems, however. To address this issue, here, we combine GCMC with nonequilibium candidate Monte Carlo (NCMC) to yield a method that we refer to as grand canonical nonequilibrium candidate Monte Carlo (GCNCMC), in which the water insertions and deletions are carried out in a gradual, nonequilibrium fashion. We validate this new approach by comparing GCNCMC and GCMC simulations of bulk water and three protein binding sites. We find that not only is the efficiency of the water sampling improved by GCNCMC but that it also results in increased sampling of ligand conformations in a protein binding site, revealing new water-mediated ligand-binding geometries that are not observed using alternative enhanced sampling techniques.

Structure-based approach: molecular insight of pyranocumarins against α-glucosidase through computational studies

α-glucosidase is an enzyme that catalyzes the release of α-glucose molecules through hydrolysis reactions. Regulation of this enzyme can increase sugar levels in type-2 diabetes mellitus (DM) patients. Pyranocoumarin derivatives have been identified as α-glucosidase inhibitors. Through an in silico approach, this work studied the inhibition of three pyranocoumarin compounds against the α-glucosidase at the molecular level. Molecular docking and molecular dynamics simulation were performed to understand the dynamics behavior of pyranocoumarin derivatives against α-glucosidase. The prediction of free binding energy (ΔG _bind) using the Quantum Mechanics/Molecular Mechanics-Generalized Born (QM/MM-GBSA) approach for each system had the following results, PC1-α-Glu: -13.97 kcal mol^-1, PC2-α-Glu: -3.69 kcal mol^-1, and PC3-α-Glu: -13.68 kcal mol^-1. The interaction energy of each system shows that the grid score, ΔG _bind, and ΔG _exp values had a similar correlation, that was PC1-α-Glu > PC3-α-Glu > PC2-α-Glu. Additionally, the decomposition energy analysis (ΔG ^residue _bind) was carried out to find out the contribution of the key binding residue. The results showed that there were 15 key binding residues responsible for stabilizing pyranocumarin binding with criteria of ΔG ^residue _bind < -1.00 kcal mol^-1. The evaluation presented in this work could provide information on the molecular level about the inhibitory efficiency of pyranocoumarin derivatives against a-glucosidase enzyme based on computational studies.

Elucidating the Sluggish Water Dynamics at the Ice-Binding Surface of the Hyperactive Tenebrio molitor Antifreeze Protein

Quasi-ice-like hydration waters on the ice-binding surface (IBS) of an antifreeze protein (AFP) commonly exhibit sluggish dynamics especially at low temperatures. In this work, we have analyzed molecular dynamics (MD) simulation trajectories at two different temperatures for Tenebrio molitor antifreeze protein (TmAFP) to explore whether the unique quasi-ice-like structuring of hydration water has any impact on making their dynamics slower on the IBS of the protein. Our calculation reveals that, as translational dynamics is coupled with the conformational fluctuations, hydration water on the IBS exhibits sluggish translational motion due to reduced flexibility of the IBS compared to that on the non-ice-binding surface (NIBS) of the protein. Interestingly, it is noticed that rotational motion of hydration water is not coupled with the conformational fluctuations of the surfaces. In that case, structural relaxations of the protein-water (PW) and water-water (WW) hydrogen bonds compete with each other to make the rotational dynamics of hydration water around the IBS either faster or slower with respect to those around the NIBS. At low temperature, the slower structural relaxation of water-water hydrogen bonds dominates and imparts sluggish rotational motion of the hydration water on the IBS of the protein. The slower structural relaxation of water-water hydrogen bonds and hence the retarded rotational dynamics, despite the weak short-lived PW hydrogen bonds on the IBS, is clearly a manifestation of the rigid quasi-ice-like structure of the hydration shell on that surface.

Integration of immunoinformatics and cheminformatics to design and evaluate a multitope vaccine against Klebsiella pneumoniae and Pseudomonas aeruginosa coinfection

Introduction: Klebsiella pneumoniae (K. pneumoniae) and Pseudomonas aeruginosa (P. aeruginosa) are the most common Gram-negative bacteria associated with pneumonia and coinfecting the same patient. Despite their high virulence, there is no effective vaccine against them. Methods: In the current study, the screening of several proteins from both pathogens highlighted FepA and OmpK35 for K. pneumonia in addition to HasR and OprF from P. aeruginosa as promising candidates for epitope mapping. Those four proteins were linked to form a multitope vaccine, that was formulated with a suitable adjuvant, and PADRE peptides to finalize the multitope vaccine construct. The final vaccine's physicochemical features, antigenicity, toxicity, allergenicity, and solubility were evaluated for use in humans. Results: The output of the computational analysis revealed that the designed multitope construct has passed these assessments with satisfactory scores where, as the last stage, we performed a molecular docking study between the potential vaccine construct and K. pneumonia associated immune receptors, TLR4 and TLR2, showing affinitive to both targets with preferentiality for the TLR4 receptor protein. Validation of the docking studies has proceeded through molecular dynamics simulation, which estimated a strong binding and supported the nomination of the designed vaccine as a putative solution for K. pneumoniae and P. aeruginosa coinfection. Here, we describe the approach for the design and assessment of our potential vaccine.

Targeting human thymidylate synthase: Ensemble-based virtual screening for drug repositioning and the role of water

A drug repositioning computational approach was carried to search inhibitors for human thymidylate synthase. An ensemble-based virtual screening of FDA-approved drugs showed the drugs Imatinib, Lumacaftor and Naldemedine to be likely candidates for repurposing. The role of water in the drug-receptor interactions was revealed by the application of an extended AutoDock scoring function that included the water forcefield. The binding affinity scores when hydrated ligands were docked were improved in the drugs considered. Further binding free energy calculations based on the Molecular Mechanics Poisson-Boltzmann Surface Area method revealed that Imatinib, Lumacaftor and Naldemedine scored -130.7 ± 28.1, -210.6 ± 29.9 and -238.0 ± 25.4 kJ/mol, respectively, showing good binding affinity for the candidates considered. Overall, the analysis of the molecular dynamics trajectory of the receptor-drug complexes revealed stable structures for Imatinib, Lumacaftor and Naldemedine, for the entire simulation time.

Substrate Recognition Properties from an Intermediate Structural State of the UreA Transporter

Through a combination of comparative modeling, site-directed and classical random mutagenesis approaches, we previously identified critical residues for binding, recognition, and translocation of urea, and its inhibition by 2-thiourea and acetamide in the Aspergillus nidulans urea transporter, UreA. To deepen the structural characterization of UreA, we employed the artificial intelligence (AI) based AlphaFold2 (AF2) program. In this analysis, the resulting AF2 models lacked inward- and outward-facing cavities, suggesting a structural intermediate state of UreA. Moreover, the orientation of the W82, W84, N279, and T282 side chains showed a large variability, which in the case of W82 and W84, may operate as a gating mechanism in the ligand pathway. To test this hypothesis non-conservative and conservative substitutions of these amino acids were introduced, and binding and transport assessed for urea and its toxic analogue 2-thiourea, as well as binding of the structural analogue acetamide. As a result, residues W82, W84, N279, and T282 were implicated in substrate identification, selection, and translocation. Using molecular docking with Autodock Vina with flexible side chains, we corroborated the AF2 theoretical intermediate model, showing a remarkable correlation between docking scores and experimental affinities determined in wild-type and UreA mutants. The combination of AI-based modeling with classical docking, validated by comprehensive mutational analysis at the binding region, would suggest an unforeseen option to determine structural level details on a challenging family of proteins.

HBD-2 variants and SARS-CoV-2: New insights into inter-individual susceptibility

Background

A deep understanding of the causes of liability to SARS-CoV-2 is essential to develop new diagnostic tests and therapeutics against this serious virus in order to overcome this pandemic completely. In the light of the discovered role of antimicrobial peptides [such as human b-defensin-2 (hBD-2) and cathelicidin LL-37] in the defense against SARS-CoV-2, it became important to identify the damaging missense mutations in the genes of these molecules and study their role in the pathogenesis of COVID-19.

Methods

We conducted a comprehensive analysis with multiple in silico approaches to identify the damaging missense SNPs for hBD-2 and LL-37; moreover, we applied docking methods and molecular dynamics analysis to study the impact of the filtered mutations.

Results

The comprehensive analysis reveals the presence of three damaging SNPs in hBD-2; these SNPs were predicted to decrease the stability of hBD-2 with a damaging impact on hBD-2 structure as well. G51D and C53G mutations were located in highly conserved positions and were associated with differences in the secondary structures of hBD-2. Docking-coupled molecular dynamics simulation analysis revealed compromised binding affinity for hBD-2 SNPs towards the SARS-CoV-2 spike domain. Different protein-protein binding profiles for hBD-2 SNPs, in relation to their native form, were guided through residue-wise levels and differential adopted conformation/orientation.

Conclusions

The presented model paves the way for identifying patients prone to COVID-19 in a way that would guide the personalization of both the diagnostic and management protocols for this serious disease.

Boosting the Full Potential of PyMOL with Structural Biology Plugins

Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses.

Absolute Binding Free Energy Calculations for Buried Water Molecules

Water often plays a key role in mediating protein-ligand interactions. Understanding contributions from active-site water molecules to binding thermodynamics of a ligand is important in predicting binding free energies for ligand optimization. In this work, we tested a non-equilibrium switching method for absolute binding free energy calculations on water molecules in binding sites of 13 systems. We discuss the lessons we learned about identified issues that affected our calculations and ways to address them. This work fits with our larger focus on how to do accurate ligand binding free energy calculations when water rearrangements are very slow, such as rearrangements due to ligand modification (as in relative free energy calculations) or ligand binding (as in absolute free energy calculations). The method studied in this work can potentially be used to account for limited water sampling via providing endpoint corrections to free energy calculations using our calculated binding free energy of water.

Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo

Water plays an important role in mediating protein-ligand interactions. Water rearrangement upon a ligand binding or modification can be very slow and beyond typical timescales used in molecular dynamics (MD) simulations. Thus, inadequate sampling of slow water motions in MD simulations often impairs the accuracy of the accuracy of ligand binding free energy calculations. Previous studies suggest grand canonical Monte Carlo (GCMC) outperforms normal MD simulations for water sampling, thus GCMC has been applied to help improve the accuracy of ligand binding free energy calculations. However, in prior work we observed protein and/or ligand motions impaired how well GCMC performs at water rehydration, suggesting more work is needed to improve this method to handle water sampling. In this work, we applied GCMC in 21 protein-ligand systems to assess the performance of GCMC for rehydrating buried water sites. While our results show that GCMC can rapidly rehydrate all selected water sites for most systems, it fails in five systems. In most failed systems, we observe protein/ligand motions, which occur in the absence of water, combine to close water sites and block instantaneous GCMC water insertion moves. For these five failed systems, we both extended our GCMC simulations and tested a new technique named grand canonical nonequilibrium candidate Monte Carlo (GCNCMC). GCNCMC combines GCMC with the nonequilibrium candidate Monte Carlo (NCMC) sampling technique to improve the probability of a successful water insertion/deletion. Our results show that GCNCMC and extended GCMC can rehydrate all target water sites for three of the five problematic systems and GCNCMC is more efficient than GCMC in two out of the three systems. In one system, only GCNCMC can rehydrate all target water sites, while GCMC fails. Both GCNCMC and GCMC fail in one system. This work suggests this new GCNCMC method is promising for water rehydration especially when protein/ligand motions may block water insertion/removal.

The mechanisms of catalysis and ligand binding for the SARS-CoV-2 NSP3 macrodomain from neutron and x-ray diffraction at room temperature

The nonstructural protein 3 (NSP3) macrodomain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Mac1) removes adenosine diphosphate (ADP) ribosylation posttranslational modifications, playing a key role in the immune evasion capabilities of the virus responsible for the coronavirus disease 2019 pandemic. Here, we determined neutron and x-ray crystal structures of the SARS-CoV-2 NSP3 macrodomain using multiple crystal forms, temperatures, and pHs, across the apo and ADP-ribose-bound states. We characterize extensive solvation in the Mac1 active site and visualize how water networks reorganize upon binding of ADP-ribose and non-native ligands, inspiring strategies for displacing waters to increase the potency of Mac1 inhibitors. Determining the precise orientations of active site water molecules and the protonation states of key catalytic site residues by neutron crystallography suggests a catalytic mechanism for coronavirus macrodomains distinct from the substrate-assisted mechanism proposed for human MacroD2. These data provoke a reevaluation of macrodomain catalytic mechanisms and will guide the optimization of Mac1 inhibitors.

Hit Expansion of a Noncovalent SARS-CoV-2 Main Protease Inhibitor

Inhibition of the SARS-CoV-2 main protease (M^pro) is a major focus of drug discovery efforts against COVID-19. Here we report a hit expansion of non-covalent inhibitors of M^pro. Starting from a recently discovered scaffold (The COVID Moonshot Consortium. Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitor Therapeutics. bioRxiv 2020.10.29.339317) represented by an isoquinoline series, we searched a database of over a billion compounds using a cheminformatics molecular fingerprinting approach. We identified and tested 48 compounds in enzyme inhibition assays, of which 21 exhibited inhibitory activity above 50% at 20 μM. Among these, four compounds with IC₅₀ values around 1 μM were found. Interestingly, despite the large search space, the isoquinolone motif was conserved in each of these four strongest binders. Room-temperature X-ray structures of co-crystallized protein–inhibitor complexes were determined up to 1.9 Å resolution for two of these compounds as well as one of the stronger inhibitors in the original isoquinoline series, revealing essential interactions with the binding site and water molecules. Molecular dynamics simulations and quantum chemical calculations further elucidate the binding interactions as well as electrostatic effects on ligand binding. The results help explain the strength of this new non-covalent scaffold for M^pro inhibition and inform lead optimization efforts for this series, while demonstrating the effectiveness of a high-throughput computational approach to expanding a pharmacophore library.

Prediction of Water Distributions and Displacement at Protein-Ligand Interfaces

The retention and displacement of water molecules during formation of ligand-protein interfaces play a major role in determining ligand binding. Understanding these effects requires a method for positioning of water molecules in the bound and unbound proteins and for defining water displacement upon ligand binding. We describe an algorithm for water placement and a calculation of ligand-driven water displacement in >9000 protein-ligand complexes. The algorithm predicts approximately 38% of experimental water positions within 1.0 Å and about 83% within 1.5 Å. We further show that the predicted water molecules can complete water networks not detected in crystallographic structures of the protein-ligand complexes. The algorithm was also applied to solvation of the corresponding unbound proteins, and this allowed calculation of water displacement upon ligand binding based on differences in the water network between the bound and unbound structures. We illustrate use of this approach through comparison of water displacement by structurally related ligands at the same binding site. This method for evaluation of water displacement upon ligand binding may be of value for prediction of the effects of ligand modification in drug design.

Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques

Water often plays a key role in protein structure, molecular recognition, and mediating protein-ligand interactions. Thus, free energy calculations must adequately sample water motions, which often proves challenging in typical MD simulation time scales. Thus, the accuracy of methods relying on MD simulations ends up limited by slow water sampling. Particularly, as a ligand is removed or modified, bulk water may not have time to fill or rearrange in the binding site. In this work, we focus on several molecular dynamics (MD) simulation-based methods attempting to help rehydrate buried water sites: BLUES, using nonequilibrium candidate Monte Carlo (NCMC); grand, using grand canonical Monte Carlo (GCMC); and normal MD. We assess the accuracy and efficiency of these methods in rehydrating target water sites. We selected a range of systems with varying numbers of waters in the binding site, as well as those where water occupancy is coupled to the identity or binding mode of the ligand. We analyzed the rehydration of buried water sites in binding pockets using both clustering of trajectories and direct analysis of electron density maps. Our results suggest both BLUES and grand enhance water sampling relative to normal MD and grand is more robust than BLUES, but also that water sampling remains a major challenge for all of the methods tested. The lessons we learned for these methods and systems are discussed.

The mechanisms of catalysis and ligand binding for the SARS-CoV-2 NSP3 macrodomain from neutron and X-ray diffraction at room temperature

The NSP3 macrodomain of SARS CoV 2 (Mac1) removes ADP-ribosylation post-translational modifications, playing a key role in the immune evasion capabilities of the virus responsible for the COVID-19 pandemic. Here, we determined neutron and X-ray crystal structures of the SARS-CoV-2 NSP3 macrodomain using multiple crystal forms, temperatures, and pHs, across the apo and ADP-ribose-bound states. We characterize extensive solvation in the Mac1 active site, and visualize how water networks reorganize upon binding of ADP-ribose and non-native ligands, inspiring strategies for displacing waters to increase potency of Mac1 inhibitors. Determining the precise orientations of active site water molecules and the protonation states of key catalytic site residues by neutron crystallography suggests a catalytic mechanism for coronavirus macrodomains distinct from the substrate-assisted mechanism proposed for human MacroD2. These data provoke a re-evaluation of macrodomain catalytic mechanisms and will guide the optimization of Mac1 inhibitors.

Disruption of Water Networks is the Cause of Human/Mouse Species Selectivity in Urokinase Plasminogen Activator (uPA) Inhibitors Derived from Hexamethylene Amiloride (HMA)

The urokinase plasminogen activator (uPA) plays a critical role in tumor cell invasion and migration and is a promising antimetastasis target. 6-Substituted analogues of 5-N,N-(hexamethylene)amiloride (HMA) are potent and selective uPA inhibitors that lack the diuretic and antikaliuretic properties of the parent drug amiloride. However, the compounds display pronounced selectivity for human over mouse uPA, thus confounding interpretation of data from human xenograft mouse models of cancer. Here, computational and experimental findings reveal that residue 99 is a key contributor to the observed species selectivity, whereby enthalpically unfavorable expulsion of a water molecule by the 5-N,N-hexamethylene ring occurs when residue 99 is Tyr (as in mouse uPA). Analogue 7 lacking the 5-N,N-hexamethylene ring maintained similar water networks when bound to human and mouse uPA and displayed reduced selectivity, thus supporting this conclusion. The study will guide further optimization of dual-potent human/mouse uPA inhibitors from the amiloride class as antimetastasis drugs.

Rational design of cell active C2-modified DGJ analogues for the inhibition of human α-galactosidase A (GALA)

We report the rational design and synthesis of C2-modified DGJ analogues to improve the selective inhibition of human GALA over other glycosidases. We prepare these analogues using a concise route from non-carbohydrate materials and demonstrate the most selective inhibitor 7c (∼100-fold) can act in Fabry patient cells to drive reductions in levels of the disease-relevant glycolipid Gb3.

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.

Structural variation of protein-ligand complexes of the first bromodomain of BRD4

The bromodomain-containing protein 4 (BRD4), a member of the bromodomain and extra-terminal domain (BET) family, plays a key role in several diseases, especially cancers. With increased interest in BRD4 as a therapeutic target, many X-ray crystal structures of the protein in complex with small molecule inhibitors are publicly available over the recent decade. In this study, we use this structural information to investigate the conformations of the first bromodomain (BD1) of BRD4. Structural alignment of 297 BRD4-BD1 complexes shows a high level of similarity between the structures of BRD4-BD1, regardless of the bound ligand. We employ WONKA, a tool for detailed analyses of protein binding sites, to compare the active site of over 100 of these crystal structures. The positions of key binding site residues show a high level of conformational similarity, with the exception of Trp81. A focused analysis on the highly conserved water network in the binding site of BRD4-BD1 is performed to identify the positions of these water molecules across the crystal structures. The importance of the water network is illustrated using molecular docking and absolute free energy perturbation simulations. 82% of the ligand poses were better predicted when including water molecules as part of the receptor. Our analysis provides guidance for the design of new BRD4-BD1 inhibitors and the selection of the best structure of BRD4-BD1 to use in structure-based drug design, an important approach for faster and more cost-efficient lead discovery.

Drugging the undruggable: a computational chemist's view of KRASG12C

In recent years, the emergence of targeted covalent inhibitors which bind to the G12C mutant of KRAS have offered a solution to this previously intractable target. Inhibitors of KRASG12C tend to be structurally complex, displaying features such as atropisomerism, chiral centres and a reactive covalent warhead. Such molecules result in lengthy and challenging syntheses, and as a consequence critical decisions need to be made at the design level to maximise the chances of success. Here we take a retrospective look into how computational chemistry can help guide and prioritise medicinal chemistry efforts in the context of a series of conformationally restricted tetracyclic quinolines.

Outliers in SAR and QSAR: 3. Importance of considering the role of water molecules in protein-ligand interactions and quantitative structure-activity relationship studies

It is frequently mentioned that QSARs have not generally lived up to expectations, especially in cases where high predictability is expected yet failed to deliver satisfactory results. Even though outliers can provide an increased opportunity in drug discovery research, outliers in SAR and QSAR can contort predictions and affect the accuracy if proper attention is not given. The percentages of outliers in QSARs have not changed appreciably over the last decade. In our previous studies, we suggested two possible sources of outliers in SAR and QSAR. In this paper, we suggest an additional possible source of outliers in QSAR. We presented several literature examples that show one or more water molecules that play a critical role in protein-ligand binding interactions as observed in their crystal structures. These examples illustrate that failing to account for the effects of water molecules in protein-ligand interactions could mislead interpretation and possibly yield outliers in SAR and QSAR. Examples include cases where QSAR, considering the role of water molecules in protein-ligand crystal structures, provided deeper insight into the understanding and interpretation of the developed QSAR.

Characterizing the Water Wire in the Gramicidin Channel Found by Monte Carlo Sampling Using Continuum Electrostatics and in Molecular Dynamics Trajectories with Conventional or Polarizable Force Fields

Water molecules play a key role in all biochemical processes. They help define the shape of proteins, and they are reactant or product in many reactions and are released as ligands are bound. They facilitate the transfer of protons through transmembrane proton channel, pump and transporter proteins. Continuum electrostatics (CE) force fields used by program Multiconformation CE (MCCE) capture electrostatic interactions in biomolecules with an implicit solvent, which captures the averaged solvent water equilibrium properties. Hybrid CE methods can use explicit water molecules within the protein surrounded by implicit solvent. These hybrid methods permit the study of explicit hydrogen bond networks within the protein and allow analysis of processes such as proton transfer reactions. Yet hybrid CE methods have not been rigorously tested. Here, we present an explicit treatment of water molecules in the Gramicidin A (gA) channel using MCCE and compare the resulting distributions of water molecules and key hydration features against those obtained with explicit solvent Molecular Dynamics (MD) simulations with the nonpolarizable CHARMM36 and polarizable Drude force fields. CHARMM36 leads to an aligned water wire in the channel characterized by a large absolute net water dipole moment; the MCCE and Drude analysis lead to a small net dipole moment as the water molecules change orientation within the channel. The correct orientation is not as yet known, so these calculations identify an open question.

Nonsteroidal ecdysone receptor agonists use a water channel for binding to the ecdysone receptor complex EcR/USP

The ecdysone receptor (EcR) possesses the remarkable capacity to adapt structurally to different types of ligands. EcR binds ecdysteroids, including 20-hydroxyecdysone (20E), as well as nonsteroidal synthetic agonists such as insecticidal dibenzoylhydrazines (DBHs). Here, we report the crystal structures of the ligand-binding domains of Heliothis virescens EcR/USP bound to the DBH agonist BYI09181 and to the imidazole-type compound BYI08346. The region delineated by helices H7 and H10 opens up to tightly fit a phenyl ring of the ligands to an extent that depends on the bulkiness of ring substituent. In the structure of 20E-bound EcR, this part of the ligand-binding pocket (LBP) contains a channel filled by water molecules that form an intricate hydrogen bond network between 20E and LBP. The water channel present in the nuclear receptor bound to its natural hormone acts as a critical molecular adaptation spring used to accommodate synthetic agonists inside its binding cavity.

Ligand design by targeting a binding site water

Solvent reorganization is a major driving force of protein–ligand association, but the contribution of binding site waters to ligand affinity is poorly understood. We investigated how altered interactions with a water network can influence ligand binding to a receptor. A series of ligands of the A_2A adenosine receptor, which either interacted with or displaced an ordered binding site water, were studied experimentally and by molecular dynamics simulations. An analog of the endogenous ligand that was unable to hydrogen bond to the ordered water lost affinity and this activity cliff was captured by molecular dynamics simulations. Two compounds designed to displace the ordered water from the binding site were then synthesized and evaluated experimentally, leading to the discovery of an A_2A agonist with nanomolar activity. Calculation of the thermodynamic profiles resulting from introducing substituents that interacted with or displaced the ordered water showed that the gain of binding affinity was enthalpy driven. Detailed analysis of the energetics and binding site hydration networks revealed that the enthalpy change was governed by contributions that are commonly neglected in structure-based drug optimization. In particular, simulations suggested that displacement of water from a binding site to the bulk solvent can lead to large energy contributions. Our findings provide insights into the molecular driving forces of protein–ligand binding and strategies for rational drug design.

Solvent reorganization is a major driving force of protein–ligand association, but the contribution of binding site waters to ligand affinity is poorly understood.

Identification of 7,8-dihydroxy-3-phenylcoumarin as a reversible monoamine oxidase enzyme inhibitor

We herein report the biological evaluation of 3-arylcoumarin derivatives (3a-l) as potential human monoamine oxidase-A and -B (hMAO-A and hMAO-B) inhibitors. The result indicated that 7,8-dihydroxy-3-(4-nitrophenyl)coumarin (3j) was most effective against MAO-A (inhibition concentration [IC₅₀ ] = 6.46 ± 0.02 µM) and MAO-B (IC₅₀  = 3.8 ± 0.3 µM) enzymes than other synthesized compounds and reference compounds (pargyline and moclobemide). Furthermore, compound (3j) showed (a) nonselectivity against hMAO enzymes, (b) reversible hMAO enzymes inhibition, and (c) neuroprotection against H₂ O₂ -treated human neuroblastoma (N2a) cells. Finally, a molecular modeling study revealed that the hMAO enzymes inhibitory activity of the compound (3j) may be due to the orientation where the nitro (NO₂ ) group lies deep into the receptor and the phenyl ring directed toward flavin adenosine dinucleotide via hydrogen bond interaction, and possible π-π interaction with various important residues. Thus, the results of the present study demonstrate that compound (3j) can be considered as a promising scaffold for the development of hMAO-A and hMAO-B inhibitors.

Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo

The prediction of protein-ligand binding affinities using free energy perturbation (FEP) is becoming increasingly routine in structure-based drug discovery. Most FEP packages use molecular dynamics (MD) to sample the configurations of proteins and ligands, as MD is well-suited to capturing coupled motion. However, MD can be prohibitively inefficient at sampling water molecules that are buried within binding sites, which has severely limited the domain of applicability of FEP and its prospective usage in drug discovery. In this paper, we present an advancement of FEP that augments MD with grand canonical Monte Carlo (GCMC), an enhanced sampling method, to overcome the problem of sampling water. We accomplished this without degrading computational performance. On both old and newly assembled data sets of protein-ligand complexes, we show that the use of GCMC in FEP is essential for accurate and robust predictions for ligand perturbations that disrupt buried water.