Tautomerism in computer-aided drug design

Epik: pKa and Protonation State Prediction through Machine Learning

Epik version 7 is a software program that uses machine learning for predicting the pK_a values and protonation state distribution of complex, druglike molecules. Using an ensemble of atomic graph convolutional neural networks (GCNNs) trained on over 42,000 pK_a values across broad chemical space from both experimental and computed origins, the model predicts pK_a values with 0.42 and 0.72 pK_a unit median absolute and root mean square errors, respectively, across seven test sets. Epik version 7 also generates protonation states and recovers 95% of the most populated protonation states compared to previous versions. Requiring on average only 47 ms per ligand, Epik version 7 is rapid and accurate enough to evaluate protonation states for crucial molecules and prepare ultra-large libraries of compounds to explore vast regions of chemical space. The simplicity and time required for the training allow for the generation of highly accurate models customized to a program's specific chemistry.

Tautomerism unveils a self-inhibition mechanism of crystallization

Modifiers are commonly used in natural, biological, and synthetic crystallization to tailor the growth of diverse materials. Here, we identify tautomers as a new class of modifiers where the dynamic interconversion between solute and its corresponding tautomer(s) produces native crystal growth inhibitors. The macroscopic and microscopic effects imposed by inhibitor-crystal interactions reveal dual mechanisms of inhibition where tautomer occlusion within crystals that leads to natural bending, tunes elastic modulus, and selectively alters the rate of crystal dissolution. Our study focuses on ammonium urate crystallization and shows that the keto-enol form of urate, which exists as a minor tautomer, is a potent inhibitor that nearly suppresses crystal growth at select solution alkalinity and supersaturation. The generalizability of this phenomenon is demonstrated for two additional tautomers with relevance to biological systems and pharmaceuticals. These findings offer potential routes in crystal engineering to strategically control the mechanical or physicochemical properties of tautomeric materials.

Importance of tautomerism in drugs

Tautomerism is an important phenomenon exhibited by many drugs. As we discuss in this review, identifying the different tautomers of drugs and exploring their importance in the mechanisms of drug action are integral components of current drug discovery. Nuclear magnetic resonance (NMR), infrared (IR), ultraviolet (UV), Raman, and terahertz spectroscopic techniques, as well as X-ray diffraction, are useful for exploring drug tautomerism. Quantum chemical methods, in association with pharmacoinformatics tools, are being used to evaluate tautomeric preferences in terms of energy effects. Desmotropy (i.e., tautomeric polymorphism) of the drugs is particularly important in drug delivery studies.

Favipiravir-Tautomeric and Complexation Properties in Solution

The tautomeric properties of favipiravir were investigated experimentally for the first time by using molecular spectroscopy (UV-Vis absorption, fluorescence and NMR), as well as DFT quantum-chemical calculations. According to the obtained results, the enol tautomer is substantially more stable in most of the organic solvents. In the presence of water, a keto form appears to be favored due to the specific solute-solvent interactions. Upon the addition of alkaline-earth-metal ions, deprotonation and complexation occurred simultaneously, giving the formation of 2 : 1 ligand : metal complexes. According to the theoretical simulations, the metal ion is captured between the carbonyl groups as a result of the size-fit effect.

The Pseudo‐Natural Product Rhonin Targets RHOGDI

For the discovery of novel chemical matter generally endowed with bioactivity, strategies may be particularly efficient that combine previous insight about biological relevance, e.g., natural product (NP) structure, with methods that enable efficient coverage of chemical space, such as fragment‐based design. We describe the de novo combination of different 5‐membered NP‐derived N‐heteroatom fragments to structurally unprecedented “pseudo‐natural products” in an efficient complexity‐generating and enantioselective one‐pot synthesis sequence. The pseudo‐NPs inherit characteristic elements of NP structure but occupy areas of chemical space not covered by NP‐derived chemotypes, and may have novel biological targets. Investigation of the pseudo‐NPs in unbiased phenotypic assays and target identification led to the discovery of the first small‐molecule ligand of the RHO GDP‐dissociation inhibitor 1 (RHOGDI1), termed Rhonin. Rhonin inhibits the binding of the RHOGDI1 chaperone to GDP‐bound RHO GTPases and alters the subcellular localization of RHO GTPases.

Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution

The computation of tautomer ratios of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution. Unfortunately, accurate calculations of aqueous tautomer ratios—the degree to which these species must be penalized in order to correctly account for tautomers in modeling binding for computer-aided drug discovery—is surprisingly difficult. While quantum chemical approaches to computing aqueous tautomer ratios using continuum solvent models and rigid-rotor harmonic-oscillator thermochemistry are currently state of the art, these methods are still surprisingly inaccurate despite their enormous computational expense. Here, we show that a major source of this inaccuracy lies in the breakdown of the standard approach to accounting for quantum chemical thermochemistry using rigid rotor harmonic oscillator (RRHO) approximations, which are frustrated by the complex conformational landscape introduced by the migration of double bonds, creation of stereocenters, and introduction of multiple conformations separated by low energetic barriers induced by migration of a single proton. Using quantum machine learning (QML) methods that allow us to compute potential energies with quantum chemical accuracy at a fraction of the cost, we show how rigorous relative alchemical free energy calculations can be used to compute tautomer ratios in vacuum free from the limitations introduced by RRHO approximations. Furthermore, since the parameters of QML methods are tunable, we show how we can train these models to correct limitations in the underlying learned quantum chemical potential energy surface using free energies, enabling these methods to learn to generalize tautomer free energies across a broader range of predictions.

We show how alchemical free energies can be calculated with QML potentials to identify deficiencies in RRHO approximations for computing tautomeric free energies, and how these potentials can be learned from experiment to improve prediction accuracy.

Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies

An essential aspect for adequate predictions of chemical properties by machine learning models is the database used for training them. However, studies that analyze how the content and structure of the databases used for training impact the prediction quality are scarce. In this work, we analyze and quantify the relationships learned by a machine learning model (Neural Network) trained on five different reference databases (QM9, PC9, ANI-1E, ANI-1, and ANI-1x) to predict tautomerization energies from molecules in Tautobase. For this, characteristics such as the number of heavy atoms in a molecule, number of atoms of a given element, bond composition, or initial geometry on the quality of the predictions are considered. The results indicate that training on a chemically diverse database is crucial for obtaining good results and also that conformational sampling can partly compensate for limited coverage of chemical diversity. The overall best-performing reference database (ANI-1x) performs on average by 1 kcal/mol better than PC9, which, however, contains about 2 orders of magnitude fewer reference structures. On the other hand, PC9 is chemically more diverse by a factor of ∼5 as quantified by the number of atom-in-molecule-based fragments (amons) it contains compared with the ANI family of databases. A quantitative measure for deficiencies is the Kullback-Leibler divergence between reference and target distributions. It is explicitly demonstrated that when certain types of bonds need to be covered in the target database (Tautobase) but are undersampled in the reference databases, the resulting predictions are poor. Examples of this include the poor performance of all databases analyzed to predict C(sp²)-C(sp²) double bonds close to heteroatoms and azoles containing N-N and N-O bonds. Analysis of the results with a Tree MAP algorithm provides deeper understanding of specific deficiencies in predicting tautomerization energies by the reference datasets due to inadequate coverage of chemical space. Capitalizing on this information can be used to either improve existing databases or generate new databases of sufficient diversity for a range of machine learning (ML) applications in chemistry.

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.

VSPrep: A KNIME Workflow for the Preparation of Molecular Databases for Virtual Screening

Drug discovery is a challenging and expensive field. Hence, novel in silico tools have been developed in early discovery stage to identify and prioritize novel molecules with suitable physicochemical properties. In many in silico drug design projects, molecular databases are screened by virtual screening tools to search for potential bioactive molecules. The preparation of the molecules is therefore a key step in the success of well-established techniques such as docking, similarity or pharmacophore searching. We review here the lists of several toolkits used in different steps during the cleaning of molecular databases, integrated within a KNIME workflow. During the first step of the automatic workflow, salts are removed, and mixtures are split to get one compound per entry. Then compounds with unwanted features are filtered. Duplicated entries are then deleted while considering stereochemistry. As a compromise between exhaustiveness and computational time, most distributed tautomers at physiological pH are computed. Additionally, various flags are applied to molecules by using either classical molecular descriptors, similarity search to known libraries or substructure search rules. Moreover, stereoisomers are enumerated depending on the unassigned chiral centers. Then, three-dimensional coordinates, and optionally conformers, are generated. This workflow has been already applied to several drug design projects and can be used for molecular database preparation upon request.

Novel mechanisms of the conformational transformations of the biologically important G·C nucleobase pairs in Watson–Crick, Hoogsteen and wobble configurations via the mutual rotations of the bases around the intermolecular H-bonds: a QM/QTAIM study

It was established conformational transformations of the G·C nucleobase pairs, occurring via the mutual rotation of the G and C bases around the intermolecular H-bonds.

When biomolecules meet 2-hydrazinopyrazine: from theory through experiment to molecular levels using a wide spectrum of techniques

The design of drug structures that are non-toxic, easily transported and permeable to cellular barriers is currently one of the most growing research trends. Indeed, the structural similarity of 2-hydrazinopyrazine (2HP) to pyrazinamide, which has been successfully used in anti-tuberculosis therapy, makes 2HP a promising research object. Thus, herein, a complete analysis of the structure of 2HP and its physicochemical and cytotoxic properties was performed. Calf thymus DNA (CT-DNA) and bovine serum albumin (BSA) binding studies were conducted, which demonstrated the higher affinity of 2HP to BSA. Furthermore, cytotoxicity tests were performed, which proved that 2HP was non-toxic to human skin keratinocyte cells. Accordingly, 2HP was initially classified as a compound with potential application. Physicochemical investigations were performed using a wide range of experiments, which were supported by DFT calculations using the B3LYP functional and 6-311+G** basis set. The good correlation, high quality and correctness of the obtained parameters were proven although the data was obtained using independent techniques. Additionally, 42 tautomeric (prototrophic) forms of 2HP were found by searching the conformational hyperspace. The most energy stable 2HP conformer structure and the partial charge distribution were established. The preferred 2HP ionic forms preferred were presented, and models of the equilibrium occurring in aqueous solution were proposed. The hydrophilic character of 2HP was established based on the partition coefficient values determined by both experiment and theory. The PCM and SMD solvent models of water and n-octanol were used.

Structure, ionic forms, hydrophilic and non-cytotoxic character of 2-hydrazinopyrazine with stronger affinity to BSA than DNA were proved.

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/

Tautomer Standardization in Chemical Databases: Deriving Business Rules from Quantum Chemistry

Databases of small, potentially bioactive molecules are ubiquitous across the industry and academia. Designed such that each unique compound should appear only once, the multiplicity of ways in which many compounds can be represented means that these databases require methods for standardizing the representation of chemistry. This is commonly achieved through the use of "Chemistry Business Rules", sets of predefined rules that describe the "house style" of the database in question. At Syngenta, the historical approach to the design of chemistry business rules has been to focus on consistency of representation, with chemical relevance given secondary consideration. In this work, we overturn that convention. Through the use of quantum chemistry calculations, we define a set of chemistry business rules for tautomer standardization that reproduces gas-phase energetic preferences. We go on to show that, compared to our historic approach, this method yields tautomers that are in better agreement with those observed experimentally in condensed phases and that are better suited for use in predictive models.

Pattern-free generation and quantum mechanical scoring of ring-chain tautomers

In contrast to the computational generation of conventional tautomers, the analogous operation that would produce ring-chain tautomers is rarely available in cheminformatics codes. This is partly due to the perceived unimportance of ring-chain tautomerism and partly because specialized algorithms are required to realize the non-local proton transfers that occur during ring-chain rearrangement. Nevertheless, for some types of organic compounds, including sugars, warfarin analogs, fluorescein dyes and some drug-like compounds, ring-chain tautomerism cannot be ignored. In this work, a novel ring-chain tautomer generation algorithm is presented. It differs from previously proposed solutions in that it does not rely on hard-coded patterns of proton migrations and bond rearrangements, and should therefore be more general and maintainable. We deploy this algorithm as part of a workflow which provides an automated solution for tautomer generation and scoring. The workflow identifies protonatable and deprotonatable sites in the molecule using a previously described approach based on rapid micro-pK_a prediction. These data are used to distribute the active protons among the protonatable sites exhaustively, at which point alternate resonance structures are considered to obtain pairs of atoms with opposite formal charge. These pairs are connected with a single bond and a 3D undistorted geometry is generated. The scoring of the generated tautomers is performed with a subsequent density functional theory calculation employing an implicit solvent model. We demonstrate the performance of our workflow on several types of organic molecules known to exist in ring-chain tautomeric equilibria in solution. In particular, we show that some ring-chain tautomers not found using previously published algorithms are successfully located by ours.

Favipiravir tautomerism: a theoretical insight

There is no experimental information about the tautomerism of Favipiravir (T-705). Therefore, its tautomeric state was predicted by using density functional theory in gas phase and in solution (toluene, acetonitrile and water). The results have shown that, in neutral state, the enol form is strongly dominating in both gas phase and solution. The carboxamide group is easily protonated in the presence of acid, which leads to shift of the tautomeric equilibrium toward the keto tautomer. In order to validate the theoretical predictions, 2-hydroxy pyridine and 2-hydroxy pyrazine were also included in the set of studied compounds. The available experimental data about their tautomerism are in very good agreement with the theoretical predictions, which validate the conclusions made for T-705.

Automated exploration of the low-energy chemical space with fast quantum chemical methods

We propose and discuss an efficient scheme for the in silico sampling for parts of the molecular chemical space by semiempirical tight-binding methods combined with a meta-dynamics driven search algorithm. The focus of this work is set on the generation of proper thermodynamic ensembles at a quantum chemical level for conformers, but similar procedures for protonation states, tautomerism and non-covalent complex geometries are also discussed. The conformational ensembles consisting of all significantly populated minimum energy structures normally form the basis of further, mostly DFT computational work, such as the calculation of spectra or macroscopic properties. By using basic quantum chemical methods, electronic effects or possible bond breaking/formation are accounted for and a very reasonable initial energetic ranking of the candidate structures is obtained. Due to the huge computational speedup gained by the fast low-cost quantum chemical methods, overall short computation times even for systems with hundreds of atoms (typically drug-sized molecules) are achieved. Furthermore, specialized applications, such as sampling with implicit solvation models or constrained conformational sampling for transition-states, metal-, surface-, or noncovalently bound complexes are discussed, opening many possible applications in modern computational chemistry and drug discovery. The procedures have been implemented in a freely available computer code called CREST, that makes use of the fast and reliable GFNn-xTB methods.

A dielectric and spectrophotometric study of the tautomerization of 2-hydroxypyridine and 2-mercaptopyridine in water

The basic ionization (pk₁) and acidic ionization (pk₂) constants and equilibrium constant (K_T) of 2HPy and 2MPy were determined. The pk₁(s) of their N- and X-methyl derivatives (X = O, S) were also determined. The equilibrium constant of 2MPy is approximately 60 times greater than its oxygen analog, 2HPy. The micro-ionization constants of the functional groups, –NH (pk_A and pk_C) and –XH (pk_B and pk_D), were determined to provide further insights into the ionization equilibria of these N-heteroaromatic XH compounds (2HPy and 2MPy). The relaxation time of water (τ) in aqueous solutions of 2HPy and 2MPy are collectively used with the K_T values to determine the forward (k_f) and backward (k_b) rate constants of tautomerization. Subsequently, the k_f and k_b are used to provide the rationale for the K_T and τ values.

The basic ionization (pk₁) and acidic ionization (pk₂) constants and equilibrium constant (K_T) of 2HPy and 2MPy were determined.

Generation of Tautomers Using Micro-p Ka's

Solutions of organic molecules containing one or more heterocycles with conjugated bonds may exist as a mixture of tautomers, but typically only a few of them are significantly populated even though the potential number grows combinatorially with the number of protonation and deprotonation sites. Generating the most stable tautomers from a given input structure is an important and challenging task, and numerous algorithms to tackle it have been proposed in the literature. This work describes a novel approach for tautomer prediction that involves the combined use of molecular mechanics, semiempirical quantum chemistry, and density functional theory. The key idea in our method is to identify the protonation and deprotonation sites using estimated micro-p K_a's for every atom in the molecule as well as in its nearest protonated and deprotonated forms. To generate tautomers in a systematic way with minimal bias, we then consider the full set of tautomers that arise from the combinatorial distribution of all such mobile protons among all protonatable sites, with efficient postprocessing to screen away high-energy species. To estimate the micro-p K_a's, we present a new method designed for the current task, but we emphasize that any alternative method can be used in conjunction with our basic algorithm. Our approach is therefore grounded in the computational prediction of physical properties in aqueous solution, in contrast to other approaches that may rely on the use of hard-coded rules of proton distribution, previously observed tautomerization patterns from a known chemical space, or human input. We present examples of the application of our algorithm to organic and drug-like molecules, with a focus on novel structures where traditional methods are expected to perform worse.

PubChem chemical structure standardization

BACKGROUND

PubChem is a chemical information repository, consisting of three primary databases: Substance, Compound, and BioAssay. When individual data contributors submit chemical substance descriptions to Substance, the unique chemical structures are extracted and stored into Compound through an automated process called structure standardization. The present study describes the PubChem standardization approaches and analyzes them for their success rates, reasons that cause structures to be rejected, and modifications applied to structures during the standardization process. Furthermore, the PubChem standardization is compared to the structure normalization of the IUPAC International Chemical Identifier (InChI) software, as manifested by conversion of the InChI back into a chemical structure.

RESULTS

The observed rejection rate for substances processed by PubChem standardization was 0.36%, which is predominantly attributed to structures with invalid atom valences that cannot be readily corrected without additional information from contributors. Of all structures that pass standardization, 44% are modified in the process, reducing the count of unique structures from 53,574,724 in substance to 45,808,881 in compound as identified by de-aromatized canonical isomeric SMILES. Even though the processing time is very low on average (only 0.4% of structures have individual standardization time above 0.1 s), total standardization time is completely dominated by edge cases: 90% of the time to standardize all structures in PubChem substance is spent on the 2.05% of structures with the highest individual standardization time. It is worth noting that 60% of the structures obtained from PubChem structure standardization are not identical to the chemical structure resulting from the InChI (primarily due to preferences for a different tautomeric form).

CONCLUSIONS

Standardization of chemical structures is complicated by the diversity of chemical information and their representations approaches. The PubChem standardization is an effective and efficient tool to account for molecular diversity and to eliminate invalid/incomplete structures. Further development will concentrate on improved tautomer consideration and an expanded stereocenter definition. Modifications are difficult to thoroughly validate, with slight changes often affecting many thousands of structures and various edge cases. The PubChem structure standardization service is accessible as a public resource ( https://pubchem.ncbi.nlm.nih.gov/standardize ), and via programmatic interfaces.

Molecular structure of clonidine: gas-phase electron diffraction, single-crystal X-ray diffraction and quantum chemical studies

This study presents the first determination of the molecular structure of clonidine using gas electron diffraction, X-ray diffraction and quantum chemical calculations.

Experimental and Chemoinformatics Study of Tautomerism in a Database of Commercially Available Screening Samples

We investigated how many cases of the same chemical sold as different products (at possibly different prices) occurred in a prototypical large aggregated database and simultaneously tested the tautomerism definitions in the chemoinformatics toolkit CACTVS. We applied the standard CACTVS tautomeric transforms plus a set of recently developed ring-chain transforms to the Aldrich Market Select (AMS) database of 6 million screening samples and building blocks. In 30 000 cases, two or more AMS products were found to be just different tautomeric forms of the same compound. We purchased and analyzed 166 such tautomer pairs and triplets by 1H and 13C NMR to determine whether the CACTVS transforms accurately predicted what is the same "stuff in the bottle". Essentially all prototropic transforms with examples in the AMS were confirmed. Some of the ring-chain transforms were found to be too "aggressive", i.e. to equate structures with one another that were different compounds.

Structure-Based Virtual Screening Protocol for in Silico Identification of Potential Thyroid Disrupting Chemicals Targeting Transthyretin

Thyroid disruption by xenobiotics is associated with a broad spectrum of severe adverse outcomes. One possible molecular target of thyroid hormone disrupting chemicals (THDCs) is transthyretin (TTR), a thyroid hormone transporter in vertebrates. To better understand the interactions between TTR and THDCs, we determined the crystallographic structures of human TTR in complex with perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and 2,2',4,4'-tetrahydroxybenzophenone (BP2). The molecular interactions between the ligands and TTR were further characterized using molecular dynamics simulations. A structure-based virtual screening (VS) protocol was developed with the intention of providing an efficient tool for the discovery of novel TTR-binders from the Tox21 inventory. Among the 192 predicted binders, 12 representatives were selected, and their TTR binding affinities were studied with isothermal titration calorimetry, of which seven compounds had binding affinities between 0.26 and 100 μM. To elucidate structural details in their binding to TTR, crystal structures were determined of TTR in complex with four of the identified compounds including 2,6-dinitro-p-cresol, bisphenol S, clonixin, and triclopyr. The compounds were found to bind in the TTR hormone binding sites as predicted. Our results show that the developed VS protocol is able to successfully identify potential THDCs, and we suggest that it can be used to propose THDCs for future toxicological evaluations.

Binding Kinetics in Drug Discovery

Over the last years, researchers have increasingly become interested in measuring and understanding drugs' binding kinetics, namely the time in which drug and its target associate and dissociate. Historically, drug discovery programs focused on the optimization of target affinity as a proxy of in-vivo efficacy. However, often the efficacy of a ligand is not appropriately described by the in-vitro measured drug-receptor affinity, but rather depends on the lifetime of the in-vivo drug-receptor interaction. In this review we review recent works that highlight the importance of binding kinetics, molecular determinants for rational optimization and the recent emergence of computational methods as powerful tools in measuring and understanding binding kinetics.

XModeScore: a novel method for accurate protonation/tautomer-state determination using quantum-mechanically driven macromolecular X-ray crystallographic refinement

Gaining an understanding of the protein-ligand complex structure along with the proper protonation and explicit solvent effects can be important in obtaining meaningful results in structure-guided drug discovery and structure-based drug discovery. Unfortunately, protonation and tautomerism are difficult to establish with conventional methods because of difficulties in the experimental detection of H atoms owing to the well known limitations of X-ray crystallography. In the present work, it is demonstrated that semiempirical, quantum-mechanics-based macromolecular crystallographic refinement is sensitive to the choice of a protonation-state/tautomer form of ligands and residues, and can therefore be used to explore potential states. A novel scoring method, called XModeScore, is described which enumerates the possible protomeric/tautomeric modes, refines each mode against X-ray diffraction data with the semiempirical quantum-mechanics (PM6) Hamiltonian and scores each mode using a combination of energetic strain (or ligand strain) and rigorous statistical analysis of the difference electron-density distribution. It is shown that using XModeScore it is possible to consistently distinguish the correct bound protomeric/tautomeric modes based on routine X-ray data, even at lower resolutions of around 3 Å. These X-ray results are compared with the results obtained from much more expensive and laborious neutron diffraction studies for three different examples: tautomerism in the acetazolamide ligand of human carbonic anhydrase II (PDB entries 3hs4 and 4k0s), tautomerism in the 8HX ligand of urate oxidase (PDB entries 4n9s and 4n9m) and the protonation states of the catalytic aspartic acid found within the active site of an aspartic protease (PDB entry 2jjj). In each case, XModeScore applied to the X-ray diffraction data is able to determine the correct protonation state as defined by the neutron diffraction data. The impact of QM-based refinement versus conventional refinement on XModeScore is also discussed.

LOX1 inhibition with small molecules

Lipoxygenases (LOXs) are nonheme, iron-containing dioxygenases that catalyze the dioxygenation of polyunsaturated fatty acids and are widely distributed among plant and animal species. Human LOXs, now identified as key enzymes in the pathogenesis of major disorders, have increasingly drawn the attention as targets and great effort has been made for the discovery and design of suitable inhibitors, to which end both pharmacological and computational methods have been employed. In the present work, using pharmacophore modeling and docking, we attempt to elucidate the inhibition of LOX1 with a new inhibitor, albidoside, an iridoid glucoside isolated from plants of the Scutellaria genus. Through a pharmacophore approach, complementarities between the ligand and the binding site are explored and a plausible mode of binding with the protein is suggested for albidoside.

A subset of N-substituted phenothiazines inhibits NADPH oxidases

NADPH oxidases (NOXs) constitute a family of enzymes generating reactive oxygen species (ROS) and are increasingly recognized as interesting drug targets. Here we investigated the effects of 10 phenothiazine compounds on NOX activity using an extensive panel of assays to measure production of ROS (Amplex red, WST-1, MCLA) and oxygen consumption. Striking differences between highly similar phenothiazines were observed. Two phenothiazines without N-substitution, including ML171, did not inhibit NOX enzymes, but showed assay interference. Introduction of an aliphatic amine chain on the N atom of the phenothiazine B ring (promazine) conferred inhibitory activity toward NOX2, NOX4, and NOX5 but not NOX1 and NOX3. Addition of an electron-attracting substituent in position 2 of the C ring extended the inhibitory activity to NOX1 and NOX3, with thioridazine being the most potent inhibitor. In contrast, the presence of a methylsulfoxide group at the same position (mesoridazine) entirely abolished NOX-inhibitory activity. A cell-free NOX2 assay suggested that inhibition by N-substituted phenothiazines was not due to competition with NADPH. A functional implication of NOX-inhibitory activity of thioridazine was demonstrated by its ability to block redox-dependent myofibroblast differentiation. Our results demonstrate that NOX-inhibitory activity is not a common feature of all antipsychotic phenothiazines and that substitution on the B-ring nitrogen is crucial for the activity, whereas that on the second position of the C ring modulates it. Our findings contribute to a better understanding of NOX pharmacology and might pave the path to discovery of more potent and selective NOX inhibitors.

Applying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalization

Background

Structure-based virtual screening techniques can help to identify new lead structures and complement other screening approaches in drug discovery. Prior to docking, the data (protein crystal structures and ligands) should be prepared with great attention to molecular and chemical details.

Results

Using a subset of 18 diverse targets from the recently introduced DEKOIS 2.0 benchmark set library, we found differences in the virtual screening performance of two popular docking tools (GOLD and Glide) when employing two different commercial packages (e.g. MOE and Maestro) for preparing input data. We systematically investigated the possible factors that can be responsible for the found differences in selected sets. For the Angiotensin-I-converting enzyme dataset, preparation of the bioactive molecules clearly exerted the highest influence on VS performance compared to preparation of the decoys or the target structure. The major contributing factors were different protonation states, molecular flexibility, and differences in the input conformation (particularly for cyclic moieties) of bioactives. In addition, score normalization strategies eliminated the biased docking scores shown by GOLD (ChemPLP) for the larger bioactives and produced a better performance. Generalizing these normalization strategies on the 18 DEKOIS 2.0 sets, improved the performances for the majority of GOLD (ChemPLP) docking, while it showed detrimental performances for the majority of Glide (SP) docking.

Conclusions

In conclusion, we exemplify herein possible issues particularly during the preparation stage of molecular data and demonstrate to which extent these issues can cause perturbations in the virtual screening performance. We provide insights into what problems can occur and should be avoided, when generating benchmarks to characterize the virtual screening performance. Particularly, careful selection of an appropriate molecular preparation setup for the bioactive set and the use of score normalization for docking with GOLD (ChemPLP) appear to have a great importance for the screening performance. For virtual screening campaigns, we recommend to invest time and effort into including alternative preparation workflows into the generation of the master library, even at the cost of including multiple representations of each molecule.

Graphical Abstract

Azine or hydrazone? The dilemma in amidinohydrazones

Amidinohydrazone, an important class of biologically active molecules, is generally represented as a hydrazone. This moiety prefers to exist in its azine tautomeric state and hence, influences the physical, chemical and receptor binding properties.

Computationally motivated synthesis and enzyme kinetic evaluation of N-(β-d-glucopyranosyl)-1,2,4-triazolecarboxamides as glycogen phosphorylase inhibitors

N-(β-d-Glucopyranosyl)-1,2,4-triazolecarboxamides discovered as low μM inhibitors of glycogen phosphorylase b.

Enumeration of ring-chain tautomers based on SMIRKS rules

A compound exhibits (prototropic) tautomerism if it can be represented by two or more structures that are related by a formal intramolecular movement of a hydrogen atom from one heavy atom position to another. When the movement of the proton is accompanied by the opening or closing of a ring it is called ring–chain tautomerism. This type of tautomerism is well observed in carbohydrates, but it also occurs in other molecules such as warfarin. In this work, we present an approach that allows for the generation of all ring–chain tautomers of a given chemical structure. Based on Baldwin’s Rules estimating the likelihood of ring closure reactions to occur, we have defined a set of transform rules covering the majority of ring–chain tautomerism cases. The rules automatically detect substructures in a given compound that can undergo a ring–chain tautomeric transformation. Each transformation is encoded in SMIRKS line notation. All work was implemented in the chemoinformatics toolkit CACTVS. We report on the application of our ring–chain tautomerism rules to a large database of commercially available screening samples in order to identify ring–chain tautomers.

Discovery of Most Stable Structures of Neutral and Anionic Phenylalanine through Automated Scanning of Tautomeric and Conformational Spaces

We have developed a software tool for combinatorial generation of tautomers and conformers of small molecules. We have demonstrated it by performing a systematic search for the most stable structures of neutral and anionic phenylalanine (Phe) using electronic structure methods. For the neutral canonical tautomer we found out that the conformers with and without the intramolecular (O)H···NH2 hydrogen bond are similarly stable, within the error bars of our method. A unique IR signature of the conformer without the hydrogen bond has been identified. We also considered anions of Phe, both valence type and dipole-bound. We have found out that tautomers resulting from proton transfer from the carboxylic OH to the phenyl ring do support valence anions that are vertically strongly bound, with electron vertical detachment energies (VDE) in a range of 3.2-3.5 eV. The most stable conformer of these valence anions remains adiabatically unbound with respect to the canonical neutral by only 2.17 kcal/mol at the CCSD(T)/aug-cc-pVDZ level. On the basis of our past experience with valence anions of nucleic acid bases, we suggest that the valence anions of Phe identified in this report can be observed experimentally. The most stable conformer of canonical Phe is characterized by an adiabatic electron affinity of 53 meV (a dipole-bound state).

Batch tautomer generation with MolTPC

Besides all their conformational degrees of freedom, drug-like molecules and natural products often also undergo tautomeric interconversions. Compared to the huge efforts made in experimental investigation of tautomerism, open and free algorithmic solutions for prototropic tautomer generation are surprisingly rare. The few freely available software packages limit their output to a subset of the possible configurational space by sometimes unwanted prior assumptions and complete neglection of ring-chain tautomerism. Here, we describe an adjustable fully automatic tautomer enumeration approach, which is freely available and also incorporates the detection of ring-chain variants. The algorithm is implemented in the MolTPC framework and accessible on SourceForge.

Determination of Tetracycline in Pharmaceutical Preparation by Molecular and Atomic Absorption Spectrophotometry and High Performance Liquid Chromatography via Complex Formation with Au(III) and Hg(II) Ions in Solutions

UV-visible and atomic spectrophotometry and HPLC techniques were applied for the determination of tetracycline (TC) in pharmaceutical preparations via complexation of the drug with Au(III) and Hg(II) ions in solutions. The mole ratio of TC to metal ions was 1 : 1. Maximum peak absorption at λ 425 and 320 nm for the two ions, respectively, was optimized at heating temperature 75°C for 15 minutes at pH = 4 followed by the extraction with ethyl acetate. The percentage of extraction and stability constants for the two complexes was 95.247, 95.335% and 2.518 × 10⁴, 1.162 × 10⁵ M⁻¹, respectively. HPLC method was applied without extraction process. The analytical data obtained from direct calibration curves of UV-visible absorption, FAAS, and HPLC for Au(III) complexes were recovery (100.78, 104.85, and 101.777%, resp.); detection limits (0.7403, 0.0997, and 2.647 μg/ml, resp.); linearity (5–70, 5–30, and 10–150 μg/ml, resp.), and correlation coefficient (0.9991, 0.9967, and 0.9986, resp.). The analytical data obtained from direct calibration curves for Hg(II) complexes by UV-visible spectrophotometry and HPLC were recovery (100.95 and 102.000%, resp.); detection limits (0.5867 and 2.532 μg/ml, resp.); linearity (5–70 and 10–150 μg/ml, resp.); and correlation coefficients (0.9989 and 0.9997, resp.).