Structure-Based Optimization of Inhibitors of the Aspartic Protease Endothiapepsin

Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin

Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.

pH-Dependent Structural Dynamics of Cathepsin D-Family Aspartic Peptidase of Clonorchis sinensis

Cathepsin D (CatD; EC 3.4.23.5) family peptidases of parasitic organisms are regarded as potential drug targets as they play critical roles in the physiology and pathobiology of parasites. Previously, we characterized the biochemical features of cathepsin D isozyme 2 (CatD2) in the carcinogenic liver fluke Clonorchis sinensis (CsCatD2). In this study, we performed all-atomic molecular dynamics simulations by applying different systems for the ligand-free/bound forms under neutral and acidic conditions to investigate the pH-dependent structural alterations and associated functional changes in CsCatD2. CsCatD2 showed several distinctive characteristics as follows: (1) acidic pH caused major conformational transitions from open to closed state in this enzyme; (2) during 30–36-ns simulations, acidic pH contributed significantly to the formation of rigid β-sheets around the catalytic residue Asp₂₁₉, higher occupancy (0% to 99%) of hydrogen bond than that of Asp₃₃, and enhanced stabilization of the CsCatD2-inhibtor complex; (3) neutral pH-induced displacement of the N-terminal part to hinder the accessibility of the active site and open allosteric site of this enzyme; and (4) the flap dynamics metrics, including distance (d₁), TriCα angles (θ₁ and θ₂), and dihedral angle (ϕ), account for the asymmetrical twisting motion of the active site of this enzyme. These findings provide an in-depth understanding of the pH-dependent structural dynamics of free and bound forms of CsCatD2 and basic information for the rational design of an inhibitor as a drug targeting parasitic CatD.

An in silico approach for identification of novel inhibitors as potential therapeutics targeting COVID-19 main protease

Respiratory disease caused by a novel coronavirus, COVID-19, has been labeled a pandemic by the World Health Organization. Very little is known about the infection mechanism for this virus. More importantly, there are no drugs or vaccines that can cure or prevent a person from getting COVID-19. In this study, the binding affinity of 2692 protease inhibitor compounds that are known in the protein data bank, are calculated against the main protease of the novel coronavirus with docking and molecular dynamics (MD). Both the docking and MD methods predict the macrocyclic tissue factor-factor VIIa (PubChem ID: 118098670) inhibitor to bind strongly with the main protease with a binding affinity of -10.6 and -10.0 kcal/mol, respectively. The TF-FVIIa inhibitors are known to prevent the coagulation of blood and have antiviral activity as shown in the case of SARS coronavirus. Two more inhibitors, phenyltriazolinones (PubChem ID: 104161460) and allosteric HCV NS5B polymerase thumb pocket 2 (PubChem ID: 163632044) have shown antiviral activity and also have high affinity towards the main protease of COVID-19. Furthermore, these inhibitors interact with the catalytic dyad in the active site of the COVID-19 main protease that is especially important in viral replication. The calculated theoretical dissociation constants of the proposed COVID-19 inhibitors are found to be very similar to the experimental dissociation constant values of similar protease-inhibitor systems.Communicated by Ramaswamy H. Sarma.

Identification and structure-activity relationship studies of small molecule inhibitors of the human cathepsin D

Cathepsin D, an aspartyl protease, is an attractive therapeutic target for various diseases, primarily cancer and osteoarthritis. However, despite several small molecule cathepsin D inhibitors being developed, that are highly potent, most of them show poor microsomal stability, which in turn limits their clinical translation. Herein, we describe the design, optimization and evaluation of a series of novel non-peptidic acylguanidine based small molecule inhibitors of cathepsin D. Optimization of our hit compound 1a (IC₅₀ = 29 nM) led to the highly potent mono sulphonamide analogue 4b (IC₅₀ = 4 nM), however with poor microsomal stability (HLM: 177 and MLM: 177 μl/min/mg). To further improve the microsomal stability while retaining the potency, we carried out an extensive structure-activity relationship screen which led to the identification of our optimised lead 24e (IC₅₀ = 45 nM), with an improved microsomal stability (HLM: 59.1 and MLM: 86.8 μl/min/mg). Our efforts reveal that 24e could be a good starting point or potential candidate for further preclinical studies against diseases where Cathepsin D plays an important role.

Urea-aromatic interactions in biology

Noncovalent interactions are key determinants in both chemical and biological processes. Among such processes, the hydrophobic interactions play an eminent role in folding of proteins, nucleic acids, formation of membranes, protein-ligand recognition, etc.. Though this interaction is mediated through the aqueous solvent, the stability of the above biomolecules can be highly sensitive to any small external perturbations, such as temperature, pressure, pH, or even cosolvent additives, like, urea-a highly soluble small organic molecule utilized by various living organisms to regulate osmotic pressure. A plethora of detailed studies exist covering both experimental and theoretical regimes, to understand how urea modulates the stability of biological macromolecules. While experimentalists have been primarily focusing on the thermodynamic and kinetic aspects, theoretical modeling predominantly involves mechanistic information at the molecular level, calculating atomistic details applying the force field approach to the high level electronic details using the quantum mechanical methods. The review focuses mainly on examples with biological relevance, such as (1) urea-assisted protein unfolding, (2) urea-assisted RNA unfolding, (3) urea lesion interaction within damaged DNA, (4) urea conduction through membrane proteins, and (5) protein-ligand interactions those explicitly address the vitality of hydrophobic interactions involving exclusively the urea-aromatic moiety.

Essential ingredients for rational drug design

This short review focuses on three aspects of rational drug design that we consider of utmost importance: the conformation of small molecules in solid form, the conformation of small molecules in solution and lesser studied interactions in protein-ligand complexes. Using examples from recent literature, we will illustrate these different aspects and how they have contributed to the discovery of potent modulators.

Converting SMILES to Stacking Interaction Energies

Predicting the strength of stacking interactions involving heterocycles is vital for several fields, including structure-based drug design. While quantum chemical computations can provide accurate stacking interaction energies, these come at a steep computational cost. To address this challenge, we recently developed quantitative predictive models of stacking interactions between druglike heterocycles and the aromatic amino acids Phe, Tyr, and Trp (DOI: 10.1021/jacs.9b00936 ). These models depend on heterocycle descriptors derived from electrostatic potentials (ESPs) computed using density functional theory and provide accurate stacking interactions without the need for expensive computations on stacked dimers. Herein, we show that these ESP-based descriptors can be reliably evaluated directly from the atom connectivity of the heterocycle, providing a means of predicting both the descriptors and the potential for a given heterocycle to engage in stacking interactions without resorting to any quantum chemical computations. This enables the rapid conversion of simple molecular representations (e.g., SMILES) directly into accurate stacking interaction energies using a freely available online tool, thereby providing a way to rank the stacking abilities of large sets of heterocycles.

Protein-Templated Dynamic Combinatorial Chemistry: Brief Overview and Experimental Protocol

Dynamic combinatorial chemistry (DCC) is a powerful tool to identify bioactive compounds. This efficient technique allows the target to select its own binders and circumvents the need for synthesis and biochemical evaluation of all individual derivatives. An ever-increasing number of publications report the use of DCC on biologically relevant target proteins. This minireview complements previous reviews by focusing on the experimental protocol and giving detailed examples of essential steps and factors that need to be considered, such as protein stability, buffer composition and cosolvents.

Stacking Interactions of Heterocyclic Drug Fragments with Protein Amide Backbones

Stacking interactions can be important enthalpic contributors to drug binding. Among the less well-studied stacking interactions are those occurring between an arene and the π-face of an amide group. Given the ubiquity of heterocycles in drugs, combined with the abundance of amides in the protein backbone, optimizing these noncovalent interactions can provide a potential route to enhanced drug binding. Previously, Diederich et al. (ChemMedChem 2013, 8, 397-404) studied stacked dimers of a model amide with a set of 18 heterocycles, showing that computed interaction energies correlate with the dipole moments of the heterocycles and providing guidelines for the optimization of these interactions. We considered stacked dimers of the same model amide with a larger set of 28 heterocycles common in pharmaceuticals, by using more robust ab initio methods. While the overall trends in these new data corroborate many of the results of Diederich et al., these data provide a more refined view of the nature of amide stacking interactions. We present a robust scoring function for amide stacking interaction energies based on the molecular dipole moment and strength of the electric field above the arene.

What Are We Missing? The Detergent Triton X-100 Added to Avoid Compound Aggregation Can Affect Assay Results in an Unpredictable Manner

In this study we show that the detergent Triton X-100, which is widely used in screening campaigns, significantly decreases the binding affinities of some known specific inhibitors of HIV-1 protease and the well-established model protease endothiapepsin in a fluorescence-based assay. Surprisingly, other structurally related inhibitors remain entirely unaffected. As a consequence, those compounds that were affected would most likely have been misclassified as unspecific binders, although they are actually true positives, and thus could be considered excellent starting points for further hit optimization.