Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein-nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Utilizing Molecular Simulations to Examine Nanosuspension Stability.

Drug nanosuspensions offer a promising approach to improve bioavailability for poorly soluble drug candidates. Such formulations often necessitate the inclusion of an excipient to stabilize the drug nanoparticles. However, the rationale for the choice of the correct excipient for a given drug candidate remains unclear. To gain molecular insight into formulation design, this work first utilizes a molecular dynamics simulation to computationally investigate drug-excipient interactions for a number of combinations that have been previously studied experimentally. We find that hydrophobic interactions drive excipient adsorption to drug nanoparticles and that the fraction of polar surface area serves as a predictor for experimental measurements of nanosuspension stability. To test these ideas prospectively, we applied our model to an uncharacterized drug compound, GDC-0810. Our simulations predicted that a salt form of GDC-0810 would lead to more stable nanosuspensions than the neutral form; therefore, we tested the stability of salt GDC-0810 nanosuspensions and found that the salt form readily formed nanosuspensions even without the excipient. To avoid computationally expensive simulations in the future, we extended our model by showing that simple, two-dimensional properties of single drug molecules can be used to rationalize nanosuspension designs without simulations. In all, our work demonstrates how computational tools can provide molecular insight into drug-excipient interactions and aid in rational formulation design.

Comparison of logP and logD correction models trained with public and proprietary data sets.

In drug discovery, partition and distribution coefficients, logP and logD for octanol/water, are widely used as metrics of the lipophilicity of molecules, which in turn have a strong influence on the bioactivity and bioavailability of potential drugs. There are a variety of established methods, mostly fragment or atom-based, to calculate logP while logD prediction generally relies on calculated logP and pKa for the estimation of neutral and ionized populations at a given pH. Algorithms such as ClogP have limitations generally leading to systematic errors for chemically related molecules while pKa estimation is generally more difficult due to the interplay of electronic, inductive and conjugation effects for ionizable moieties. We propose an integrated machine learning QSAR modeling approach to predict logD by training the model with experimental data while using ClogP and pKa predicted by commercial software as model descriptors. By optimizing the loss function for the ClogD calculated by the software, we build a correction model that incorporates both descriptors from the software and available experimental logD data. Additionally, we calculate logP from the logD model using the software predicted pKa's. Here, we have trained models using publicly or commercial available logD data to show that this approach can improve on commercial software predictions of lipophilicity. When applied to other logD data sets, this approach extends the domain of applicability of logD and logP predictions over commercial software. Performance of these models favorably compare with models built with a larger set of proprietary logD data.

Discovery of Inhibitors of the Lipopolysaccharide Transporter MsbA: From a Screening Hit to Potent Wild-Type Gram-Negative Activity.

The dramatic increase in the prevalence of multi-drug resistant Gram-negative bacterial infections and the simultaneous lack of new classes of antibiotics is projected to result in approximately 10 million deaths per year by 2050. We report on efforts to target the Gram-negative ATP-binding cassette (ABC) transporter MsbA, an essential inner membrane protein that transports lipopolysaccharide from the inner leaflet to the periplasmic face of the inner membrane. We demonstrate the improvement of a high throughput screening hit into compounds with on-target single digit micromolar (µM) minimum inhibitory concentrations against wild-type uropathogenic Escherichia coli, Klebsiella pneumoniae, and Enterobacter cloacae. A 2.98 Å resolution X-ray crystal structure of MsbA complexed with an inhibitor revealed a novel mechanism for inhibition of an ABC transporter. The identification of a fully encapsulated membrane binding site in Gram-negative bacteria led to unique physicochemical property requirements for wild-type activity.

Incorporating NOE-Derived Distances in Conformer Generation of Cyclic Peptides with Distance Geometry.

Nuclear magnetic resonance (NMR) data from NOESY (nuclear Overhauser enhancement spectroscopy) and ROESY (rotating frame Overhauser enhancement spectroscopy) experiments can easily be combined with distance geometry (DG) based conformer generators by modifying the molecular distance bounds matrix. In this work, we extend the modern DG based conformer generator ETKDG, which has been shown to reproduce experimental crystal structures from small molecules to large macrocycles well, to include NOE-derived interproton distances. In noeETKDG, the experimentally derived interproton distances are incorporated into the distance bounds matrix as loose upper (or lower) bounds to generate large conformer sets. Various subselection techniques can subsequently be applied to yield a conformer bundle that best reproduces the NOE data. The approach is benchmarked using a set of 24 (mostly) cyclic peptides for which NOE-derived distances as well as reference solution structures obtained by other software are available. With respect to other packages currently available, the advantages of noeETKDG are its speed and that no prior force-field parametrization is required, which is especially useful for peptides with unnatural amino acids. The resulting conformer bundles can be further processed with the use of structural refinement techniques to improve the modeling of the intramolecular nonbonded interactions. The noeETKDG code is released as a fully open-source software package available at www.github.com/rinikerlab/customETKDG.

Structure of the essential inner membrane lipopolysaccharide-PbgA complex.

Lipopolysaccharide (LPS) resides in the outer membrane of Gram-negative bacteria where it is responsible for barrier function1,2. LPS can cause death as a result of septic shock, and its lipid A core is the target of polymyxin antibiotics3,4. Despite the clinical importance of polymyxins and the emergence of multidrug resistant strains5, our understanding of the bacterial factors that regulate LPS biogenesis is incomplete. Here we characterize the inner membrane protein PbgA and report that its depletion attenuates the virulence of Escherichia coli by reducing levels of LPS and outer membrane integrity. In contrast to previous claims that PbgA functions as a cardiolipin transporter6-9, our structural analyses and physiological studies identify a lipid A-binding motif along the periplasmic leaflet of the inner membrane. Synthetic PbgA-derived peptides selectively bind to LPS in vitro and inhibit the growth of diverse Gram-negative bacteria, including polymyxin-resistant strains. Proteomic, genetic and pharmacological experiments uncover a model in which direct periplasmic sensing of LPS by PbgA coordinates the biosynthesis of lipid A by regulating the stability of LpxC, a key cytoplasmic biosynthetic enzyme10-12. In summary, we find that PbgA has an unexpected but essential role in the regulation of LPS biogenesis, presents a new structural basis for the selective recognition of lipids, and provides opportunities for future antibiotic discovery.

Implementation of the CYP Index for the Design of Selective Tryptophan-2,3-dioxygenase Inhibitors.

A class of imidazoisoindole (III) heme-binding indoleamine-2,3-dioxygenase (IDO1) inhibitors were optimized via structure-based drug design into a series of tryptophan-2,3-dioxygenase (TDO)-selective inhibitors. Kynurenine pathway modulation was demonstrated in vivo, which enabled evaluation of TDO as a potential cancer immunotherapy target. As means of mitigating the risk of drug-drug interactions arising from cytochrome P450 inhibition, a novel property-based drug design parameter, herein referred to as the CYP Index, was implemented for the design of inhibitors with appreciable selectivity for TDO over CYP3A4. We anticipate the CYP Index will be a valuable design parameter for optimizing CYP inhibition of any small molecule inhibitor containing a Lewis basic motif capable of binding heme.

Towards an improved understanding of drug excipient interactions to enable rapid optimization of nanosuspension formulations.

Suspensions of drug nanoparticles known as nanosuspensions have emerged as a successful enabling formulation approach for poorly soluble drug candidates. These nanoparticles typically require stabilization with specific polymer or surfactant excipients to prevent aggregation from occurring. This study demonstrates the necessity of formulating drug nanosuspensions with amphiphilic excipients possessing long hydrophobic alkyl or polymer block chains to produce stable nanoparticles. 28 different excipients and excipient combinations at various loadings were screened across the 3 drug compounds and their effectiveness, as characterized by the lowest excipient loading needed to stabilize a monodisperse drug suspension, is quantified as a function of various excipient parameters such as molecular weight, HLB value, CMC, H-bond donors and acceptors, and the length of the hydrophobic alkyl chains and polymer blocks within their molecular structure. Traditional characterization parameters (molecular weight, HLB value, and CMC) fail to predict excipient effectiveness. The conformational flexibility and length of the hydrophobic regions of amphiphilic excipients appears to be critical for effectiveness. This hypothesis was supported by molecular modeling studies to better understand the interactions between the excipients with the drug nanoparticle surface.

GluN2A NMDA Receptor Enhancement Improves Brain Oscillations, Synchrony, and Cognitive Functions in Dravet Syndrome and Alzheimer's Disease Models.

NMDA receptors (NMDARs) play subunit-specific roles in synaptic function and are implicated in neuropsychiatric and neurodegenerative disorders. However, the in vivo consequences and therapeutic potential of pharmacologically enhancing NMDAR function via allosteric modulation are largely unknown. We examine the in vivo effects of GNE-0723, a positive allosteric modulator of GluN2A-subunit-containing NMDARs, on brain network and cognitive functions in mouse models of Dravet syndrome (DS) and Alzheimer's disease (AD). GNE-0723 use dependently potentiates synaptic NMDA receptor currents and reduces brain oscillation power with a predominant effect on low-frequency (12-20 Hz) oscillations. Interestingly, DS and AD mouse models display aberrant low-frequency oscillatory power that is tightly correlated with network hypersynchrony. GNE-0723 treatment reduces aberrant low-frequency oscillations and epileptiform discharges and improves cognitive functions in DS and AD mouse models. GluN2A-subunit-containing NMDAR enhancers may have therapeutic benefits in brain disorders with network hypersynchrony and cognitive impairments.

Identification of Selective Acyl Sulfonamide-Cycloalkylether Inhibitors of the Voltage-Gated Sodium Channel (NaV) 1.7 with Potent Analgesic Activity.

Herein, we report the discovery and optimization of a series of orally bioavailable acyl sulfonamide NaV1.7 inhibitors that are selective for NaV1.7 over NaV1.5 and highly efficacious in in vivo models of pain and hNaV1.7 target engagement. An analysis of the physicochemical properties of literature NaV1.7 inhibitors suggested that acyl sulfonamides with high fsp3 could overcome some of the pharmacokinetic (PK) and efficacy challenges seen with existing series. Parallel library syntheses lead to the identification of analogue 7, which exhibited moderate potency against NaV1.7 and an acceptable PK profile in rodents, but relatively poor stability in human liver microsomes. Further, design strategy then focused on the optimization of potency against hNaV1.7 and improvement of human metabolic stability, utilizing induced fit docking in our previously disclosed X-ray cocrystal of the NaV1.7 voltage sensing domain. These investigations culminated in the discovery of tool compound 33, one of the most potent and efficacious NaV1.7 inhibitors reported to date.

Disrupting Gram-Negative Bacterial Outer Membrane Biosynthesis through Inhibition of the Lipopolysaccharide Transporter MsbA.

There is a critical need for new antibacterial strategies to counter the growing problem of antibiotic resistance. In Gram-negative bacteria, the outer membrane (OM) provides a protective barrier against antibiotics and other environmental insults. The outer leaflet of the outer membrane is primarily composed of lipopolysaccharide (LPS). Outer membrane biogenesis presents many potentially compelling drug targets as this pathway is absent in higher eukaryotes. Most proteins involved in LPS biosynthesis and transport are essential; however, few compounds have been identified that inhibit these proteins. The inner membrane ABC transporter MsbA carries out the first essential step in the trafficking of LPS to the outer membrane. We conducted a biochemical screen for inhibitors of MsbA and identified a series of quinoline compounds that kill Escherichia coli through inhibition of its ATPase and transport activity, with no loss of activity against clinical multidrug-resistant strains. Identification of these selective inhibitors indicates that MsbA is a viable target for new antibiotics, and the compounds we identified serve as useful tools to further probe the LPS transport pathway in Gram-negative bacteria.

Structural basis for dual-mode inhibition of the ABC transporter MsbA.

The movement of core-lipopolysaccharide across the inner membrane of Gram-negative bacteria is catalysed by an essential ATP-binding cassette transporter, MsbA. Recent structures of MsbA and related transporters have provided insights into the molecular basis of active lipid transport; however, structural information about their pharmacological modulation remains limited. Here we report the 2.9 Å resolution structure of MsbA in complex with G907, a selective small-molecule antagonist with bactericidal activity, revealing an unprecedented mechanism of ABC transporter inhibition. G907 traps MsbA in an inward-facing, lipopolysaccharide-bound conformation by wedging into an architecturally conserved transmembrane pocket. A second allosteric mechanism of antagonism occurs through structural and functional uncoupling of the nucleotide-binding domains. This study establishes a framework for the selective modulation of ABC transporters and provides rational avenues for the design of new antibiotics and other therapeutics targeting this protein family.

Aminoisoxazoles as Potent Inhibitors of Tryptophan 2,3-Dioxygenase 2 (TDO2).

Tryptophan 2,3-dioxygenase 2 (TDO2) catalyzes the conversion of tryptophan to the immunosuppressive metabolite kynurenine. TDO2 overexpression has been observed in a number of cancers; therefore, TDO inhibition may be a useful therapeutic intervention for cancers. We identified an aminoisoxazole series as potent TDO2 inhibitors from a high-throughput screen (HTS). An extensive medicinal chemistry effort revealed that both the amino group and the isoxazole moiety are important for TDO2 inhibitory activity. Computational modeling yielded a binding hypothesis and provided insight into the observed structure-activity relationships. The optimized compound 21 is a potent TDO2 inhibitor with modest selectivity over indolamine 2,3-dioxygenase 1 (IDO1) and with improved human whole blood stability.

chemalot and chemalot_knime: Command line programs as workflow tools for drug discovery.

BACKGROUND: Analyzing files containing chemical information is at the core of cheminformatics. Each analysis may require a unique workflow. This paper describes the chemalot and chemalot_knime open source packages. Chemalot is a set of command line programs with a wide range of functionalities for cheminformatics. The chemalot_knime package allows command line programs that read and write SD files from stdin and to stdout to be wrapped into KNIME nodes. The combination of chemalot and chemalot_knime not only facilitates the compilation and maintenance of sequences of command line programs but also allows KNIME workflows to take advantage of the compute power of a LINUX cluster. RESULTS: Use of the command line programs is demonstrated in three different workflow examples: (1) A workflow to create a data file with project-relevant data for structure-activity or property analysis and other type of investigations, (2) The creation of a quantitative structure-property-relationship model using the command line programs via KNIME nodes, and (3) The analysis of strain energy in small molecule ligand conformations from the Protein Data Bank database. CONCLUSIONS: The chemalot and chemalot_knime packages provide lightweight and powerful tools for many tasks in cheminformatics. They are easily integrated with other open source and commercial command line tools and can be combined to build new and even more powerful tools. The chemalot_knime package facilitates the generation and maintenance of user-defined command line workflows, taking advantage of the graphical design capabilities in KNIME. Graphical abstract Example KNIME workflow with chemalot nodes and the corresponding command line pipe.

A novel NMDA receptor positive allosteric modulator that acts via the transmembrane domain.

Ionotropic glutamate receptors (iGluRs) mediate fast excitatory neurotransmission and are key nervous system drug targets. While diverse pharmacological tools have yielded insight into iGluR extracellular domain function, less is known about molecular mechanisms underlying the ion conduction gating process within the transmembrane domain (TMD). We have discovered a novel NMDAR positive allosteric modulator (PAM), GNE-9278, with a unique binding site on the extracellular surface of the TMD. Mutation of a single residue near the Lurcher motif on GluN1 M3 can convert GNE-9278 modulation from positive to negative, and replacing three AMPAR pre-M1 residues with corresponding NMDAR residues can confer GNE-9278 sensitivity to AMPARs. Modulation by GNE-9278 is state-dependent and significantly alters extracellular domain pharmacology. The unique properties and structural determinants of GNE-9278 reveal new modulatory potential of the iGluR TMD.

A Comparison of Quantum and Molecular Mechanical Methods to Estimate Strain Energy in Druglike Fragments.

Reducing internal strain energy in small molecules is critical for designing potent drugs. Quantum mechanical (QM) and molecular mechanical (MM) methods are often used to estimate these energies. In an effort to determine which methods offer an optimal balance in accuracy and performance, we have carried out torsion scan analyses on 62 fragments. We compared nine QM and four MM methods to reference energies calculated at a higher level of theory: CCSD(T)/CBS single point energies (coupled cluster with single, double, and perturbative triple excitations at the complete basis set limit) calculated on optimized geometries using MP2/6-311+G**. The results show that both the more recent MP2.X perturbation method as well as MP2/CBS perform quite well. In addition, combining a Hartree-Fock geometry optimization with a MP2/CBS single point energy calculation offers a fast and accurate compromise when dispersion is not a key energy component. Among MM methods, the OPLS3 force field accurately reproduces CCSD(T)/CBS torsion energies on more test cases than the MMFF94s or Amber12:EHT force fields, which struggle with aryl-amide and aryl-aryl torsions. Using experimental conformations from the Cambridge Structural Database, we highlight three example structures for which OPLS3 significantly overestimates the strain. The energies and conformations presented should enable scientists to estimate the expected error for the methods described and we hope will spur further research into QM and MM methods.

Computationally Discovered Potentiating Role of Glycans on NMDA Receptors.

N-methyl-D-aspartate receptors (NMDARs) are glycoproteins in the brain central to learning and memory. The effects of glycosylation on the structure and dynamics of NMDARs are largely unknown. In this work, we use extensive molecular dynamics simulations of GluN1 and GluN2B ligand binding domains (LBDs) of NMDARs to investigate these effects. Our simulations predict that intra-domain interactions involving the glycan attached to residue GluN1-N440 stabilize closed-clamshell conformations of the GluN1 LBD. The glycan on GluN2B-N688 shows a similar, though weaker, effect. Based on these results, and assuming the transferability of the results of LBD simulations to the full receptor, we predict that glycans at GluN1-N440 might play a potentiator role in NMDARs. To validate this prediction, we perform electrophysiological analysis of full-length NMDARs with a glycosylation-preventing GluN1-N440Q mutation, and demonstrate an increase in the glycine EC50 value. Overall, our results suggest an intramolecular potentiating role of glycans on NMDA receptors.

GluN2A-Selective Pyridopyrimidinone Series of NMDAR Positive Allosteric Modulators with an Improved in Vivo Profile.

The N-methyl-d-aspartate receptor (NMDAR) is an ionotropic glutamate receptor, gated by the endogenous coagonists glutamate and glycine, permeable to Ca2+ and Na+. NMDAR dysfunction is associated with numerous neurological and psychiatric disorders, including schizophrenia, depression, and Alzheimer's disease. Recently, we have disclosed GNE-0723 (1), a GluN2A subunit-selective and brain-penetrant positive allosteric modulator (PAM) of NMDARs. This work highlights the discovery of a related pyridopyrimidinone core with distinct structure-activity relationships, despite the structural similarity to GNE-0723. GNE-5729 (13), a pyridopyrimidinone-based NMDAR PAM, was identified with both an improved pharmacokinetic profile and increased selectivity against AMPARs. We also include X-ray structure analysis and modeling to propose hypotheses for the activity and selectivity differences.