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.

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.

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.

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.

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.

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.

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.

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.

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.

Antibodies as a model system for comparative model refinement.

Predicting the conformations of loops is a critical aspect of protein comparative (homology) modeling. Despite considerable advances in developing loop prediction algorithms, refining loops in homology models remains challenging. In this work, we use antibodies as a model system to investigate strategies for more robustly predicting loop conformations when the protein model contains errors in the conformations of side chains and protein backbone surrounding the loop in question. Specifically, our test system consists of partial models of antibodies in which the "scaffold" (i.e., the portion other than the complementarity determining region, CDR, loops) retains native backbone conformation, whereas the CDR loops are predicted using a combination of knowledge-based modeling (H1, H2, L1, L2, and L3) and ab initio loop prediction (H3). H3 is the most variable of the CDRs. Using a previously published method, a test set of 10 shorter H3 loops (5-7 residues) are predicted to an average backbone (N-C alpha-C-O) RMSD of 2.7 A while 11 longer loops (8-9 residues) are predicted to 5.1 A, thus recapitulating the difficulties in refining loops in models. By contrast, in control calculations predicting the same loops in crystal structures, the same method reconstructs the loops to an average of 0.5 and 1.4 A for the shorter and longer loops, respectively. We modify the loop prediction method to improve the ability to sample near-native loop conformations in the models, primarily by reducing the sensitivity of the sampling to the loop surroundings, and allowing the other CDR loops to optimize with the H3 loop. The new method improves the average accuracy significantly to 1.3 A RMSD and 3.1 A RMSD for the shorter and longer loops, respectively. Finally, we present results predicting 8-10 residue loops within complete comparative models of five nonantibody proteins. While anecdotal, these mixed, full-model results suggest our approach is a promising step toward more accurately predicting loops in homology models. Furthermore, while significant challenges remain, our method is a potentially useful tool for predicting antibody structures based on a known Fv scaffold.

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.

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.

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.

Improving the species cross-reactivity of an antibody using computational design.

The high degree of specificity displayed by antibodies often results in varying potencies against antigen orthologs, which can affect the efficacy of these molecules in different animal models of disease. We have used a computational design strategy to improve the species cross-reactivity of an antibody-based inhibitor of the cancer-associated serine protease MT-SP1. In silico predictions were tested in vitro, and the most effective mutation, T98R, was shown to improve antibody affinity for the mouse ortholog of the enzyme 14-fold, resulting in an inhibitor with a K(I) of 340 pM. This improved affinity will be valuable when exploring the role of MT-SP1 in mouse models of cancer, and the strategy outlined here could be useful in fine-tuning antibody specificity.

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.

Toward better refinement of comparative models: predicting loops in inexact environments.

Achieving atomic-level accuracy in comparative protein models is limited by our ability to refine the initial, homolog-derived model closer to the native state. Despite considerable effort, progress in developing a generalized refinement method has been limited. In contrast, methods have been described that can accurately reconstruct loop conformations in native protein structures. We hypothesize that loop refinement in homology models is much more difficult than loop reconstruction in crystal structures, in part, because side-chain, backbone, and other structural inaccuracies surrounding the loop create a challenging sampling problem; the loop cannot be refined without simultaneously refining adjacent portions. In this work, we single out one sampling issue in an artificial but useful test set and examine how loop refinement accuracy is affected by errors in surrounding side-chains. In 80 high-resolution crystal structures, we first perturbed 6-12 residue loops away from the crystal conformation, and placed all protein side chains in non-native but low energy conformations. Even these relatively small perturbations in the surroundings made the loop prediction problem much more challenging. Using a previously published loop prediction method, median backbone (N-Calpha-C-O) RMSD's for groups of 6, 8, 10, and 12 residue loops are 0.3/0.6/0.4/0.6 A, respectively, on native structures and increase to 1.1/2.2/1.5/2.3 A on the perturbed cases. We then augmented our previous loop prediction method to simultaneously optimize the rotamer states of side chains surrounding the loop. Our results show that this augmented loop prediction method can recover the native state in many perturbed structures where the previous method failed; the median RMSD's for the 6, 8, 10, and 12 residue perturbed loops improve to 0.4/0.8/1.1/1.2 A. Finally, we highlight three comparative models from blind tests, in which our new method predicted loops closer to the native conformation than first modeled using the homolog template, a task generally understood to be difficult. Although many challenges remain in refining full comparative models to high accuracy, this work offers a methodical step toward that goal.

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.

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.