High-throughput screening (HTS) in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb) for further development towards new tuberculosis (TB) drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15–28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.
The human Organic Cation/Carnitine Transporter (hOCTN2), is a high affinity cation/carnitine transporter expressed widely in human tissues and is physiologically important for the homeostasis of L-carnitine. The objective of this study was to elucidate the substrate requirements of this transporter via computational modelling based on published in vitro data. Nine published substrates of hOCTN2 were used to create a common features pharmacophore that was validated by mapping other known OCTN2 substrates. The pharmacophore was used to search a drug database and retrieved molecules that were then used as search queries in PubMed for instances of a side effect (rhabdomyolysis) associated with interference with L-carnitine transport. The substrate pharmacophore was comprised of two hydrogen bond acceptors, a positive ionizable feature and ten excluded volumes. The substrate pharmacophore also mapped 6 out of 7 known substrate molecules used as a test set. After searching a database of ~800 known drugs, thirty drugs were predicted to map to the substrate pharmacophore with L-carnitine shape restriction. At least 16 of these molecules had case reports documenting an association with rhabdomyolysis and represent a set for prioritizing for future testing as OCTN2 substrates or inhibitors. This computational OCTN2 substrate pharmacophore derived from published data partially overlaps a previous OCTN2 inhibitor pharmacophore and is also able to select compounds that demonstrate rhabdomyolysis, further confirming the possible linkage between this side effect and hOCTN2.
human Organic Cation/Carnitine Transporter (hOCTN2); carnitine; pharmacophore; transporters
New strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) are required in order to identify the next generation of tuberculosis (TB) drugs. Our approach leverages the integration of intensive data mining and curation and computational approaches, including cheminformatics combined with bioinformatics, to suggest biological targets and their small molecule modulators. Knowledge of which biological targets are essential for Mtb viability, under a given set of in vitro or in vivo assay conditions, and absent in the human host is a crucial input. We draw on the mimicry of the associated “essential metabolites” to suggest small molecule inhibitors of the essential protein target. Empirical studies are then utilized to delineate the effect of the small molecule putative mimic on cultured Mtb growth.
We now describe a combined cheminformatics and bioinformatics approach that uses the TBCyc pathway and genome database, the Collaborative Drug Discovery database of molecules with activity against Mtb and their associated targets, a 3D pharmacophore approach and Bayesian models of TB activity in order to select pathways and metabolites and ultimately prioritize molecules that may be acting as metabolite mimics and exhibit activity against TB.
In this study we combined the TB cheminformatics and pathways databases that enabled us to computationally search >80,000 vendor available molecules and ultimately test 23 compounds in vitro that resulted in two compounds (N-(2-furylmethyl)-N′-[(5-nitro-3-thienyl)carbonyl]thioureaand N-[(5-nitro-3-thienyl)carbonyl]-N′-(2-thienylmethyl)thiourea) proposed as mimics of D-fructose 1,6 bisphosphate, (MIC of 20 and 40μg/ml, respectively).
This is a simple yet novel approach that has the potential to identify inhibitors of bacterial growth as illustrated by compounds identified in this study that have activity against Mtb.
Bayesian models; bioinformatics; cheminformatics; Collaborative Drug Discovery; D-fructose 1,6-bisphosphate; essential metabolites; metabolites; Mimics; Mycobacterium tuberculosis; pathways; pharmacophore
An increasing number of researchers are focused on strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) as tuberculosis (TB) drugs.
In order to learn from prior work we have collated information on molecules screened versus Mtb and their targets which has been made available in the Collaborative Drug Discovery (CDD) database. This dataset contains published data on target, essentiality, links to PubMed, TBDB, TBCyc (which provides a pathway-based visualization of the entire cellular biochemical network) and human homolog information. The development of mobile cheminformatics apps could lower the barrier to drug discovery and promote collaboration. Therefore we have used this set of over 700 molecules screened versus Mtb and their targets to create a free mobile app (TB Mobile) that displays molecule structures and links to the bioinformatics data. By input of a molecular structures and performing a similarity search within the app we can infer potential targets or search by targets to retrieve compounds known to be active.
TB Mobile may assist researchers as part of their workflow in identifying potential targets for hits generated from phenotypic screening and in prioritizing them for further follow-up. The app is designed to lower the barriers to accessing this information, so that all researchers with an interest in combatting this deadly disease can use it freely to the benefit of their own efforts.
Collaborative drug discovery tuberculosis database; Drug discovery; Mobile applications; Mycobacterium tuberculosis; Tuberculosis; TB Mobile
When we look at the rapid growth of scientific databases on the Internet in the past decade, we tend to take the accessibility and provenance of the data for granted. As we see a future of increased database integration, the licensing of the data may be a hurdle that hampers progress and usability. We have formulated four rules for licensing data for open drug discovery, which we propose as a starting point for consideration by databases and for their ultimate adoption. This work could also be extended to the computational models derived from such data. We suggest that scientists in the future will need to consider data licensing before they embark upon re-using such content in databases they construct themselves.
With the advent of next-generation DNA sequencing, the pace of inherited orphan disease gene identification has increased dramatically, a situation that will continue for at least the next several years. At present, the numbers of such identified disease genes significantly outstrips the number of laboratories available to investigate a given disorder, an asymmetry that will only increase over time. The hope for any genetic disorder is, where possible and in addition to accurate diagnostic test formulation, the development of therapeutic approaches. To this end, we propose here the development of a strategic toolbox and preclinical research pathway for inherited orphan disease. Taking much of what has been learned from rare genetic disease research over the past two decades, we propose generalizable methods utilizing transcriptomic, system-wide chemical biology datasets combined with chemical informatics and, where possible, repurposing of FDA approved drugs for pre-clinical orphan disease therapies. It is hoped that this approach may be of utility for the broader orphan disease research community and provide funding organizations and patient advocacy groups with suggestions for the optimal path forward. In addition to enabling academic pre-clinical research, strategies such as this may also aid in seeding startup companies, as well as further engaging the pharmaceutical industry in the treatment of rare genetic disease.
Orphan disease therapy; Preclinical drug development; Generalizable screening methods; Translational toolbox
Nuclear hormone receptors (NHRs) are transcription factors that work in concert with co-activators and co-repressors to regulate gene expression. Some examples of ligands for NHRs include endogenous compounds such as bile acids, retinoids, steroid hormones, thyroid hormone, and vitamin D. This review describes the evolution of liver X receptors α and β (NR1H3 and 1H2, respectively), farnesoid X receptor (NR1H4), vitamin D receptor (NR1I1), pregnane X receptor (NR1I2), and constitutive androstane receptor (NR1I3). These NHRs participate in complex, overlapping transcriptional regulation networks involving cholesterol homeostasis and energy metabolism. Some of these receptors, particularly PXR and CAR, are promiscuous with respect to the structurally wide range of ligands that act as agonists. A combination of functional and computational analyses has shed light on the evolutionary changes of NR1H and NR1I receptors across vertebrates, and how these receptors may have diverged from ancestral receptors that first appeared in invertebrates.
Bile acids and salts; Ciona intestinalis; cholesterol; drug modeling; molecular evolution; oxysterols; phylogeny
We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage this data in order to move from a hit to a lead to a clinical candidate and potentially a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are herein reviewed. We suggest these computational approaches should be more optimally integrated in a workflow with experimental approaches to accelerate TB drug discovery.
Organic cation/carnitine transporter (OCTN2; SLC22A5) is an important transporter for L-carnitine homeostasis, but can be inhibited by drugs, which may cause L-carnitine deficiency and possibly other OCTN2-mediated drug-drug interactions. One objective was to develop a quantitative structure–activity relationship (QSAR) of OCTN2 inhibitors, in order to predict and identify other potential OCTN2 inhibitors and infer potential clinical interactions. A second objective was to assess two high renal clearance drugs that interact with OCTN2 in vitro (cetirizine and cephaloridine) for possible OCTN2-mediated drug-drug interactions. Using previously generated in vitro data of 22 drugs, a 3D quantitative pharmacophore model and a Bayesian machine learning model were developed. The four pharmacophore features include two hydrophobic groups, one hydrogen-bond acceptor, and one positive ionizable center. The Bayesian machine learning model was developed using simple interpretable descriptors and function class fingerprints of maximum diameter 6 (FCFP_6). An external test set of 27 molecules, including 15 newly identified OCTN2 inhibitors, and a literature test set of 22 molecules were used to validate both models. The computational models afforded good capability to identify structurally diverse OCTN2 inhibitors, providing a valuable tool to predict new inhibitors efficiently. Inhibition results confirmed our previously observed association between rhabdomyolysis and Cmax/Ki ratio. The two high renal clearance drugs cetirizine and cephaloridine were found not to be OCTN2 substrates and their diminished elimination by other drugs is concluded not to be mediated by OCTN2.
Bayesian model; pharmacophore; transporters
From routine in vitro drug-transporter inhibition assays, observed inhibition is typically assumed from direct interaction with the transporter. Other mechanisms that possibly reduce substrate uptake are not frequently fully examined. The objective of this study was to investigate the association of transporter inhibition with drug cytotoxicity. From a pool of drugs that were identified as known ASBT or OCTN2 inhibitors, twenty one drugs were selected to screen inhibitory potency of their prototypical substrate and cytotoxicity against three human sodium-dependent solute carrier (SLC) transporters: apical sodium-dependent bile acid transporter (ASBT), organic cation/carnitine transporter (OCTN2), and the excitatory amino acid transporter 4 (EAAT4) in stable cell lines. Twenty drugs showed apparent inhibition in OCTN2-MDCK and ASBT-MDCK. Four dihydropyridine calcium channel blockers were cytotoxic to MDCK cells, and the observed cytotoxicity of three of them accounted for their apparent OCTN2 inhibition, and consequently were classified as non-OCTN2 inhibitors. Meanwhile, since their cytotoxicity only moderately contributed to ASBT inhibition, these three were still considered ASBT inhibitors. Four other drugs showed apparent inhibition in EAAT4-HEK cells, and cytotoxicity of three drugs corresponded with their inhibition of this transporter. Therefore, cytotoxicity significantly affected EAAT4 observations. Results showed the potential of cytotoxicity as a mechanism that can account for apparent in vitro transporter inhibition. Drug cytotoxicity varied in different cell lines, which could increase false positives for pharmacophore development.
Cytotoxicity; HEK cells; inhibition; MDCK cells; transporter
The nuclear receptor farnesoid X receptor alpha (FXRα, NR1H4) is activated by bile acids in multiple species including mouse, rat, and human and in this study we have identified two isoforms of Fxrα in Japanese medaka (Oryzias latipes), a small freshwater teleost. Both isoforms share a high amino acid sequence identity to mammalian FXRα (~70% in the ligand-binding domain). Fxrα1 and Fxrα2 differ within the AF1 domain due to alternative splicing at the fourth intron-exon boundary. This process results in Fxrα1 having an extended N-terminus compared to Fxrα2. A Gal4DBD-FxrαLBD fusion construct was activated by chenodeoxycholic, cholic, deoxycholic and lithocholic acids, and the synthetic agonist GW4064 in transient transactivation assays. Activation of the Gal4DBD-FxrαLBD fusion construct was enhanced by addition of PGC-1α, as demonstrated through titration assays. Surprisingly, when the full-length versions of the two Fxrα isoforms were compared in transient transfection assays, Fxrα2 was activated by C24 bile acids and GW4064, while Fxrα1 was not significantly activated by any of the compounds tested. Since the only significant difference between the full-length constructs was sequence in the AF1 domain, these experiments highlight a key functional region in the Fxrα AF1 domain. Furthermore, mammalian two-hybrid studies demonstrated the ability of Fxrα2, but not Fxrα1, to interact with PGC-1α and SRC-1, and supported our results from the transient transfection reporter gene activation assays. These data demonstrate that both mammalian and teleost FXR (Fxrα2 isoform) are activated by primary and secondary bile acids.
The farnesoid X receptor (FXR), pregnane X receptor (PXR), and vitamin D receptor (VDR) are three closely related nuclear hormone receptors in the NR1H and 1I subfamilies that share the property of being activated by bile salts. Bile salts vary significantly in structure across vertebrate species, suggesting that receptors binding these molecules may show adaptive evolutionary changes in response. We have previously shown that FXRs from the sea lamprey (Petromyzon marinus) and zebrafish (Danio rerio) are activated by planar bile alcohols found in these two species. In this report, we characterize FXR, PXR, and VDR from the green-spotted pufferfish (Tetraodon nigriviridis), an actinopterygian fish that unlike the zebrafish has a bile salt profile similar to humans. We utilize homology modelling, docking, and pharmacophore studies to understand the structural features of the Tetraodon receptors.
Tetraodon FXR has a ligand selectivity profile very similar to human FXR, with strong activation by the synthetic ligand GW4064 and by the primary bile acid chenodeoxycholic acid. Homology modelling and docking studies suggest a ligand-binding pocket architecture more similar to human and rat FXRs than to lamprey or zebrafish FXRs. Tetraodon PXR was activated by a variety of bile acids and steroids, although not by the larger synthetic ligands that activate human PXR such as rifampicin. Homology modelling predicts a larger ligand-binding cavity than zebrafish PXR. We also demonstrate that VDRs from the pufferfish and Japanese medaka were activated by small secondary bile acids such as lithocholic acid, whereas the African clawed frog VDR was not.
Our studies provide further evidence of the relationship between both FXR, PXR, and VDR ligand selectivity and cross-species variation in bile salt profiles. Zebrafish and green-spotted pufferfish provide a clear contrast in having markedly different primary bile salt profiles (planar bile alcohols for zebrafish and sterically bent bile acids for the pufferfish) and receptor selectivity that matches these differences in endogenous ligands. Our observations to date present an integrated picture of the co-evolution of bile salt structure and changes in the binding pockets of three nuclear hormone receptors across the species studied.
An organism requires a range of biomolecules for its growth. By definition, these are essential molecules which constitute the basic metabolic requirements of an organism. A small organic molecule with chemical similarity to that of an essential metabolite may bind to the enzyme that catalyzes its production and inhibit it, likely resulting in the stasis or death of the organism. Here, we report a high-throughput approach for identifying essential metabolites of an organism using genetic and biochemical approaches and then implement computational approaches to identify metabolite mimics. We generated and genotyped 5,126 Mycobacterium tuberculosis mutants and performed a statistical analysis to determine putative essential genes. The essential molecules of M. tuberculosis were classified as products of enzymes that are encoded by genes in this list. Although incomplete, as many enzymes of M. tuberculosis have yet to be identified and characterized, this is the first report of a large number of essential molecules of the organism. We identified essential metabolites of three distinct metabolic pathways in M. tuberculosis and selected molecules with chemical similarity using cheminformatics strategies that illustrate a variety of different pharmacophores. Our approach is aimed at systematic identification of essential molecules and their mimics as a blueprint for development of effective chemical probes of M. tuberculosis metabolism, with the ultimate goal of seeking drugs that can kill this pathogen. As an illustration of this approach, we report that compounds JFD01307SC and l-methionine-S-sulfoximine, which share chemical similarity with an essential molecule of M. tuberculosis, inhibited the growth of this organism at micromolar concentrations.
The estimate that more lives may have been lost in 2009 due to tuberculosis (TB) than in any year in history is alarming. Approximately 9.2 million new cases and 1.8 million deaths due to TB were reported in 2008. The widespread prevalence of Mycobacterium tuberculosis strains that are resistant to drugs currently used to treat TB means that new drugs are urgently needed to treat these infections. Here, we have identified pathways for the biosynthesis of essential metabolites and associated enzymes in M. tuberculosis using a genetics-based approach. Small molecules that mimic these essential metabolites were identified, and some of them were shown to inhibit the growth of M. tuberculosis. Therefore, we illustrate an approach based on genetics to develop inhibitors that have the potential to be advanced as candidate drugs for treating TB.
The human apical sodium-dependent bile acid transporter (ASBT; SLC10A2) is the primary mechanism for intestinal bile acid re-absorption. In the colon, secondary bile acids increase the risk of cancer. Therefore, drugs that inhibit ASBT have the potential to increase the risk of colon cancer. The objectives of this study were to identify FDA-approved drugs that inhibit ASBT and to derive computational models for ASBT inhibition. Inhibition was evaluated using ASBT-MDCK monolayers and taurocholate as the model substrate. Computational modeling employed a HipHop qualitative approach, a Hypogen quantitative approach, as well as a modified Laplacian Bayesian modeling method using 2D descriptors. Initially, 30 compounds were screened for ASBT inhibition. A qualitative pharmacophore was developed using the most potent 11 compounds and applied to search a drug database, yielding 58 hits. Additional compounds were tested and their Ki values were measured. A 3D-QSAR and a Bayesian model were developed using 38 molecules. The quantitative pharmacophore consisted of one hydrogen bond acceptor, three hydrophobic features, and five excluded volumes. Each model was further validated with two external test sets of 30 and 19 molecules. Validation analysis showed both models exhibited good predictability in determining whether a drug is a potent or non-potent ASBT inhibitor. The Bayesian model correctly ranked the most active compounds. In summary, using a combined in vitro and computational approach, we found that many FDA-approved drugs from diverse classes, such as the dihydropyridine calcium channel blockers and HMG CoA-reductase inhibitors, are ASBT inhibitors.
Bile acids; ASBT; QSAR; Bayesian; SLC10A2; transporters; colon cancer
The pregnane X receptor (PXR) is a key transcriptional regulator of many genes [e.g., cytochrome P450s (CYP2C9, CYP3A4, CYP2B6), MDR1] involved in xenobiotic metabolism and excretion.
As part of an evaluation of different approaches to predict compound affinity for nuclear hormone receptors, we used the molecular docking program GOLD and a hybrid scoring scheme based on similarity weighted GoldScores to predict potential PXR agonists in the ToxCast database of pesticides and other industrial chemicals. We present some of the limitations of different in vitro systems, as well as docking and ligand-based computational models.
Each ToxCast compound was docked into the five published crystallographic structures of human PXR (hPXR), and 15 compounds were selected based on their consensus docking scores for testing. In addition, we used a Bayesian model to classify the ToxCast compounds into PXR agonists and nonagonists. hPXR activation was determined by luciferase-based reporter assays in the HepG2 and DPX-2 human liver cell lines.
We tested 11 compounds, of which 6 were strong agonists and 2 had weak agonist activity. Docking results of additional compounds were compared with data reported in the literature. The prediction sensitivity of PXR agonists in our sample ToxCast data set (n = 28) using docking and the GoldScore was higher than with the hybrid score at 66.7%. The prediction sensitivity for PXR agonists using GoldScore for the entire ToxCast data set (n = 308) compared with data from the NIH (National Institutes of Health) Chemical Genomics Center data was 73.8%.
Docking and the GoldScore may be useful for prioritizing large data sets prior to in vitro testing with good sensitivity across the sample and entire ToxCast data set for hPXR agonists.
Bayesian model; docking; GoldScore; hybrid scoring; PXR; ToxCast
The pregnane X receptor belongs to the nuclear hormone receptor superfamily and is involved in the transcriptional control of numerous genes. It was originally thought that it was a xenobiotic sensor controlling detoxification pathways. Recent studies have shown an increasingly important role in inflammation and cancer, supporting its function in abrogating tissue damage. PXR orthologs and PXR-like pathways have been identified in several non-mammalian species which corroborate a conserved role for PXR in cellular detoxification. In summary, PXR has a multiplicity of roles in vivo and is being revealed as behaving like a “Jekyll and Hyde” nuclear hormone receptor. The importance of this review is to elucidate the need for discovery of antagonists of PXR to further probe its biology and therapeutic applications. Although several PXR agonists are already reported, virtually nothing is known about PXR antagonists. Here, we propose the development of PXR antagonists through chemical, genetic and molecular modeling approaches. Based on this review it will be clear that antagonists of PXR and PXR-like pathways will have widespread utility in PXR biology and therapeutics.
Agonists; Antagonists; Machine Learning; Pregnane X Receptor; Pharmacophore
The objective was to elucidate the inhibition requirements of the human Organic Cation/Carnitine Transporter (hOCTN2).
Twenty-seven drugs were screened initially for their potential to inhibit uptake of L-carnitine into a stably transfected hOCTN2-MDCK cell monolayer. A HipHop common features pharmacophore was developed and used to search a drug database. Fifty-three drugs, including some not predicted to be inhibitors, were selected and screened in vitro.
A common features pharmacophore was derived from initial screening data and consisted of three hydrophobic features and a positive ionizable feature. Among the 33 tested drugs that were predicted to map to the pharmacophore, 27 inhibited hOCTN2 in vitro (40% or less L-carnitine uptake from 2.5μM L-carnitine solution in presence of 500 μM drug, compared to L-carnitine uptake without drug present). Hence, the pharmacophore accurately prioritized compounds for testing. Ki measurements showed low micromolar inhibitors belonged to diverse therapeutic classes of drugs, including many not previously known to inhibit hOCTN2. Compounds were more likely to cause rhabdomyolysis if the Cmax/Ki ratio was higher than 0.0025.
A combined pharmacophore and in vitro approach found new, structurally diverse inhibitors for hOCTN2 that may possibly cause clinical significant toxicity such as rhabdomyolysis.
human Organic Cation/Carnitine Transporter (hOCTN2); carnitine; pharmacophore; rhabdomyolysis
Immunoassays are used for therapeutic drug monitoring (TDM) yet may suffer from cross-reacting compounds able to bind the assay antibodies in a manner similar to the target molecule. To our knowledge, there has been no investigation using computational tools to predict cross-reactivity with TDM immunoassays. The authors used molecular similarity methods to enable calculation of structural similarity for a wide range of compounds (prescription and over-the-counter medications, illicit drugs, and clinically significant metabolites) to the target molecules of TDM immunoassays. Utilizing different molecular descriptors (MDL public keys, functional class fingerprints, and pharmacophore fingerprints) and the Tanimoto similarity coefficient, the authors compared cross-reactivity data in the package inserts of immunoassays marketed for in vitro diagnostic use. Using MDL public keys and the Tanimoto similarity coefficient showed a strong and statistically significant separation between cross-reactive and non-cross-reactive compounds. Thus, two-dimensional shape similarity of cross-reacting molecules and the target molecules of TDM immunoassays provides a fast chemoinformatics methods for a priori prediction of potential of cross-reactivity that might be otherwise undetected. These methods could be used to reliably focus cross-reactivity testing on compounds with high similarity to the target molecule and limit testing of compounds with low similarity and ultimately with a very low probability of cross-reacting with the assay in vitro.
drug monitoring; molecular conformations; molecular models; immunoassay; similarity
Immunoassays used for routine drug of abuse (DOA) and toxicology screening may be limited by cross-reacting compounds able to bind to the antibodies in a manner similar to the target molecule(s). To date, there has been little systematic investigation using computational tools to predict cross-reactive compounds.
Commonly used molecular similarity methods enabled calculation of structural similarity for a wide range of compounds (prescription and over-the-counter medications, illicit drugs, and clinically significant metabolites) to the target molecules of DOA/toxicology screening assays. We utilized different molecular descriptors (MDL public keys, functional class fingerprints, and pharmacophore fingerprints) and the Tanimoto similarity coefficient. These data were then compared with cross-reactivity data in the package inserts of immunoassays marketed for in vitro diagnostic use. Previously untested compounds that were predicted to have a high probability of cross-reactivity were tested.
Molecular similarity calculated using MDL public keys and the Tanimoto similarity coefficient showed a strong and statistically significant separation between cross-reactive and non-cross-reactive compounds. This was validated experimentally by discovery of additional cross-reactive compounds based on computational predictions.
The computational methods employed are amenable towards rapid screening of databases of drugs, metabolites, and endogenous molecules, and may be useful for identifying cross-reactive molecules that would be otherwise unsuspected. These methods may also have value in focusing cross-reactivity testing on compounds with high similarity to the target molecule(s) and limiting testing of compounds with low similarity and very low probability of cross-reacting with the assay.
toxicology; molecular conformations; molecular models; immunoassay; similarity; substance abuse detection
Shape is a fundamentally important molecular feature that often determines the fate of a compound in terms of molecular interactions with preferred and non-preferred biological targets. Complementarity of binding in small molecule-protein, peptide-receptor, antigen-antibody and protein-protein interactions is key to life and survival, but also to targeting molecules with bioactivity. We review the application of shape in various biological systems such as substrate recognition, ligand specificity / selectivity and antibody recognition in the context of computational methods such as docking, quantitative structure activity relationships, classification models and similarity search algorithms. These in silico pharmacology methods have recently demonstrated the importance and applicability of determining molecular shape in drug discovery, virtual screening and predictive toxicology. The results from recently published studies show that shape and shape-based descriptors are at least as useful as other traditional molecular descriptors.
Antibody; Depth; Descriptors; Dopamine receptors; Molecular shape; Nuclear hormone receptor; Pharmacophore
The human pregnane X receptor (PXR) is a transcriptional regulator of many genes involved in xenobiotic metabolism and excretion. Reliable prediction of high affinity binders with this receptor would be valuable for pharmaceutical drug discovery to predict potential toxicological responses
Materials and Methods
Computational models were developed and validated for a dataset consisting of human PXR (PXR) activators and non-activators. We used support vector machine (SVM) algorithms with molecular descriptors derived from two sources, Shape Signatures and the Molecular Operating Environment (MOE) application software. We also employed the molecular docking program GOLD in which the GoldScore method was supplemented with other scoring functions to improve docking results.
The overall test set prediction accuracy for PXR activators with SVM was 72% to 81%. This indicates that molecular shape descriptors are useful in classification of compounds binding to this receptor. The best docking prediction accuracy (61%) was obtained using 1D Shape Signature descriptors as a weighting factor to the GoldScore. By pooling the available human PXR data sets we revealed those molecular features that are associated with human PXR activators.
These combined computational approaches using molecular shape information may assist scientists to more confidently identify PXR activators.
docking; hybrid methods; machine learning; pregnane X receptor; shape signatures descriptors; support vector machine
The goals of the present study were to apply a generalized regression model and support vector machine (SVM) models with Shape Signatures descriptors, to the domain of blood—brain barrier (BBB) modeling.
Materials and Methods
The Shape Signatures method is a novel computational tool that was used to generate molecular descriptors utilized with the SVM classification technique with various BBB datasets. For comparison purposes we have created a generalized linear regression model with eight MOE descriptors and these same descriptors were also used to create SVM models.
The generalized regression model was tested on 100 molecules not in the model and resulted in a correlation r2=0.65. SVM models with MOE descriptors were superior to regression models, while Shape Signatures SVM models were comparable or better than those with MOE descriptors. The best 2D shape signature models had 10-fold cross validation prediction accuracy between 80–83% and leave-20%-out testing prediction accuracy between 80–82% as well as correctly predicting 84% of BBB+ compounds (n=95) in an external database of drugs.
Our data indicate that Shape Signatures descriptors can be used with SVM and these models may have utility for predicting blood—brain barrier permeation in drug discovery.
blood—brain barrier; principal component analysis; regression; shape signatures; support vector machine
Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches.
Promiscuous proteins generally bind a large array of diverse ligand structures. This may be facilitated by a very large binding site, multiple binding sites, or a flexible binding site that can adjust to the size of the ligand. These aspects also increase the complexity of predicting whether a molecule will bind or not to such proteins which frequently function as exogenous compound sensors to respond to toxic stress. For example, transporters may prevent absorption of some molecules, and enzymes may convert them to more readily excretable compounds (or alternatively activate them prior to further clearance by other detoxification enzymes). Nuclear hormone receptors may respond to ligands and then affect downstream gene expression to upregulate both enzymes and transporters to increase the clearance for the same or different molecules. We have assessed the ability of many different ligand-based and structure-based computational approaches to model and predict the activation of human PXR by steroidal compounds. We find the most effective computational approach to identify potential steroidal PXR agonists which are clinically relevant due to their widespread use in clinical medicine and the presence of mimics in the environment.
Microbial detection requires the recognition of pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRRs) that are distributed on the cell surface and within the cytosol. The nucleotide-binding oligomerization domain (NOD)-like receptor (NLR) family functions as an intracellular PRR that triggers the innate immune response. The mechanism by which PAMPs enter the cytosol to interact with NLRs, particularly muropeptides derived from the bacterial proteoglycan cell wall, is poorly understood. PEPT2 is a proton-dependent transporter that mediates the active translocation of di- and tripeptides across epithelial tissues, including the lung. Using computational tools, we initially established that bacterial dipeptides, particularly γ-D-glutamyl-meso-diaminopimelic acid (γ-iE-DAP), are suitable substrates for PEPT2. We then determined in primary cultures of human upper airway epithelia and transiently transfected CHO-PEPT2 cell lines that γ-iE-DAP uptake was mediated by PEPT2 with an affinity constant of approximately 193 μM, whereas muramyl dipeptide was not transported. Exposure to γ-iE-DAP at the apical surface of differentiated, polarized cultures resulted in activation of the innate immune response in an NOD1- and RIP2-dependent manner, resulting in release of IL-6 and IL-8. Based on these findings we report that PEPT2 plays a vital role in microbial recognition by NLR proteins, particularly with regard to airborne pathogens, thereby participating in host defense in the lung.
human; lung; bacterial; cell surface molecules; acute phase reactants