Abiotic Small Molecule Inhibitors and Activators of the LasR Quorum Sensing Receptor in Pseudomonas aeruginosa with Potencies Comparable or Surpassing N-Acyl Homoserine Lactones.

The opportunistic pathogen Pseudomonas aeruginosa controls almost 10% of its genome, including myriad virulence genes, via a cell-to-cell chemical communication system called quorum sensing (QS). Small molecules that either inhibit or activate QS in P. aeruginosa represent useful research tools to study the role of this signaling pathway in infection and interrogate its viability as an antivirulence target. However, despite active research in this area over the past 20+ years, there are relatively few synthetic compounds known to strongly inhibit or activate QS in P. aeruginosa. Most reported QS modulators in this pathogen are of low potency or have structural liabilities that limit their application in biologically relevant environments such as mimics of the native N-acyl l-homoserine lactone (AHL) signals. Here, we report the results of a high-throughput screen for abiotic small molecules that target LasR, a key QS regulator in P. aeruginosa. We screened a 25,000-compound library and discovered four new structural classes of abiotic LasR modulators. These compounds include antagonists that surpass the potency of all known AHL-type compounds and mimetics thereof, along with an agonist with potency approaching that of LasR's native ligand. The novel structures of this compound set, along with their anticipated robust physicochemical profiles, underscore their potential value as probe molecules to interrogate the roles of QS in this formidable pathogen.

Development of non-sedating antischistosomal benzodiazepines.

The neglected tropical disease schistosomiasis infects over 200 million people worldwide and is treated with just one broad spectrum antiparasitic drug (praziquantel). Alternative drugs are needed in the event of emerging praziquantel resistance or treatment failure. One promising lead that has shown efficacy in animal models and a human clinical trial is the benzodiazepine meclonazepam, discovered by Roche in the 1970's. Meclonazepam was not brought to market because of dose-limiting sedative side effects. However, the human target of meclonazepam that causes sedation (GABAARs) are not orthologous to the parasite targets that cause worm death. Therefore, we were interested in whether the structure of meclonazepam could be modified to produce antiparasitic benzodiazepines that do not cause host sedation. We synthesized 18 meclonazepam derivatives with modifications at different positions on the benzodiazepine ring system and tested them for in vitro antiparasitic activity. This identified five compounds that progressed to in vivo screening in a murine model, two of which cured parasite infections with comparable potency to meclonazepam. When these two compounds were administered to mice that were run on the rotarod test, both were less sedating than meclonazepam. These findings demonstrate the proof of concept that meclonazepam analogs can be designed with an improved therapeutic index, and point to the C3 position of the benzodiazepine ring system as a logical site for further structure-activity exploration to further optimize this chemical series.

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries.

Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 µM.

Multivariate chemogenomic screening prioritizes new macrofilaricidal leads.

Development of direct acting macrofilaricides for the treatment of human filariases is hampered by limitations in screening throughput imposed by the parasite life cycle. In vitro adult screens typically assess single phenotypes without prior enrichment for chemicals with antifilarial potential. We developed a multivariate screen that identified dozens of compounds with submicromolar macrofilaricidal activity, achieving a hit rate of >50% by leveraging abundantly accessible microfilariae. Adult assays were multiplexed to thoroughly characterize compound activity across relevant parasite fitness traits, including neuromuscular control, fecundity, metabolism, and viability. Seventeen compounds from a diverse chemogenomic library elicited strong effects on at least one adult trait, with differential potency against microfilariae and adults. Our screen identified five compounds with high potency against adults but low potency or slow-acting microfilaricidal effects, at least one of which acts through a novel mechanism. We show that the use of microfilariae in a primary screen outperforms model nematode developmental assays and virtual screening of protein structures inferred with deep learning. These data provide new leads for drug development, and the high-content and multiplex assays set a new foundation for antifilarial discovery.

Bayes optimal informer sets for early-stage drug discovery.

An important experimental design problem in early-stage drug discovery is how to prioritize available compounds for testing when very little is known about the target protein. Informer-based ranking (IBR) methods address the prioritization problem when the compounds have provided bioactivity data on other potentially relevant targets. An IBR method selects an informer set of compounds, and then prioritizes the remaining compounds on the basis of new bioactivity experiments performed with the informer set on the target. We formalize the problem as a two-stage decision problem and introduce the Bayes Optimal Informer SEt (BOISE) method for its solution. BOISE leverages a flexible model of the initial bioactivity data, a relevant loss function, and effective computational schemes to resolve the two-step design problem. We evaluate BOISE and compare it to other IBR strategies in two retrospective studies, one on protein-kinase inhibition and the other on anticancer drug sensitivity. In both empirical settings BOISE exhibits better predictive performance than available methods. It also behaves well with missing data, where methods that use matrix completion show worse predictive performance.

Atovaquone: An Inhibitor of Oxidative Phosphorylation as Studied in Gynecologic Cancers.

Oxidative phosphorylation is an active metabolic pathway in cancer. Atovaquone is an oral medication that inhibits oxidative phosphorylation and is FDA-approved for the treatment of malaria. We investigated its potential anti-cancer properties by measuring cell proliferation in 2D culture. The clinical formulation of atovaquone, Mepron, was given to mice with ovarian cancers to monitor its effects on tumor and ascites. Patient-derived cancer stem-like cells and spheroids implanted in NSG mice were treated with atovaquone. Atovaquone inhibited the proliferation of cancer cells and ovarian cancer growth in vitro and in vivo. The effect of atovaquone on oxygen radicals was determined using flow and imaging cytometry. The oxygen consumption rate (OCR) in adherent cells was measured using a Seahorse XFe96 Extracellular Flux Analyzer. Oxygen consumption and ATP production were inhibited by atovaquone. Imaging cytometry indicated that the majority of the oxygen radical flux triggered by atovaquone occurred in the mitochondria. Atovaquone decreased the viability of patient-derived cancer stem-like cells and spheroids implanted in NSG mice. NMR metabolomics showed shifts in glycolysis, citric acid cycle, electron transport chain, phosphotransfer, and metabolism following atovaquone treatment. Our studies provide the mechanistic understanding and preclinical data to support the further investigation of atovaquone's potential as a gynecologic cancer therapeutic.

In Silico Modeling and Scoring of PROTAC-Mediated Ternary Complex Poses.

Proteolysis targeting chimeras (PROTACs) are molecules that induce protein degradation via formation of ternary complexes between an E3 ubiquitin ligase and a target protein. The rational design of PROTACs requires accurate knowledge of the native configuration of the PROTAC-induced ternary complex. This study demonstrates that native and non-native ternary complex poses can be distinguished based on the pose occupancy time in MD, where native poses exhibit longer occupancy times at both room and higher temperatures. Candidate poses are generated by MD sampling and pre-ranked by classic MM/GBSA. A specific heating scheme is then applied to accelerate ternary pose departure, with the pose occupancy time and fraction being measured. This scoring identifies the native pose in all systems tested. Its success is partially attributed to the dynamic nature of pose departure analyses, which accounts for entropic effects typically neglected in the faster static scoring methods, while entropy plays a greater role in protein-protein than in protein-ligand systems.

A marine microbiome antifungal targets urgent-threat drug-resistant fungi.

New antifungal drugs are urgently needed to address the emergence and transcontinental spread of fungal infectious diseases, such as pandrug-resistant Candida auris. Leveraging the microbiomes of marine animals and cutting-edge metabolomics and genomic tools, we identified encouraging lead antifungal molecules with in vivo efficacy. The most promising lead, turbinmicin, displays potent in vitro and mouse-model efficacy toward multiple-drug-resistant fungal pathogens, exhibits a wide safety index, and functions through a fungal-specific mode of action, targeting Sec14 of the vesicular trafficking pathway. The efficacy, safety, and mode of action distinct from other antifungal drugs make turbinmicin a highly promising antifungal drug lead to help address devastating global fungal pathogens such as C. auris.

Predicting kinase inhibitors using bioactivity matrix derived informer sets.

Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prioritization, initial screening must provide the necessary data. Commonly, such an initial library is selected on the basis of chemical diversity by some pseudo-random process (for example, the first few plates of a larger library) or by selecting an entire smaller library. These approaches may not produce a sufficient number or diversity of actives. An alternative approach is to select an informer set of screening compounds on the basis of chemogenomic information from previous testing of compounds against a large number of targets. We compare different ways of using chemogenomic data to choose a small informer set of compounds based on previously measured bioactivity data. We develop this Informer-Based-Ranking (IBR) approach using the Published Kinase Inhibitor Sets (PKIS) as the chemogenomic data to select the informer sets. We test the informer compounds on a target that is not part of the chemogenomic data, then predict the activity of the remaining compounds based on the experimental informer data and the chemogenomic data. Through new chemical screening experiments, we demonstrate the utility of IBR strategies in a prospective test on three kinase targets not included in the PKIS.

Practical Model Selection for Prospective Virtual Screening.

Virtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the data set and evaluation strategy. We consider a wide range of ligand-based machine learning and docking-based approaches for virtual screening on two protein-protein interactions, PriA-SSB and RMI-FANCM, and present a strategy for choosing which algorithm is best for prospective compound prioritization. Our workflow identifies a random forest as the best algorithm for these targets over more sophisticated neural network-based models. The top 250 predictions from our selected random forest recover 37 of the 54 active compounds from a library of 22,434 new molecules assayed on PriA-SSB. We show that virtual screening methods that perform well on public data sets and synthetic benchmarks, like multi-task neural networks, may not always translate to prospective screening performance on a specific assay of interest.

Oxidative stress via inhibition of the mitochondrial electron transport and Nrf-2-mediated anti-oxidative response regulate the cytotoxic activity of plumbagin.

Plumbagin, an anti-cancer agent, is toxic to cells of multiple species. We investigated if plumbagin targets conserved biochemical processes. Plumbagin induced DNA damage and apoptosis in cells of diverse mutational background with comparable potency. A 3-5 fold increase in intracellular oxygen radicals occurred in response to plumbagin. Neutralization of the reactive oxygen species by N-acetylcysteine blocked apoptosis, indicating a central role for oxidative stress in plumbagin-mediated cell death. Plumbagin docks in the ubiquinone binding sites (Q0 and Qi) of mitochondrial complexes I-III, the major sites for oxygen radicals. Plumbagin decreased oxygen consumption rate, ATP production and optical redox ratio (NAD(P)H/FAD) indicating interference with electron transport downstream of mitochondrial Complex II. Oxidative stress induced by plumbagin triggered an anti-oxidative response via activation of Nrf2. Plumbagin and the Nrf2 inhibitor, brusatol, synergized to inhibit cell proliferation. These data indicate that while inhibition of electron transport is the conserved mechanism responsible for plumbagin's chemotoxicity, activation of Nrf2 is the resulting anti-oxidative response that allows plumbagin to serve as a chemopreventive agent. This study provides the basis for designing potent and selective plumbagin analogs that can be coupled with suitable Nrf2 inhibitors for chemotherapy or administered as single agents to induce Nrf2-mediated chemoprevention.

Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening.

In structure-based virtual screening, compound ranking through a consensus of scores from a variety of docking programs or scoring functions, rather than ranking by scores from a single program, provides better predictive performance and reduces target performance variability. Here we compare traditional consensus scoring methods with a novel, unsupervised gradient boosting approach. We also observed increased score variation among active ligands and developed a statistical mixture model consensus score based on combining score means and variances. To evaluate performance, we used the common performance metrics ROCAUC and EF1 on 21 benchmark targets from DUD-E. Traditional consensus methods, such as taking the mean of quantile normalized docking scores, outperformed individual docking methods and are more robust to target variation. The mixture model and gradient boosting provided further improvements over the traditional consensus methods. These methods are readily applicable to new targets in academic research and overcome the potentially poor performance of using a single docking method on a new target.

DBSI server: DNA binding site identifier.

UNLABELLED: : Protein-nucleic acid interactions are among the most important intermolecular interactions in the regulation of cellular events. Identifying residues involved in these interactions from protein structure alone is an important challenge. Here we introduce the webserver interface to DNA Binding Site Identifier (DBSI), a powerful structure-based SVM model for the prediction and visualization of DNA binding sites on protein structures. DBSI has been shown to be a top-performing model to predict DNA binding sites on the surface of a protein or peptide and shows promise in predicting RNA binding sites. AVAILABILITY AND IMPLEMENTATION: Server is available at http://dbsi.mitchell-lab.org CONTACT: jcmitchell@wisc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data-driven models for protein interaction and design.

We describe methods and results for four new types of challenge in the Critical Assessment of PRedicted Interactions (CAPRI). Two new challenges asked predictors to create models related to protein interface design. The first of these was to distinguish binding interfaces from designed nonbinding interfaces. The second was to predict the effects of all single-point mutations on hemagglutinin binding to two small designed proteins. Two additional challenges asked predictors to submit high-resolution structures for interface-bound crystallographic waters and for binding heparin to a putative glycosylase.

DBSI: DNA-binding site identifier.

In this study, we present the DNA-Binding Site Identifier (DBSI), a new structure-based method for predicting protein interaction sites for DNA binding. DBSI was trained and validated on a data set of 263 proteins (TRAIN-263), tested on an independent set of protein-DNA complexes (TEST-206) and data sets of 29 unbound (APO-29) and 30 bound (HOLO-30) protein structures distinct from the training data. We computed 480 candidate features for identifying protein residues that bind DNA, including new features that capture the electrostatic microenvironment within shells near the protein surface. Our iterative feature selection process identified features important in other models, as well as features unique to the DBSI model, such as a banded electrostatic feature with spatial separation comparable with the canonical width of the DNA minor groove. Validations and comparisons with established methods using a range of performance metrics clearly demonstrate the predictive advantage of DBSI, and its comparable performance on unbound (APO-29) and bound (HOLO-30) conformations demonstrates robustness to binding-induced protein conformational changes. Finally, we offer our feature data table to others for integration into their own models or for testing improved feature selection and model training strategies based on DBSI.

Preferred binding orientations of phenacetin in CYP1A1 and CYP1A2 are associated with isoform-selective metabolism.

Human cytochromes P450 1A1 and 1A2 play important roles in drug metabolism and chemical carcinogenesis. Although these two enzymes share high sequence identity, they display different substrate specificities and inhibitor susceptibilities. In the present studies, we investigated the structural basis for these differences with phenacetin as a probe using a number of complementary approaches, such as enzyme kinetics, stoichiometric assays, NMR, and molecular modeling. Kinetic and stoichiometric analyses revealed that substrate specificity (k(cat)/K(m)) of CYP1A2 was approximately 18-fold greater than that of CYP1A1, as expected. Moreover, despite higher H2O2 production, the coupling efficiency of reducing equivalents to acetaminophen formation in CYP1A2 was tighter than that in CYP1A1. CYP1A1, in contrast to CYP1A2, displayed much higher uncoupling, producing more water. The subsequent NMR longitudinal (T1) relaxation studies with the substrate phenacetin and its product acetaminophen showed that both compounds displayed similar binding orientations within the active site of CYP1A1 and CYP1A2. However, the distance between the OCH2 protons of the ethoxy group (site of phenacetin O-deethylation) and the heme iron was 1.5 Å shorter in CYP1A2 than in CYP1A1. The NMR findings are thus consistent with our kinetic and stoichiometric results, providing a likely molecular basis for more efficient metabolism of phenacetin by CYP1A2.

Ring substituents on substituted benzamide ligands indirectly mediate interactions with position 7.39 of transmembrane helix 7 of the D4 dopamine receptor.

In an effort to delineate how specific molecular interactions of dopamine receptor ligand classes vary between D2-like dopamine receptor subtypes, a conserved threonine in transmembrane (TM) helix 7 (Thr7.39), implicated as a key ligand interaction site with biogenic amine G protein-coupled receptors, was substituted with alanine in D2 and D4 receptors. Interrogation of different ligand chemotypes for sensitivity to this substitution revealed enhanced affinity in the D4, but not the D2 receptor, specifically for substituted benzamides (SBAs) having polar 4- (para) and/or 5- (meta) benzamide ring substituents. D4-T7.39A was fully functional, and the mutation did not alter the sodium-mediated positive and negative allostery observed with SBAs and agonists, respectively. With the exception of the non-SBA ligand (+)-butaclamol, which, in contrast to certain SBAs, had decreased affinity for the D4-T7.39A mutant, the interactions of numerous other ligands were unaffected by this mutation. SBAs were docked into D4 models in the same mode as observed for eticlopride in the D3 crystal structure. In this mode, interactions with TM5 and TM6 residues constrain the SBA ring position that produces distal steric crowding between pyrrolidinyl/diethylamine moieties and D4-Thr7.39. Ligand-residue interaction energy profiles suggest this crowding is mitigated by substitution with a smaller alanine. The profiles indicate sites that contribute to the SBA binding interaction and site-specific energy changes imparted by the D4-T7.39A mutation. Substantial interaction energy changes are observed at only a few positions, some of which are not conserved among the dopamine receptor subtypes and thus seem to account for this D4 subtype-specific structure-activity relationship.

Transmembrane segment five serines of the D4 dopamine receptor uniquely influence the interactions of dopamine, norepinephrine, and Ro10-4548.

Conserved serines of transmembrane segment (TM) five (TM5) are critical for the interactions of endogenous catecholamines with alpha(1)- and alpha(2)-adrenergic, beta(2)-adrenergic, and D1, D2, and D3 dopamine receptors. The unique high-affinity interaction of the D4 dopamine receptor subtype with both norepinephrine and dopamine, and the fact that TM5 serine interactions have never been studied for this receptor subtype, led us to investigate the interactions of ligands with D4 receptor TM5 serines. Serine-to-alanine mutations at positions 5.42 and 5.46 drastically decreased affinities of dopamine and norepinephrine for the D4 receptor. The D4-S5.43A receptor mutant had substantially reduced affinity for norepinephrine, but a modest loss of affinity for dopamine. In functional assays of cAMP accumulation, norephinephrine was unable to activate any of the mutant receptors, even though the agonist quinpirole displayed wild-type functional properties for all of them. Dopamine was unable to activate the S5.46A mutant and had reduced potency for the S5.43A mutant and reduced potency and efficacy for the S5.42A mutant. In contrast, Ro10-4548 [RAC-2'-2-hydroxy-3-4-(4-hydroxy-2-methoxyphenyl)-1-piperazinyl-propoxy-acetanilide], a catechol-like antagonist of the wild-type receptor unexpectedly functions as an agonist of the S5.43A mutant. Other noncatechol ligands had similar properties for mutant and wild-type receptors. This is the first example of a dopamine receptor point mutation selectively changing the receptor's interaction with a specific antagonist to that of an agonist, and together with other data, provides evidence, supported by molecular modeling, that catecholamine-type agonism is induced by different ligand-specific configurations of intermolecular H-bonds with the TM5 conserved serines.