Energetics of displacing water molecules from protein binding sites: consequences for ligand optimization

Structural and thermodynamic properties of conserved water molecules in Mpro native: A combined approach by MD simulation and Grid Inhomogeneous Solvation Theory

The new viral strains of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are continuously rising, becoming more virulent, and transmissible. Therefore, the development of new antiviral drugs is essential. Due to its significant role in the viral life cycle of SARS-CoV-2, the main protease (Mpro) enzyme is a leading target for antiviral drug design. The Mpro monomer consists of domain DI, DII, and DI-DII interface. Twenty-one conserved water molecules (W4-W24) are occupied at these domains according to multiple crystal structure analyses. The crystal and MD structures reveal the presence of eight conserved water sites in domain DI, DII and remaining in the DI-DII interface. Grid-based inhomogeneous fluid solvation theory (GIST) was employed on MD structures of Mpro native to predict structural and thermodynamic properties of each conserved water site for focusing to identify the specific conserved water molecules that can easily be displaced by proposed ligands. Finally, MD water W13 is emerged as a promising candidate for water mimic drug design due to its low mean interaction energy, loose binding character with the protein, and its involvement in a water-mediated H-bond with catalytic His41 via the interaction Thr25(OG)---W13---W---His41(NE2). In this context, water occupancy, relative interaction energy, entropy, and topologies of W13 are thermodynamically acceptable for the water displacement method. Therefore, the strategic use of W13's geometrical position in the DI domain may be implemented for drug discovery against COVID disease by designing new ligands with appropriately oriented chemical groups to mimic its structural, electronic, and thermodynamic properties.

Recent advances in the development of inhibitors targeting KRAS-G12C and its related pathways

The RAS gene, also known as the mouse sarcoma virus, includes three genes (KRAS, HRAS, and NRAS) that are associated with human tumors. Among them, KRAS has the highest incidence of mutations in cancer, accounting for around 80% of cases. At the molecular level, the RAS gene plays a regulatory role in transcription and translation, while at the cellular level, it affects cell proliferation and migration, making it crucial for cancer development. In 2021, the FDA approved AMG510, the first direct inhibitor targeting the KRAS-G12C mutation, which has shown tumor regression, prolonged survival, and low off-target activity. However, with the increase of drug resistance, a single inhibitor is no longer sufficient to achieve the desired effect on tumors. Therefore, a large number of other highly efficient inhibitors are being developed at different stages. This article provides an overview of the mechanism of action targeting KRAS-G12C in the KRASGTP-KRASGDP cycle pathway, as well as the structure-activity relationship, structure optimization, and biological activity effects of inhibitors that target the upstream and downstream pathways, or combination therapy.

Investigation of molecular details of a bacterial cationic amino acid transporter (GkApcT) during arginine transportation using molecular dynamics simulation and umbrella sampling techniques

CONTEXT

Cationic amino acid transporters (CATs) facilitate arginine transport across membranes and maintain its levels in various tissues and organs, but their overexpression has been associated with severe cancers. A recent study identified the alternating access mechanism and critical residues involved in arginine transportation in a cationic amino acid transporter from Geobacillus kaustophilus (GkApcT). Here, we used molecular dynamics (MD) simulation methods to investigate the transportation mechanism of arginine (Arg) through GkApcT. The results revealed that arginine strongly interacts with specific binding site residues (Thr43, Asp111, Glu115, Lys191, Phe231, Ile234, and Asp237). Based on the umbrella sampling, the main driving force for arginine transport is the polar interactions of the arginine with channel-lining residues. An in-depth description of the dissociation mechanism and binding energy analysis brings valuable insight into the interactions between arginine and transporter residues, facilitating the design of effective CAT inhibitors in cancer cells.

METHODS

The membrane-protein system was constructed by uploading the prokaryotic CAT (PDB ID: 6F34) to the CHARMM-GUI web server. Molecular dynamics simulations were done using the GROMACS package, version 5.1.4, with the CHARMM36 force field and TIP3P water model. The MM-PBSA approach was performed for determining the arginine binding free energy. Furthermore, the hotspot residues were identified through per-residue decomposition analysis. The characteristics of the channel such as bottleneck radius and channel length were analyzed using the CaverWeb 1.1 web server. The proton wire inside the transporter was investigated based on the classic Grotthuss mechanism. We also investigated the atomistic details of arginine transportation using the path-based free energy umbrella sampling technique (US).

Interaction of lectin Sambucus nigra with sialylated trisaccharides in the presence of osmolytes. Chronopotentiometric sensing

Trisaccharides bind to their interaction partners-lectins relatively weakly, which makes detection of their complexes challenging. In this work, we show that an osmolyte presence improves the distinguishing complexes of lectin Sambucus nigra with trisialyllactoses with various binding affinities. The addition of osmolyte, non-binding sugar mannose significantly improved the precision of binding experiments performed using chronopotentiometric stripping at the electrode surface and fluorescence analysis in solution. Osmolytes minimized nonspecific interactions between binding sugar and lectin. Obtained findings can be utilized in any in vitro methods studying interactions of carbohydrates, respectively their conjugates with proteins. The study of carbohydrate interactions appears important since they play essential roles in a variety of biological processes including carcinogenesis.

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.

A highly selective purine-based inhibitor of CSF1R potently inhibits osteoclast differentiation

The colony-stimulating factor 1 receptor (CSF1R) plays an important role in the regulation of many inflammatory processes, and overexpression of the kinase is implicated in several disease states. Identifying selective, small-molecule inhibitors of CSF1R may be a crucial step toward treating these disorders. Through modelling, synthesis, and a systematic structure-activity relationship study, we have identified a number of potent and highly selective purine-based inhibitors of CSF1R. The optimized 6,8-disubstituted antagonist, compound 9, has enzymatic IC₅₀ of 0.2 nM, and displays a strong affinity toward the autoinhibited form of CSF1R, contrasting that of other previously reported inhibitors. As a result of its binding mode, the inhibitor shows excellent selectivity (Selectivity score: 0.06), evidenced by profiling towards a panel of 468 kinases. In cell-based assays, this inhibitor shows dose-dependent blockade of CSF1-mediated downstream signalling in murine bone marrow-derived macrophages (IC₅₀ = 106 nM) as well as disruption of osteoclast differentiation at nanomolar levels. In vivo experiments, however, indicate that improve metabolic stability is needed in order to further progress this compound class.

Heterogeneous and Allosteric Role of Surface Hydration for Protein-Ligand Binding

Atomistic-level understanding of surface hydration mediating protein-protein interactions and ligand binding has been a challenge due to the dynamic nature of water molecules near the surface. We develop a computational method to evaluate the solvation free energy based on the density map of the first hydration shell constructed from all-atom molecular dynamics simulation and use it to examine the binding of two intrinsically disordered ligands to their target protein domain. One ligand is from the human protein, and the other is from the 1918 Spanish flu virus. We find that the viral ligand incurs a 6.9 kcal/mol lower desolvation penalty upon binding to the target, which is consistent with its stronger binding affinity. The difference arises from the spatially fragmented and nonuniform water density profiles of the first hydration shell. In particular, residues that are distal from the ligand-binding site contribute to a varying extent to the desolvation penalty, among which the "entropy hotspot" residues contribute significantly. Thus, ligand binding alters hydration on remote sites in addition to affecting the binding interface. The nonlocal effect disappears when the conformational motion of the protein is suppressed. The present results elucidate the interplay between protein conformational dynamics and surface hydration. Our approach of measuring the solvation free energy based on the water density of the first hydration shell is tolerant of the conformational fluctuation of protein, and we expect it to be applicable to investigating a broad range of biomolecular interfaces.

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.

In Silico Evaluation of the Thr58-Associated Conserved Water with KRAS Switch-II Pocket Binders

The KRAS switch-II pocket (SII-P) has proven to be one of the most successful tools for targeting KRAS with small molecules to date. This has been demonstrated with several KRAS(G12C)-targeting covalent inhibitors, already resulting in two FDA-approved drugs. Several earlier-stage compounds have also been reported to engage KRAS SII-P with other position 12 mutants, including G12D, G12S, and G12R. A highly conserved water molecule exists in the KRAS SII-P, linking Thr58 of switch-II and Gly10 of β1 sheet. This conserved water is also present in the cocrystal structures of most of the disclosed small-molecule inhibitors but is only displaced by a handful of SII-P binders. Here, we evaluated the conserved water molecule energetics by the WaterMap for the SII-P binders with publicly disclosed structures and studied the water behavior in the presence of selected inhibitors by microsecond timescale molecular dynamics (MD) simulations using two water models (total simulation time of 120 μs). Our data revealed the high-energy nature of this hydration site when coexisting with an SII-P binder and that there is a preference for a single isolated hydration site in this location within the most advanced compounds. Furthermore, water displacement was only achieved with a few disclosed compounds and was suboptimal, as for instance a cyanomethyl group as a water displacer appears to introduce repulsion with the native conformation of Thr58. These results suggested that this conserved water should be considered more central when designing new inhibitors, especially in the design of noncovalent inhibitors targeting the SII-P.

The slow but steady rise of binding free energy calculations in drug discovery

Binding free energy calculations are increasingly used in drug discovery research to predict protein-ligand binding affinities and to prioritize candidate drug molecules accordingly. It has taken decades of collective effort to transform this academic concept into a technology adopted by the pharmaceutical and biotech industry. Having personally witnessed and taken part in this transformation, here I recount the (incomplete) list of problems that had to be solved to make this computational tool practical and suggest areas of future development.

Conserved Water Networks Identification for Drug Design Using Density Clustering Approaches on Positional and Orientational Data

This work describes the development and testing of a method for the identification and classification of conserved water molecules and their networks from molecular dynamics (MD) simulations. The conserved waters in the active sites of proteins influence protein-ligand binding. Recently, several groups have argued that a water network formed from conserved waters can be used to interpret the thermodynamic signature of the binding site. We implemented a novel methodology in which we apply the complex approach to categorize water molecules extracted from the MD simulation trajectories using clustering approaches. The main advantage of our methodology as compared to current state of the art approaches is the inclusion of the information on the orientation of hydrogen atoms to further inform the clustering algorithm and to classify the conserved waters into different subtypes depending on how strongly certain orientations are preferred. This information is vital for assessing the stability of water networks. The newly developed approach is described in detail as well as validated against known results from the scientific literature including comparisons with the experimental data on thermolysin, thrombin, and Haemophilus influenzae virulence protein SiaP as well as with the previous computational results on thermolysin. We observed excellent agreement with the literature and were also able to provide additional insights into the orientations of the conserved water molecules, highlighting the key interactions which stabilize them. The source code of our approach, as well as the utility tools used for visualization, are freely available on GitHub.

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.

4-Chloro-1,2-phenylenediamine induced structural perturbation and genotoxic aggregation in human serum albumin

4-Chloro-1,2-phenylenediamine (4-Cl-OPD) is a halogenated aromatic diamine used as a precursor in permanent hair color production. Despite its well-documented mutagenic and carcinogenic effects in various in vitro and in vivo models, its role in fibrillar aggregate formation and their genotoxic effect in therapeutic proteins has received less attention. The significance of human serum albumin (HSA) arises from its involvement in bio-regulatory and transport processes. HSA misfolding and aggregation are responsible for some of the most frequent neurodegenerative disorders. We used various complementary approaches to track the formation of amyloid fibrils and their genotoxic effect. Molecular dynamics study demonstrated the complex stability. The impact of 4-Cl-OPD on the structural dynamics of HSA was confirmed by Raman spectroscopy, X-ray diffraction, HPLC and SDS-PAGE. Fibrilllar aggregates were investigated using Congo red assay, DLS, and SEM. The genotoxic nature of 4-Cl-OPD was confirmed using plasmid nicking assay and DAPI staining, which revealed DNA damage and cell apoptosis. 4-Cl-OPD provides a model system for studying fibrillar aggregation and their genotoxic potential in the current investigation. Future studies should investigate the inhibition of the aggregation/fibrillation process, which may yield valuable clinical insights.

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1]

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.

Water Networks Repopulate Protein-Ligand Interfaces with Temperature

High-resolution crystal structures highlight the importance of water networks in protein-ligand interactions. However, as these are typically determined at cryogenic temperature, resulting insights may be structurally precise but not biologically accurate. By collecting 10 matched room-temperature and cryogenic datasets of the biomedical target Hsp90α, we identified changes in water networks that impact protein conformations at the ligand binding interface. Water repositioning with temperature repopulates protein ensembles and ligand interactions. We introduce Flipper conformational barcodes to identify temperature-sensitive regions in electron density maps. This revealed that temperature-responsive states coincide with ligand-responsive regions and capture unique binding signatures that disappear upon cryo-cooling. Our results have implications for discovering Hsp90 selective ligands, and, more generally, for the utility of hidden protein and water conformations in drug discovery.

Leveraging structural and 2D-QSAR to investigate the role of functional group substitutions, conserved surface residues and desolvation in triggering the small molecule-induced dimerization of hPD-L1

Small molecules are rising as a new generation of immune checkpoints’ inhibitors, with compounds targeting the human Programmed death-ligand 1 (hPD-L1) protein are pioneering this area of research. Promising examples include the recently disclosed compounds from Bristol-Myers-Squibb (BMS). These molecules bind specifically to hPD-L1 through a unique mode of action. They induce dimerization between two hPD-L1 monomers through the hPD-1 binding interface in each monomer, thereby inhibiting the PD-1/PD-L1 axis. While the recently reported crystal structures of such small molecules bound to hPD-L1 reveal valuable insights regarding their molecular interactions, there is still limited information about the dynamics driving this unusual complex formation. The current study provides an in-depth computational structural analysis to study the interactions of five small molecule compounds in complex with hPD-L1. By employing a combination of molecular dynamic simulations, binding energy calculations and computational solvent mapping techniques, our analyses quantified the dynamic roles of different hydrophilic and lipophilic residues at the surface of hPD-L1 in mediating these interactions. Furthermore, ligand-based analyses, including Free-Wilson 2D-QSAR was conducted to quantify the impact of R-group substitutions at different sites of the phenoxy-methyl biphenyl core. Our results emphasize the importance of a terminal phenyl ring that must be present in any hPD-L1 small molecule inhibitor. This phenyl moiety overlaps with a very unfavorable hydration site, which can explain the ability of such small molecules to trigger hPD-L1 dimerization.

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.

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.

Fumarato and Phthalato Bridged Dinuclear Metal-Organic Cu(II) and Mn(II) Compounds involving Infinite Fumarate-water Assemblies and Unusual Structure-guiding H-bonded Synthons: Antiproliferative Evaluation and Theoretical Studies

Two new dinuclear coordination compounds viz. [Cu2(µ-fum)(phen)2(H2O)6](fum)•6H2O (1) and [Mn2(µ-phth)2(phen)4]•2H2O (2) (phen = 1,10-phenanthroline, fum = fumarate and phth = phthalate) have been synthesized and characterized by elemental analysis, single...

Characterizing the potentially neuronal acetylcholinesterase reactivity toward chiral pyraclofos: Enantioselective insights from spectroscopy, in silico docking, molecular dynamics simulation and per-residue energy decomposition studies

Chiral organophosphorus agents are distributed ubiquitously in the environment, but the neuroactivity of these asymmetric chemicals to humans remains uncertain. This scenario was to explore the stereoselective neurobiological response of human acetylcholinesterase (AChE) to chiral pyraclofos at the enantiomeric scale, and then decipher the microscopic basis of enantioselective neurotoxicity of pyraclofos enantiomers. The results indicated that (R)-/(S)-pyraclofos can form the bioconjugates with AChE with a stoichiometric ratio of 1:1, but the neuronal affinity of (R)-pyraclofos (K = 6.31 × 10⁴ M^-1) with AChE was larger than that of (S)-pyraclofos (K = 1.86 × 10⁴ M^-1), and significant enantioselectivity was existed in the biochemical reaction. The modes of neurobiological action revealed that pyraclofos enantiomers were situated at the substrate binding domain, and the strength of the overall noncovalent bonds between (S)-pyraclofos and the residues was weaker than that of (R)-pyraclofos, resulting in the high inhibitory effect of (R)-pyraclofos toward the activity of AChE. Dynamic enantioselective biointeractions illustrated that the intervention of inherent conformational flexibility in the AChE-(R)-pyraclofos was greater than that of the AChE-(S)-pyraclofos, which arises from the big spatial displacement and the conformational flip of the binding domain composed of the residues Thr-64～Asn-89, Gly-122～Asp-134, and Thr-436～Tyr-449. Energy decomposition exhibited that the Gibbs free energies of the AChE-(R)-/(S)-pyraclofos were ΔG° = －37.4/－30.2 kJ mol^-1, respectively, and the disparity comes from the electrostatic energy during the stereoselective neurochemical reactions. Quantitative conformational analysis further confirmed the atomic-scale computational chemistry conclusions, and the perturbation of (S)-pyraclofos on the AChE's ordered conformation was lower than that of (R)-pyraclofos, which is germane to the interaction energies of the crucial residues, e.g. Tyr-124, Tyr-337, Asp-74, Trp-86, and Tyr-119. Evidently, this attempt will contribute mechanistic information to uncovering the neurobiological effects of chiral organophosphates on the body.

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.

Addressing a Trapped High-Energy Water: Design and Synthesis of Highly Potent Pyrimidoindole-Based Glycogen Synthase Kinase-3β Inhibitors

In small molecule binding, water is not a passive bystander but rather takes an active role in the binding site, which may be decisive for the potency of the inhibitor. Here, by addressing a high-energy water, we improved the IC₅₀ value of our co-crystallized glycogen synthase kinase-3β (GSK-3β) inhibitor by nearly two orders of magnitude. Surprisingly, our results demonstrate that this high-energy water was not displaced by our potent inhibitor (S)-3-(3-((7-ethynyl-9H-pyrimido[4,5-b]indol-4-yl)(methyl)amino)piperidin-1-yl)propanenitrile ((S)-15, IC₅₀ value of 6 nM). Instead, only a subtle shift in the location of this water molecule resulted in a dramatic decrease in the energy of this high-energy hydration site, as shown by the WaterMap analysis combined with microsecond timescale molecular dynamics simulations. (S)-15 demonstrated both a favorable kinome selectivity profile and target engagement in a cellular environment and reduced GSK-3 autophosphorylation in neuronal SH-SY5Y cells. Overall, our findings highlight that even a slight adjustment in the location of a high-energy water can be decisive for ligand binding.

Factors Governing Selectivity of Dopamine Receptor Binding Compounds for D2R and D3R Subtypes

Targeting the D3 dopamine receptor (D3R) is a promising pharmacotherapeutic strategy for the treatment of many disorders. The structure of the D3R is similar to the D2 dopamine receptor (D2R), especially in the transmembrane spanning regions that form the orthosteric binding site, making it difficult to identify D3R selective pharmacotherapeutic agents. Here, we examine the molecular basis for the high affinity D3R binding and D3R vs D2R binding selectivity of substituted phenylpiperazine thiopheneamides. We show that removing the thiophenearylamide portion of the ligand consistently decreases the affinity of these ligands at D3R, while not affecting their affinity at the D2R. Our long (>10 μs) molecular dynamics simulations demonstrated that both dopamine receptor subtypes adopt two major conformations that we refer to as closed or open conformations, with D3R sampling the open conformation more frequently than D2R. The binding of ligands with conjoined orthosteric-allosteric binding moieties causes the closed conformation to populate more often in the trajectories. Also, significant differences were observed in the extracellular loops (ECL) of these two receptor subtypes leading to the identification of several residues that contribute differently to the ligand binding for the two receptors that could potentially contribute to ligand binding selectivity. Our observations also suggest that the displacement of ordered water in the binding pocket of D3R contributes to the affinity of the compounds containing an allosteric binding motif. These studies provide a better understanding of how a bitopic mode of engagement can determine ligands that bind selectively to D2 and D3 dopamine receptor subtypes.

Molecular Boronic Acid-Based Saccharide Sensors

Boronic acids can reversibly bind diols, a molecular feature that is ubiquitous within saccharides, leading to their use in the design and implementation of sensors for numerous saccharide species. There is a growing understanding of the importance of saccharides in many biological processes and systems; while saccharide or carbohydrate sensing in medicine is most often associated with detection of glucose in diabetes patients, saccharides have proven to be relevant in a range of disease states. Herein the relevance of carbohydrate sensing for biomedical applications is explored, and this review seeks to outline how the complexity of saccharides presents a challenge for the development of selective sensors and describes efforts that have been made to understand the underpinning fluorescence and binding mechanisms of these systems, before outlining examples of how researchers have used this knowledge to develop ever more selective receptors.

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.

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.

The critical role of QM/MM X-ray refinement and accurate tautomer/protomer determination in structure-based drug design

Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule—along with any bound ligand(s)—within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method—coupled with our XModeScore method for experimental tautomer/protomer state determination—to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.

MovableType Software for Fast Free Energy-Based Virtual Screening: Protocol Development, Deployment, Validation, and Assessment

For decades, the complicated energy surfaces found in macromolecular protein:ligand structures, which require large amounts of computational time and resources for energy state sampling, have been an inherent obstacle to fast, routine free energy estimation in industrial drug discovery efforts. Beginning in 2013, the Merz research group addressed this cost with the introduction of a novel sampling methodology termed “Movable Type” (MT). Using numerical integration methods, the MT method reduces the computational expense for energy state sampling by independently calculating each atomic partition function from an initial molecular conformation in order to estimate the molecular free energy using ensembles of the atomic partition functions. In this work, we report a software package, the DivCon Discovery Suite with the MovableType module from QuantumBio Inc., that performs this MT free energy estimation protocol in a fast, fully encapsulated manner. We discuss the computational procedures and improvements to the original work, and we detail the corresponding settings for this software package. Finally, we introduce two validation benchmarks to evaluate the overall robustness of the method against a broad range of protein:ligand structural cases. With these publicly available benchmarks, we show that the method can use a variety of input types and parameters and exhibits comparable predictability whether the method is presented with “expensive” X-ray structures or “inexpensively docked” theoretical models. We also explore some next steps for the method. The MovableType software is available at http://www.quantumbioinc.com/

Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

ProBiS H2O MD Approach for Identification of Conserved Water Sites in Protein Structures for Drug Design

The ProBiS H2O MD approach for identification of conserved waters and water sites of interest in macromolecular systems, which is becoming a typical step in a structure-based drug design or macromolecular study in general, is described. This work explores an extension of the ProBiS H2O approach introduced by Jukič et al. Indeed, water molecules are key players in the interaction mechanisms of macromolecules and small molecules and play structural roles. Our earlier developed approach, ProBiS H2O, is a simple and transparent workflow for conserved water detection. Here we have considered generalizing the idea by supplementing the experimental data with data derived from molecular dynamics to facilitate work on less known systems. Newly developed ProBiS H2O MD workflow uses trajectory data, extracts and identifies interesting water sites, and visualizes the results. ProBiS H2O MD can thus robustly process molecular dynamic trajectory snapshots, perform local superpositions, collect water location data, and perform density-based clustering to identify discrete sites with high conservation of water molecules. This is a new approach that uses experimental data in silico to identify interesting water sites. Methodology is fast and water-model or molecular dynamics software independent. Trends in the conservation of water molecules can be followed over a variety of trajectories, and our approach has been successfully validated using reported protein systems with experimentally observed conserved water molecules. ProBiS H2O MD is freely available as PyMOL plugin at http://insilab.org.

Enantioselective Assembly of Cycloenones with a Nitrile-Containing All-Carbon Quaternary Center from Malononitriles Enabled by Ni Catalysis

Chiral nitriles are valuable molecules in modern organic synthesis and drug discovery. Selectively differentiating the two nitrile groups of widely available malononitrile derivatives is a straightforward yet underdeveloped route to construct enantioenriched nitriles. Here we report an enantioselective nickel-catalyzed desymmetrization of malononitriles for the generation of nitrile-containing all-carbon quaternary stereocenters. This protocol involves a nickel-catalyzed addition of aryl boronic acids to alkynes, followed by a selective nitrile insertion, providing unprecedented access to enantioenriched 5-7-membered α-cyano-cycloenones with a fully substituted olefin from a broad range of substrates. The synthetic utility of these nitrile products is demonstrated by gram-scale synthesis and conversion to several useful functional groups.

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

Best Practices for Alchemical Free Energy Calculations [Article v1.0]

Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing alchemical intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations performed with equilibrium simulations, in particular relative and absolute small molecule binding free energy calculations to biomolecular targets.

Adipato bridged novel hexanuclear Cu(ii) and polymeric Co(ii) coordination compounds involving cooperative supramolecular assemblies and encapsulated guest water clusters in a square grid host: antiproliferative evaluation and theoretical studies

Novel adipato bridged Cu(ii) and Co(ii) complexes synthesized by considering cytotoxicity, apoptosis, ROS generation, molecular docking and pharmacophore features.

Ligand Binding Thermodynamic Cycles: Hysteresis, the Locally Weighted Histogram Analysis Method, and the Overlapping States Matrix

Free energy perturbation (FEP) simulations have been widely applied to obtain predictions of the relative binding free energy for a series of congeneric ligands binding to the same receptor, which is an essential component for the lead optimization process in computer-aided drug discovery. In the case of several congeneric ligands forming a perturbation map involving a closed thermodynamic cycle, the summation of the estimated free energy change along each edge in the cycle using Bennett acceptance ratio (BAR) usually will deviate from zero due to systematic and random errors, which is the hysteresis of cycle closure. In this work, the advanced reweighting techniques binless weighted histogram analysis method (UWHAM) and locally weighted histogram analysis method (LWHAM) are applied to provide statistical estimators of the free energy change along each edge in order to eliminate the hysteresis effect. As an example, we analyze a closed thermodynamic cycle involving four congeneric ligands which bind to HIV-1 integrase, a promising target which has emerged for antiviral therapy. We demonstrate that, compared with FEP and BAR, more accurate and hysteresis-free estimates of free energy differences can be achieved by using UWHAM to find a single estimate of the density of states based on all of the data in the cycle. Furthermore, by comparison of LWHAM results obtained from the inclusion of different numbers of neighboring states with UWHAM estimation involving all the states, we show how to determine the optimal neighborhood size in the LWHAM analysis to balance the trade-offs between computational cost and accuracy of the free energy prediction. Even with the smallest neighborhood, LWHAM can improve the BAR free energy estimates using the same input data as BAR. We introduce an overlapping states matrix that is constructed by using the global jump formula of LWHAM and plot its heat map. The heat map provides a quantitative measure of the overlap between pairs of alchemical/thermodynamic states. We explain how to identify and improve the FEP calculations along the edges that most likely cause large systematic errors by using the heat map of the overlapping states matrix and by comparing the BAR and UWHAM estimates of the free energy change.

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.

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.

A Fragmenting Protocol with Explicit Hydration for Calculation of Binding Enthalpies of Target-Ligand Complexes at a Quantum Mechanical Level

Optimization of the enthalpy component of binding thermodynamics of drug candidates is a successful pathway of rational molecular design. However, the large size and missing hydration structure of target-ligand complexes often hinder such optimizations with quantum mechanical (QM) methods. At the same time, QM calculations are often necessitated for proper handling of electronic effects. To overcome the above problems, and help the QM design of new drugs, a protocol is introduced for atomic level determination of hydration structure and extraction of structures of target-ligand complex interfaces. The protocol is a combination of a previously published program MobyWat, an engine for assigning explicit water positions, and Fragmenter, a new tool for optimal fragmentation of protein targets. The protocol fostered a series of fast calculations of ligand binding enthalpies at the semi-empirical QM level. Ligands of diverse chemistry ranging from small aromatic compounds up to a large peptide helix of a molecular weight of 3000 targeting a leukemia protein were selected for systematic investigations. Comparison of various combinations of implicit and explicit water models demonstrated that the presence of accurately predicted explicit water molecules in the complex interface considerably improved the agreement with experimental results. A single scaling factor was derived for conversion of QM reaction heats into binding enthalpy values. The factor links molecular structure with binding thermodynamics via QM calculations. The new protocol and scaling factor will help automated optimization of binding enthalpy in future molecular design projects.

Water in protein hydration and ligand recognition

This review describes selected basics of water in biomolecular recognition. We focus on a qualitative understanding of the most important physical aspects, how these change in magnitude between bulk water and protein environment, and how the roles that water plays for proteins arise from them. These roles include mechanical support, thermal coupling, dielectric screening, mass and charge transport, and the competition with a ligand for the occupation of a binding site. The presence or absence of water has ramifications that range from the thermodynamic binding signature of a single ligand up to cellular survival. The large inhomogeneity in water density, polarity and mobility around a solute is hard to assess in experiment. This is a source of many difficulties in the solvation of protein models and computational studies that attempt to elucidate or predict ligand recognition. The influence of water in a protein binding site on the experimental enthalpic and entropic signature of ligand binding is still a point of much debate. The strong water‐water interaction in enthalpic terms is counteracted by a water molecule's high mobility in entropic terms. The complete arrest of a water molecule's mobility sets a limit on the entropic contribution of a water displacement process, while the solvent environment sets limits on ligand reactivity.

Development of novel theranostic agents for in vivo amyloid imaging and protective effects on human neuroblastoma cells

Brain amyloid deposits have been identified as the main neuropathological hallmarks of Alzheimer's diseases (AD) and intensive efforts have been devoted to develop aggregation inhibitors preventing the formation of toxic oligomeric Aβ for therapeutic. In addition, evidence indicates that the formation and accumulation of β-amyloid plaques probably precede clinical symptoms by around 20 years and imaging of such plaques would be beneficial for early-stage AD detection. In this study, we investigated phenothiazine-based compounds as novel promising theranostic agents for AD. These multifunctional agents exhibited BBB permeability, low neurotoxicity, good bio-stability as well as strong turn-on fluorescence with a Stokes shift upon binding to Aβ aggregates. They had metal-chelating property which could delay Aβ aggregation and displayed high binding affinity for β-amyloid aggregates. Moreover, they have been simultaneously applied to perform in vivo near-infrared fluorescence imaging of β-amyloid plaques in double transgenic AD mouse model, to prevent self-aggregation of Aβ monomer from forming toxic oligomers and to protect human neuroblastoma SH-SY5Y cells against Aβ-induced toxicity and oxidative stress.

Replacement of Protein Binding-Site Waters Contributes to Favorable Halogen Bond Interactions

Halogen bond interaction between a protein electronegative atom and a ligand halogen atom is increasingly attracting attention in the field of structure-based drug design. Nevertheless, gaps in understanding make it desirable to better examine the role of forces governing the formation of favorable halogen bond interactions, and the development of effective and efficient computational approaches to "design in" favorable halogen bond interactions in lead optimization process are warranted. Here, we analyzed the binding-site water properties of crystal structures with characterized halogen bond interactions between ligand halogen atoms and protein backbone carbonyl groups and, thus, found that halogen atoms involved in halogen bond interactions frequently replace calculated binding-site waters upon ligand binding. Moreover, we observed that the preferential directionality of halogen bond interactions aligns well with the orientations of these replaced waters, and these replaced waters exhibited differential energetic characteristics as compared to waters that are displaced by halogen atoms that do not form halogen bond interactions. Our discovery that replacement of calculated binding-site waters contributes to the formation of favorable halogen bond interactions suggests a practical approach for rational drug design utilizing halogen bond interactions with protein backbone carbonyl groups.