Proof-of-concept studies with a computationally designed Mpro inhibitor as a synergistic combination regimen alternative to Paxlovid.

As the SARS-CoV-2 virus continues to spread and mutate, it remains important to focus not only on preventing spread through vaccination but also on treating infection with direct-acting antivirals (DAA). The approval of Paxlovid, a SARS-CoV-2 main protease (Mpro) DAA, has been significant for treatment of patients. A limitation of this DAA, however, is that the antiviral component, nirmatrelvir, is rapidly metabolized and requires inclusion of a CYP450 3A4 metabolic inhibitor, ritonavir, to boost levels of the active drug. Serious drug-drug interactions can occur with Paxlovid for patients who are also taking other medications metabolized by CYP4503A4, particularly transplant or otherwise immunocompromised patients who are most at risk for SARS-CoV-2 infection and the development of severe symptoms. Developing an alternative antiviral with improved pharmacological properties is critical for treatment of these patients. By using a computational and structure-guided approach, we were able to optimize a 100 to 250 µM screening hit to a potent nanomolar inhibitor and lead compound, Mpro61. In this study, we further evaluate Mpro61 as a lead compound, starting with examination of its mode of binding to SARS-CoV-2 Mpro. In vitro pharmacological profiling established a lack of off-target effects, particularly CYP450 3A4 inhibition, as well as potential for synergy with the currently approved alternate antiviral, molnupiravir. Development and subsequent testing of a capsule formulation for oral dosing of Mpro61 in B6-K18-hACE2 mice demonstrated favorable pharmacological properties, efficacy, and synergy with molnupiravir, and complete recovery from subsequent challenge by SARS-CoV-2, establishing Mpro61 as a promising potential preclinical candidate.

Enthalpies and entropies of hydration from Monte Carlo simulations.

The changes in free energy, enthalpy, and entropy for transfer of a solute from the gas phase into solution are the fundamental thermodynamic quantities that characterize the solvation process. Owing to the development of methods based on free-energy perturbation theory, computation of free energies of solvation has become routine in conjunction with Monte Carlo (MC) statistical mechanics and molecular dynamics (MD) simulations. Computation of the enthalpy change and by inference the entropy change is more challenging. Two methods are considered in this work corresponding to direct averaging for the solvent and solution and to computing the temperature derivative of the free energy in the van't Hoff approach. The application is for neutral organic solutes in TIP4P water using long MC simulations to improve precision. Definitive results are also provided for pure TIP4P water. While the uncertainty in computed free energies of hydration is ca. 0.05 kcal mol-1, it is ca. 0.4 kcal mol-1 for the enthalpy changes from either van't Hoff plots or the direct method with sampling for 5 billion MC configurations. Partial molar volumes of hydration are also computed by the direct method; they agree well with experimental data with an average deviation of 3 cm3 mol-1. In addition, the results permit breakdown of the errors in the free energy changes from the OPLS-AA force field into their enthalpic and entropic components. The excess hydrophobicity of organic solutes is enthalpic in origin.

Chemistry of the Au-Thiol Interface through the Lens of Single-Molecule Flicker Noise Measurements.

Chemistry of the Au-S interface at the nanoscale is one of the most complex systems to study, as the nature and strength of the Au-S bond change under different experimental conditions. In this study, using mechanically controlled break junction technique, we probed the conductance and analyzed Flicker noise for several aliphatic and aromatic thiol derivatives and thioethers. We demonstrate that Flicker noise can be used to unambiguously differentiate between stronger chemisorption (Au-SR) and weaker physisorption (Au-SRR') type interactions. The Flicker noise measurements indicate that the gold rearrangement in chemisorbed Au-SR junctions resembles that of the Au rearrangement in pure Au-Au metal contact breaking, which is independent of the molecular backbone structure and the resulting conductance. In contrast, thioethers showed the formation of a weaker physisorbed Au-SRR' type bond, and the Flicker noise measurement indicates the changes in the Au-anchoring group interface but not the Au-Au rearrangement like that in the Au-SR case. Additionally, by employing single-molecular conductance and Flicker noise analysis, we have probed the interfacial electric field-catalyzed ring-opening reaction of cyclic thioether under mild environmental conditions, which otherwise requires harsh chemical conditions for cleavage of the C-S bond. All of our conductance measurements are complemented by NEGF transport calculations. This study illustrates that the single-molecule conductance, together with the Flicker noise measurements can be used to tune and monitor chemical reactions at the single-molecule level.

OPLS/2020 Force Field for Unsaturated Hydrocarbons, Alcohols, and Ethers.

The OPLS all-atom force field was updated and applied to modeling unsaturated hydrocarbons, alcohols, and ethers. Testing has included gas-phase conformational energetics, properties of pure liquids, and free energies of hydration. Monte Carlo statistical mechanics (MC) calculations were used to model 60 liquids. In addition, a robust, automated procedure was devised to compute the free energies of hydration with high precision via free-energy perturbation (FEP) calculations using double annihilation. Testing has included larger molecules than in the past, and parameters are reported for the first time for some less common groups including alkynes, allenes, dienes, and acetals. The average errors in comparison with experimental data for the computed properties of the pure liquids were improved with the modified force field (OPLS/2020). For liquid densities and heats of vaporization, the average unsigned errors are 0.01 g/cm3 and 0.2 kcal/mol. The average error and signed error for free energies of hydration are both 1.2 kcal/mol. As noted before, this reflects a systematic overestimate of the hydrophobicity of organic molecules when the parametrization is done to minimize the errors for properties of pure liquids. Implications for the modeling of biomolecular systems with standard force fields are considered.

Ensemble Geometric Deep Learning of Aqueous Solubility.

Geometric deep learning is one of the main workhorses for harnessing the power of big data to predict molecular properties such as aqueous solubility, which is key to the pharmacokinetic improvement of drug candidates. Two ensembles of graph neural network architectures were built, one based on spectral convolution and the other on spatial convolution. The pretrained models, denoted respectively as SolNet-GCN and SolNet-GAT, significantly outperformed the existing neural networks benchmarked on a validation set of 207 molecules. The SolNet-GCN model demonstrated the best performance on both the training and validation sets, with RMSE values of 0.53 and 0.72 log molar unit and Pearson r2 values of 0.95 and 0.75, respectively. Further, the ranking power of the SolNet models agreed well with a QM-based thermodynamic cycle approach at the PBE-vdW level of theory on a series of benzophenylurea derivatives and a series of benzodiazepine derivatives. Nevertheless, testing the resultant models on a set of inhibitors of the macrophage migration inhibitory factor (MIF) illustrated that the inclusion of atomic attributes to discriminate atoms with a higher tendency to form intermolecular hydrogen bonds in the crystalline state and to identify planar or nonplanar substructures can be beneficial for the prediction of aqueous solubility.

A High-Throughput, High-Containment Human Primary Epithelial Airway Organ-on-Chip Platform for SARS-CoV-2 Therapeutic Screening.

COVID-19 emerged as a worldwide pandemic in early 2020, and while the rapid development of safe and efficacious vaccines stands as an extraordinary achievement, the identification of effective therapeutics has been less successful. This process has been limited in part by a lack of human-relevant preclinical models compatible with therapeutic screening on the native virus, which requires a high-containment environment. Here, we report SARS-CoV-2 infection and robust viral replication in PREDICT96-ALI, a high-throughput, human primary cell-based organ-on-chip platform. We evaluate unique infection kinetic profiles across lung tissue from three human donors by immunofluorescence, RT-qPCR, and plaque assays over a 6-day infection period. Enabled by the 96 devices/plate throughput of PREDICT96-ALI, we also investigate the efficacy of Remdesivir and MPro61 in a proof-of-concept antiviral study. Both compounds exhibit an antiviral effect against SARS-CoV-2 in the platform. This demonstration of SARS-CoV-2 infection and antiviral dosing in a high-throughput organ-on-chip platform presents a critical capability for disease modeling and therapeutic screening applications in a human physiology-relevant in vitro system.

Assessing Metadynamics and Docking for Absolute Binding Free Energy Calculations Using Severe Acute Respiratory Syndrome Coronavirus 2 Main Protease Inhibitors.

Absolute binding free energy (ABFE) calculations can be an important part of the drug discovery process by identifying molecules that have the potential to be strong binders for a biomolecular target. Recent work has used free energy perturbation (FEP) theory for these calculations, focusing on a set of 16 inhibitors of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). Herein, the same data set is evaluated by metadynamics (MetaD), four different docking programs, and molecular mechanics with generalized Born and surface area solvation. MetaD yields a Kendall τ distance of 0.28 and Pearson r2 of 0.49, which reflect somewhat less accuracy than that from the ABFE FEP results. Notably, it is demonstrated that an ensemble docking protocol by which each ligand is docked into the 13 crystal structures in this data set provides improved performance, particularly when docking is carried out with Glide XP (Kendall τ distance = 0.20, Pearson r2 = 0.71), Glide SP (Kendall τ distance = 0.19, Pearson r2 = 0.66), or AutoDock 4 (Kendall τ distance = 0.21, Pearson r2 = 0.55). The best results are obtained with "superconsensus" docking by averaging the 52 results for each compound using the 4 docking protocols and all 13 crystal structures (Kendall τ distance = 0.18, Pearson r2 = 0.73).

Understanding Current Density in Molecules Using Molecular Orbitals.

While the use of molecular orbitals (MOs) and their isosurfaces to explain physical phenomena in chemical systems is a time-honored tool, we show that the nodes are an equally important component for understanding the current density through single-molecule junctions. We investigate three different model systems consisting of an alkane, alkene, and even [n]cumulene and show that we can explain the form of the current density using the MOs of the molecule. Essentially, the MOs define the region in which current can flow and their gradients define the direction in which current flows within that region. We also show that it is possible to simplify the current density for improved understanding by either partitioning the current density into more chemically intuitive parts, such as σ- and π-systems, or by filtering out MOs with negligible contributions to the overall current density. Our work highlights that it is possible to infer a non-equilibrium property (current density) given only equilibrium properties (MOs and their gradients), and this, in turn, grants deeper insight into coherent electron transport.

Covalent and noncovalent strategies for targeting Lys102 in HIV-1 reverse transcriptase.

Reverse transcriptase (RT) is one of three key proteins responsible for the replication cycle of HIV-1 in the host. Several classes of inhibitors have been developed to target the enzyme, with non-nucleoside reverse transcriptase inhibitors forming first-line treatment. Previously, covalent RT inhibitors have been identified and found to bind irreversibly to commonly mutated residues such as Y181C. In this work we aim to circumvent the issue of NNRTI resistance through targeting K102, which has not yet been identified to confer drug resistance. As reported here, 34 compounds were synthesized and characterized biochemically and structurally with wild-type (WT) HIV-1 RT. Two of these inhibitors demonstrate covalent inhibition as evidenced by protein crystallography, enzyme kinetics, mass spectrometry, and antiviral potency in HIV-1 infected human T-cell assays.

Exploring novel HIV-1 reverse transcriptase inhibitors with drug-resistant mutants: A double mutant surprise.

HIV-1 reverse transcriptase (RT) remains a key target for HIV drug development. As successful management of the disease requires lifelong treatment, the emergence of resistance mutations is inevitable, making development of new RT inhibitors, which remain effective against resistant variants crucial. To this end, previous computationally guided drug design efforts have resulted in catechol diether compounds, which inhibit wildtype RT with picomolar affinities and appear to be promising preclinical candidates. To confirm that these compounds remain potent against Y181C, a widespread mutation conferring resistance to first generation inhibitors, they were screened against the HIV-1 N119 clinical isolate, reported as a Y181C single mutant. In comparison to a molecular clone with the same mutation, N119 appears less susceptible to inhibition by our preclinical candidate compounds. A more detailed sequencing effort determined that N119 was misidentified and carries V106A in combination with Y181C. While both indolizine and naphthalene substituted catechol diethers are potent against the classical Y181C single mutant, the addition of V106A confers more resistance against the indolizine derivatives than the naphthalene derivatives. Crystal structures presented in this study highlight key features of the naphthyl group, which allow these compounds to remain potent in the double mutant, including stronger interactions with F227 and less reliance on V106 for stabilization of the ethoxy-uracil ring, which makes critical hydrogen bonds with other residues in the binding pocket.

Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease.

Accurate, routine calculation of absolute binding free energies (ABFEs) for protein-ligand complexes remains a key goal of computer-aided drug design since it can enable screening and optimization of drug candidates. For development and testing of related methods, it is important to have high-quality datasets. To this end, from our own experimental studies, we have selected a set of 16 inhibitors of the SARS-CoV-2 main protease (Mpro) with structural diversity and well-distributed BFEs covering a 5 kcal/mol range. There is also minimal structural uncertainty since X-ray crystal structures have been deposited for 12 of the compounds. For methods testing, we report ABFE results from 2 µs molecular dynamics (MD) simulations using free energy perturbation (FEP) theory. The correlation of experimental and computed results is encouraging, with a Pearson's r2 of 0.58 and a Kendall τ of 0.24. The results indicate that current FEP-based ABFE calculations can be used for identification of active compounds (hits). While their accuracy for lead optimization is not yet sufficient, this activity remains addressable in separate lead series by relative BFE calculations.

Progress on the Pharmacological Targeting of Janus Pseudokinases.

The Janus kinases (JAKs) are key components of the JAK-STAT signaling pathway and are involved in myriad physiological processes. Though they are the molecular targets of many FDA-approved drugs, these drugs manifest adverse effects due in part to their inhibition of the requisite JAK kinase activity. However, the JAKs uniquely possess an integrated pseudokinase domain (JH2) that regulates the adjacent kinase domain (JH1). The therapeutic targeting of JH2 domains has been less thoroughly explored and may present an avenue to modulate the JAKs without the adverse effects associated with targeting the adjacent JH1 domain. The potential of this strategy was recently demonstrated with the FDA approval of the TYK2 JH2 ligand deucravacitinib for treating plaque psoriasis. In this light, the structure and targetability of the JAK pseudokinases are discussed, in conjunction with the state of development of ligands that bind to these domains.

Visualizing and comparing quantum interference in the π-system and σ-system of organic molecules.

Quantum interference effects in conjugated molecules have been well-explored, with benzene frequently invoked as a pedagogical example. These interference effects have been understood through a quantum interference map in which the electronic transmission is separated into interfering and non-interfering terms, with a focus on the π-orbitals for conjugated molecules. Recently, saturated molecules have also been reported to exhibit destructive quantum interference effects; however, the very different σ-orbital character in these molecules means that it is not clear how orbital contributions manifest. Herein, we demonstrate that the quantum interference effects in conjugated molecules are quite different from those observed in saturated molecules, as demonstrated by the quantum interference map. While destructive interference at the Fermi energy in the π-system of benzene arises from interference terms between paired occupied and virtual orbitals, this is not the case at the Fermi energy in saturated systems. Instead, destructive interference is evident when contributions from a larger number of non-paired orbitals cancel, leading to more subtle and varied manifestations of destructive interference in saturated systems.

Design, synthesis, and biological testing of biphenylmethyloxazole inhibitors targeting HIV-1 reverse transcriptase.

We report non-nucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) using a biphenylmethyloxazole pharmacophore. A crystal structure of benzyloxazole 1 was obtained and suggested the potential viability of biphenyl analogues. In particular, 6a, 6b, and 7 turned out to be potent NNRTIs with low-nanomolar activity in enzyme inhibition and infected T-cell assays, and with low cytotoxicity. Though modeling further suggested that analogues with fluorosulfate and epoxide warheads might provide covalent modification of Tyr188, synthesis and testing did not find evidence for this outcome.

Understanding In Vitro Pathways to Drug Discovery for TDP-43 Proteinopathies.

The use of cellular models is a common means to investigate the potency of therapeutics in pre-clinical drug discovery. However, there is currently no consensus on which model most accurately replicates key aspects of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) pathology, such as accumulation of insoluble, cytoplasmic transactive response DNA-binding protein (TDP-43) and the formation of insoluble stress granules. Given this, we characterised two TDP-43 proteinopathy cellular models that were based on different aetiologies of disease. The first was a sodium arsenite-induced chronic oxidative stress model and the second expressed a disease-relevant TDP-43 mutation (TDP-43 M337V). The sodium arsenite model displayed most aspects of TDP-43, stress granule and ubiquitin pathology seen in human ALS/FTD donor tissue, whereas the mutant cell line only modelled some aspects. When these two cellular models were exposed to small molecule chemical probes, different effects were observed across the two models. For example, a previously disclosed sulfonamide compound decreased cytoplasmic TDP-43 and increased soluble levels of stress granule marker TIA-1 in the cellular stress model without impacting these levels in the mutant cell line. This study highlights the challenges of using cellular models in lead development during drug discovery for ALS and FTD and reinforces the need to perform assessments of novel therapeutics across a variety of cell lines and aetiological models.

Covalent Modification of the JH2 Domain of Janus Kinase 2.

Probe molecules that covalently modify the JAK2 pseudokinase domain (JH2) are reported. Selective targeting of JH2 domains over the kinase (JH1) domains is a necessary feature for ligands intended to evaluate JH2 domains as therapeutic targets. The JH2 domains of three Janus kinases (JAK1, JAK2, and TYK2) possess a cysteine residue in the catalytic loop that does not occur in their JH1 domains. Starting from a non-selective kinase binding molecule, computer-aided design directed attachment of substituents terminating in acrylamide warheads to modify Cys675 of JAK2 JH2. Successful covalent attachment was demonstrated first through observation of enhanced binding with increasing incubation time in fluorescence polarization experiments. Covalent binding also increased selectivity to as much as ca. 30-fold for binding the JAK2 JH2 domain over the JH1 domain after a 20-h incubation. Covalency was confirmed through HPLC electrospray quadrupole time-of-flight HRMS experiments, which revealed the expected mass shifts.

Refinement of the Optimized Potentials for Liquid Simulations Force Field for Thermodynamics and Dynamics of Liquid Alkanes.

Torsion and Lennard-Jones parameters of the optimized potentials for liquid simulations (OPLS) all-atom force field have been refined for describing thermodynamics and dynamics of a wide range of liquid alkanes. Monte Carlo statistical mechanics (MC) and molecular dynamics (MD) simulations were carried out. For thermodynamics properties, MC simulations with truncated electrostatic interactions performed very closely to MD simulations with a Verlet neighbor list and the particle mesh Ewald algorithm. The average errors in comparison with experimental data for computed properties were improved with the modified force field (OPLS/2020), especially for long-chain alkanes. For liquid densities, heats of vaporization, and free energies of hydration, the average errors are 0.01 g/cm3, 0.2 kcal/mol, and ca. 0.5 kcal/mol, respectively; significant gains were made for relative heats of vaporization of isomeric series. Results for self-diffusion coefficients also reproduce experimental data well for linear alkane liquids up to hexadecane. The new force field is suitable for use in improved modeling of myriad systems of importance in chemistry, biology, and materials science.

Insights on JAK2 Modulation by Potent, Selective, and Cell-Permeable Pseudokinase-Domain Ligands.

JAK2 is a non-receptor tyrosine kinase that regulates hematopoiesis through the JAK-STAT pathway. The pseudokinase domain (JH2) is an important regulator of the activity of the kinase domain (JH1). V617F mutation in JH2 has been associated with the pathogenesis of various myeloproliferative neoplasms, but JAK2 JH2 has been poorly explored as a pharmacological target. In light of this, we aimed to develop JAK2 JH2 binders that could selectively target JH2 over JH1 and test their capacity to modulate JAK2 activity in cells. Toward this goal, we optimized a diaminotriazole lead compound into potent, selective, and cell-permeable JH2 binders leveraging computational design, synthesis, binding affinity measurements for the JH1, JH2 WT, and JH2 V617F domains, permeability measurements, crystallography, and cell assays. Optimized diaminotriazoles are capable of inhibiting STAT5 phosphorylation in both WT and V617F JAK2 in cells.

Trusting our machines: validating machine learning models for single-molecule transport experiments.

In this tutorial review, we will describe crucial aspects related to the application of machine learning to help users avoid the most common pitfalls. The examples we present will be based on data from the field of molecular electronics, specifically single-molecule electron transport experiments, but the concepts and problems we explore will be sufficiently general for application in other fields with similar data. In the first part of the tutorial review, we will introduce the field of single-molecule transport, and provide an overview of the most common machine learning algorithms employed. In the second part of the tutorial review, we will show, through examples grounded in single-molecule transport, that the promises of machine learning can only be fulfilled by careful application. We will end the tutorial review with a discussion of where we, as a field, could go from here.

Conversion of a False Virtual Screen Hit into Selective JAK2 JH2 Domain Binders Using Convergent Design Strategies.

The Janus kinase 2 (JAK2) pseudokinase domain (JH2) is an ATP-binding domain that regulates the activity of the catalytic tyrosine kinase domain (JH1). Dysregulation of JAK2 JH1 signaling caused by the V617F mutation in JH2 is implicated in various myeloproliferative neoplasms. To explore if JAK2 activity can be modulated by a small molecule binding to the ATP site in JH2, we have developed several ligand series aimed at selectively targeting the JAK2 JH2 domain. We report here the evolution of a false virtual screen hit into a new JAK2 JH2 series. Optimization guided by computational modeling has yielded analogues with nanomolar affinity for the JAK2 JH2 domain and >100-fold selectivity for the JH2 domain over the JH1 domain. A crystal structure for one of the potent compounds bound to JAK2 JH2 clarifies the origins of the strong binding and selectivity. The compounds expand the platform for seeking molecules to regulate JAK2 signaling, including V617F JAK2 hyperactivation.