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Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

Persistent symptoms of COVID-19 survivors constitute long COVID syndrome, also called post-acute sequelae of SARS-CoV-2 infection (PASC). Neurologic manifestations of PASC (Neuro-PASC) are particularly debilitating, long lasting, and poorly understood. To gain insight into the pathogenesis of PASC, we leveraged a well-characterized group of Neuro-PASC (NP) patients seen at our Neuro-COVID-19 clinic who had mild acute COVID-19 and never required hospitalization to investigate their plasma proteome. Using the SomaLogic platform, SomaScan, the plasma concentration of >7000 proteins was measured from 92 unvaccinated individuals, including 48 NP patients, 20 COVID-19 convalescents (CC) without lingering symptoms, and 24 unexposed healthy controls (HC) to interrogate underlying pathobiology and potential biomarkers of PASC. We analyzed the plasma proteome based on post-COVID-19 status, neurologic and non-neurologic symptoms, as well as subjective and objective standardized tests for changes in quality-of-life (QoL) and cognition associated with Neuro-PASC. The plasma proteome of NP patients differed from CC and HC subjects more substantially than post-COVID-19 groups (NP and CC combined) differed from HC. Proteomic differences in NP patients 3-9 months following acute COVID-19 showed alterations in inflammatory proteins and pathways relative to CC and HC subjects. Proteomic associations with Neuro-PASC symptoms of brain fog and fatigue included changes in markers of DNA repair, oxidative stress, and neutrophil degranulation. Furthermore, we discovered a correlation between NP patients lower subjective impression of recovery to pre-COVID-19 baseline with an increase in the concentration of the oxidative phosphorylation protein COX7A1, which was also associated with neurologic symptoms and fatigue, as well as impairment in QoL and cognitive dysfunction. Finally, we identified other oxidative phosphorylation-associated proteins correlating with central nervous system symptoms. Our results suggest ongoing inflammatory changes and mitochondrial involvement in Neuro-PASC and pave the way for biomarker validation for use in monitoring and development of therapeutic intervention for this debilitating condition., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: The emergence and spread of Plasmodium falciparum parasites that lack HRP2/3 proteins and the resulting decreased utility of HRP2-based malaria rapid diagnostic tests (RDTs) prompted the World Health Organization and other global health stakeholders to prioritize the discovery of novel diagnostic biomarkers for malaria., Methods: To address this pressing need, we adopted a dual, systematic approach by conducting a systematic review of the literature for publications on diagnostic biomarkers for uncomplicated malaria and a systematic in silico analysis of P. falciparum proteomics data for Plasmodium proteins with favorable diagnostic features., Results: Our complementary analyses led us to 2 novel malaria diagnostic biomarkers compatible for use in an RDT format: glyceraldehyde 3-phosphate dehydrogenase and dihydrofolate reductase-thymidylate synthase., Conclusions: Overall, our results pave the way for the development of next-generation malaria RDTs based on new antigens by identifying 2 lead candidates with favorable diagnostic features and partially de-risked product development prospects., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America.)

Background Chronic kidney disease (CKD) confers increased cardiovascular risk, not fully explained by traditional factors. Proteins regulate biological processes and inform the risk of diseases. Thus, in 938 patients with stable coronary heart disease from the Heart and Soul cohort, we quantified 1054 plasma proteins using modified aptamers (SOMAscan) to: (1) discern how reduced glomerular filtration influences the circulating proteome, (2) learn of the importance of kidney function to the prognostic information contained in recently identified protein cardiovascular risk biomarkers, and (3) identify novel and even unique cardiovascular risk biomarkers among individuals with CKD. Methods and Results Plasma protein levels were correlated to estimated glomerular filtration rate (eGFR) using Spearman-rank correlation coefficients. Cox proportional hazard models were used to estimate the association between individual protein levels and the risk of the cardiovascular outcome (first among myocardial infarction, stroke, heart failure hospitalization, or mortality). Seven hundred and nine (67.3%) plasma proteins correlated with eGFR at P <0.05 (ρ 0.06-0.74); 218 (20.7%) proteins correlated with eGFR moderately or strongly (ρ 0.2-0.74). Among the previously identified 196 protein cardiovascular biomarkers, just 87 remained prognostic after correction for eGFR. Among patients with CKD (eGFR <60 mL/min per 1.73 m 2 ), we identified 21 protein cardiovascular risk biomarkers of which 8 are unique to CKD. Conclusions CKD broadly alters the composition of the circulating proteome. We describe protein biomarkers capable of predicting cardiovascular risk independently of glomerular filtration, and those that are prognostic of cardiovascular risk specifically in patients with CKD and even unique to patients with CKD.

Proteins are effector molecules that mediate the functions of genes 1,2 and modulate comorbidities 3-10 , behaviors and drug treatments 11 . They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning 12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

Purpose: The application of proteomics in chronic kidney disease (CKD) can potentially uncover biomarkers and pathways that are predictive of disease., Experimental Design: Within this context, this study examines the relationship between the human plasma proteome and glomerular filtration rate (GFR) as measured by iohexol clearance in a cohort from Sweden (n = 389; GFR range: 8-100 mL min -1 /1.73 m 2 ). A total of 2893 proteins are quantified using a modified aptamer assay., Results: A large proportion of the proteome is associated with GFR, reinforcing the concept that CKD affects multiple physiological systems (individual protein-GFR correlations listed here). Of these, cystatin C shows the most significant correlation with GFR (rho = -0.85, p = 1.2 × 10 -97 ), establishing strong validation for the use of this biomarker in CKD diagnostics. Among the other highly significant protein markers are insulin-like growth factor-binding protein 6, neuroblastoma suppressor of tumorigenicity 1, follistatin-related protein 3, trefoil factor 3, and beta-2 microglobulin. These proteins may indicate an imbalance in homeostasis across a variety of cellular processes, which may be underlying renal dysfunction., Conclusions and Clinical Relevance: Overall, this study represents the most extensive characterization of the plasma proteome and its relation to GFR to date, and suggests the diagnostic and prognostic value of proteomics for CKD across all stages., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: Early detection of adverse effects of novel therapies and understanding of their mechanisms could improve the safety and efficiency of drug development. We have retrospectively applied large-scale proteomics to blood samples from ILLUMINATE (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events), a trial of torcetrapib (a cholesterol ester transfer protein inhibitor), that involved 15 067 participants at high cardiovascular risk. ILLUMINATE was terminated at a median of 550 days because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in mortality with torcetrapib. The aims of our analysis were to determine whether a proteomic analysis might reveal biological mechanisms responsible for these harmful effects and whether harmful effects of torcetrapib could have been detected early in the ILLUMINATE trial with proteomics., Methods: A nested case-control analysis of paired plasma samples at baseline and at 3 months was performed in 249 participants assigned to torcetrapib plus atorvastatin and 223 participants assigned to atorvastatin only. Within each treatment arm, cases with events were matched to controls 1:1. Main outcomes were a survey of 1129 proteins for discovery of biological pathways altered by torcetrapib and a 9-protein risk score validated to predict myocardial infarction, stroke, heart failure, or death., Results: Plasma concentrations of 200 proteins changed significantly with torcetrapib. Their pathway analysis revealed unexpected and widespread changes in immune and inflammatory functions, as well as changes in endocrine systems, including in aldosterone function and glycemic control. At baseline, 9-protein risk scores were similar in the 2 treatment arms and higher in participants with subsequent events. At 3 months, the absolute 9-protein derived risk increased in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm by 1.08% ( P =0.0004). Thirty-seven proteins changed in the direction of increased risk of 49 proteins previously associated with cardiovascular and mortality risk., Conclusions: Heretofore unknown effects of torcetrapib were revealed in immune and inflammatory functions. A protein-based risk score predicted harm from torcetrapib within just 3 months. A protein-based risk assessment embedded within a large proteomic survey may prove to be useful in the evaluation of therapies to prevent harm to patients., Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00134264., (© 2017 The Authors.)

Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.

Serum biomarkers in Duchenne muscular dystrophy (DMD) may provide deeper insights into disease pathogenesis, suggest new therapeutic approaches, serve as acute read-outs of drug effects, and be useful as surrogate outcome measures to predict later clinical benefit. In this study a large-scale biomarker discovery was performed on serum samples from patients with DMD and age-matched healthy volunteers using a modified aptamer-based proteomics technology. Levels of 1,125 proteins were quantified in serum samples from two independent DMD cohorts: cohort 1 (The Parent Project Muscular Dystrophy-Cincinnati Children's Hospital Medical Center), 42 patients with DMD and 28 age-matched normal volunteers; and cohort 2 (The Cooperative International Neuromuscular Research Group, Duchenne Natural History Study), 51 patients with DMD and 17 age-matched normal volunteers. Forty-four proteins showed significant differences that were consistent in both cohorts when comparing DMD patients and healthy volunteers at a 1% false-discovery rate, a large number of significant protein changes for such a small study. These biomarkers can be classified by known cellular processes and by age-dependent changes in protein concentration. Our findings demonstrate both the utility of this unbiased biomarker discovery approach and suggest potential new diagnostic and therapeutic avenues for ameliorating the burden of DMD and, we hope, other rare and devastating diseases.

A series of 2,3,4,4a,10,10a-hexahydropyrano[3,2-b]chromene analogs was developed that demonstrated high selectivity (>2000-fold) for BACE1 vs Cathepsin D (CatD). Three different Asp-binding moieties were examined: spirocyclic acyl guanidines, aminooxazolines, and aminothiazolines in order to modulate potency, selectivity, efflux, and permeability. Guided by structure based design, changes to P2' and P3 moieties were explored. A conformationally restricted P2' methyl group provided inhibitors with excellent cell potency (37-137 nM) and selectivity (435 to >2000-fold) for BACE1 vs CatD. These efforts lead to compound 59, which demonstrated a 69% reduction in rat CSF Aβ1-40 at 60 mg/kg (PO).

In an attempt to increase selectivity vs Cathepsin D (CatD) in our BACE1 program, a series of 1,3,4,4a,10,10a-hexahydropyrano[4,3-b]chromene analogues was developed. Three different Asp-binding moieties were examined: spirocyclic acyl guanidines, aminooxazolines, and aminothiazolines in order to modulate potency, selectivity, efflux, and permeability. Using structure-based design, substitutions to improve binding to both the S3 and S2' sites of BACE1 were explored. An acyl guanidine moiety provided the most potent analogues. These compounds demonstrated 10-420 fold selectivity for BACE1 vs CatD, and were highly potent in a cell assay measuring Aβ1-40 production (5-99 nM). They also suffered from high efflux. Despite this undesirable property, two of the acyl guanidines achieved free brain concentrations (Cfree,brain) in a guinea pig PD model sufficient to cover their cell IC50s. Moreover, a significant reduction of Aβ1-40 in guinea pig, rat, and cyno CSF (58%, 53%, and 63%, respectively) was observed for compound 62.

The in silico construction of a PDGFRβ kinase homology model and ensuing medicinal chemistry guided by molecular modeling, led to the identification of potent, small molecule inhibitors of PDGFR. Subsequent exploration of structure-activity relationships (SAR) led to the incorporation of a constrained secondary amine to enhance selectivity. Further refinements led to the integration of a fluorine substituted piperidine, which resulted in significant reduction of P-glycoprotein (Pgp) mediated efflux and improved bioavailability. Compound 28 displayed oral exposure in rodents and had a pronounced effect in a pharmacokinetic-pharmacodynamic (PKPD) assay.

A hallmark of Alzheimer's disease is the brain deposition of amyloid beta (Aβ), a peptide of 36-43 amino acids that is likely a primary driver of neurodegeneration. Aβ is produced by the sequential cleavage of APP by BACE1 and γ-secretase; therefore, inhibition of BACE1 represents an attractive therapeutic target to slow or prevent Alzheimer's disease. Herein we describe BACE1 inhibitors with limited molecular flexibility and molecular weight that decrease CSF Aβ in vivo, despite efflux. Starting with spirocycle 1a, we explore structure-activity relationships of core changes, P3 moieties, and Asp binding functional groups in order to optimize BACE1 affinity, cathepsin D selectivity, and blood-brain barrier (BBB) penetration. Using wild type guinea pig and rat, we demonstrate a PK/PD relationship between free drug concentrations in the brain and CSF Aβ lowering. Optimization of brain exposure led to the discovery of (R)-50 which reduced CSF Aβ in rodents and in monkey.

Malaria, the disease caused by infection with protozoan parasites from the genus Plasmodium, claims the lives of nearly 1 million people annually. Developing nations, particularly in the African Region, bear the brunt of this malaria burden. Alarmingly, the most dangerous etiologic agent of malaria, Plasmodium falciparum, is becoming increasingly resistant to current first-line antimalarials. In light of the widespread devastation caused by malaria, the emergence of drug-resistant P. falciparum strains, and the projected decrease in funding for malaria eradication that may occur over the next decade, the identification of promising new targets for antimalarial drug design is imperative. P. falciparum kinases have been proposed as ideal drug targets for antimalarial drug design because they mediate critical cellular processes within the parasite and are, in many cases, structurally and mechanistically divergent when compared with kinases from humans. Identifying a molecule capable of inhibiting the activity of a target enzyme is generally an arduous and expensive process that can be greatly aided by utilizing in silico drug design techniques. Such methods have been extensively applied to human kinases, but as yet have not been fully exploited for the exploration and characterization of antimalarial kinase targets. This review focuses on in silico methods that have been used for the evaluation of potential antimalarials and the Plasmodium kinases that could be explored using these techniques.

Cytochrome P450 enzymes are the predominant mediators of phase I metabolism of exogenous small molecules. As a result of their extensive role in metabolism of xenobiotics, drug compounds, and endogenous compounds, as well as their wide tissue distribution, significant drug discovery resources are spent to avoid interacting with this class of enzymes. Here we review historical and recent in silico modeling of 7 cytochrome P450 enzymes of particular interest, specifically CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4. For each we provide a brief biological background including known inhibitors, substrates, and inducers, as well as details of computational modeling efforts and advances in structural biology. We also provide similar details for 3 nuclear receptors known to regulate gene expression of these enzyme families.

Gemfibrozil-1-O-beta-glucuronide (GEM-1-O-gluc), a major metabolite of the antihyperlipidemic drug gemfibrozil, is a mechanism-based inhibitor of P450 2C8 in vitro, and this irreversible inactivation may lead to clinical drug-drug interactions between gemfibrozil and other P450 2C8 substrates. In light of this in vitro finding and the observation that the glucuronide conjugate does not contain any obvious structural alerts, the current study was conducted to determine the potential site of GEM-1-O-gluc bioactivation and the subsequent mechanism of P450 2C8 inhibition (i.e., modification of apoprotein or heme). LC/MS analysis of a reaction mixture containing recombinant P450 2C8 and GEM-1-O-gluc revealed that the substrate was covalently linked to the heme prosthetic heme group during catalysis. A combination of mass spectrometry and deuterium isotope effects revealed that a benzylic carbon on the 2',5'-dimethylphenoxy group of GEM-1-O-gluc was covalently bound to the heme of P450 2C8. The regiospecificity of substrate addition to the heme group was not confirmed experimentally, but computational modeling experiments indicated that the gamma-meso position was the most likely site of modification. The metabolite profile, which consisted of two benzyl alcohol metabolites and a 4'-hydroxy-GEM-1-O-gluc metabolite, indicated that oxidation of GEM-1-O-gluc was limited to the 2',5'-dimethylphenoxy group. These results are consistent with an inactivation mechanism wherein GEM-1-O-gluc is oxidized to a benzyl radical intermediate, which evades oxygen rebound, and adds to the gamma-meso position of heme. Mechanism-based inhibition of P450 2C8 can be rationalized by the formation of the GEM-1-O-gluc-heme adduct and the consequential restriction of additional substrate access to the catalytic iron center.

We describe and demonstrate a method for the simultaneous, fully flexible alignment of multiple molecules with a common biological activity. The key aspect of the algorithm is that the alignment problem is first solved in a lower dimensional space, in this case using the one-dimensional representations of the molecules. The three-dimensional alignment is then guided by constraints derived from the one-dimensional alignment. We demonstrate using 10 hERG channel blockers, with a total of 72 rotatable bonds, that the one-dimensional alignment is able to effectively isolate key conserved pharmacophoric features and that these conserved features can effectively guide the three-dimensional alignment. Further using 10 estrogen receptor agonists and 5 estrogen receptor antagonists with publicly available cocrystal structures we show that the method is able to produce superpositions comparable to those derived from crystal structures. Finally, we demonstrate, using examples from peptidic CXCR3 agonists, that the method is able to generate reasonable binding hypotheses.

Self-organizing maps (SOMs) are a type of artificial neural network that through training can produce simplified representations of large, high dimensional data sets. These representations are typically used for visualization, classification, and clustering and have been successfully applied to a variety of problems in the pharmaceutical and bioinformatics domains. SOMs in these domains have generally been restricted to static sets of nodes connected in either a grid or hexagonal connectivity and planar or toroidal topologies. We investigate the impact of connectivity and topology on SOM performance, and experiments were performed on fixed and growing SOMs. Three synthetic and two relevant data sets from the chemistry domain were used for evaluation, and performance was assessed on the basis of topological and quantization errors after equivalent training periods. Although we found that all SOMs were roughly comparable at quantizing a data space, there was wide variation in the ability to capture its underlying structure, and growing SOMs consistently outperformed their static counterparts in regards to topological errors. Additionally, one growing SOM, the Neural Gas, was found to be far more capable of capturing details of a target data space, finding lower dimensional relationships hidden within higher dimensional representations.

Multiple sequence alignment has proven to be a powerful method for creating protein and DNA sequence alignment profiles. These profiles of protein families are useful tools for identifying conserved motifs, such as the catalytic triad of the serine protease family or the seven transmembrane helices of the G-protein coupled receptor family. Ultimately, the understanding of the critical motifs within a family is useful for identifying new members of the family. Due to the complexity of protein-ligand recognition, no universally accepted method exists for clustering small molecules into families with the same or similar biological activity. A combination of the concept of multiple sequence alignment and the 1-dimensional molecular representation described earlier offers a new method for profiling sets of small molecules with the same biological activity. These small molecule profiles can isolate key commonalities within the set of bioactive compounds much like a multiple sequence alignment can isolate critical motifs within a protein family. The small molecule profiles then make useful tools for searching small molecule databases for new compounds with the same biological activity. The technique is demonstrated here using the human ether-a-go-go potassium channel and the kinase SRC.

Despite controversy over their use and the potential for toxic side effects, cardiac glycosides have remained an important clinical component for the treatment for congestive heart failure (CHF) and supraventricular arrhythmias since the effects of Digitalis purpurea were first described in 1785. While there is a wealth of information available with regard to the effects of these drugs on their pharmacological receptor, the Na(+), K(+)-ATPase, the exact molecular mechanism of digitalis binding and inhibition of the enzyme has remained elusive. In particular, the absence of structural knowledge about Na(+), K(+)-ATPase has thwarted the development of improved therapeutic agents with larger therapeutic indices via rational drug design approaches. Here, we propose a binding mode for digoxin and several analogues to the Na(+), K(+)-ATPase. A 3D-structural model of the extracellular loop regions of the catalytic alpha1-subunit of the digitalis-sensitive sheep Na(+), K(+)-ATPase was constructed from the crystal structure of an E(1)Ca(2+) conformation of the SERCA1a and a consensus orientation for digitalis binding was inferred from the in silico docking of a series of steroid-based cardiotonic compounds. Analyses of species-specific enzyme affinities for ouabain were also used to validate the model and, for the first time, propose a detailed model of the digitalis binding site.

In recent years the trend in combinatorial library design has shifted to include target class focusing along with diversity and drug-likeness criteria. In this manuscript we review the computational tools available for target class library design and highlight the areas where they have proven useful in our work. The protein kinase family is used to illustrated structure-based target class focused library design, and the G-protein coupled receptor (GPCR) family is used to illustrate ligand-based target class focused library design. Most of the tools discussed are those designed for libraries targeted to a single protein and are simply applied "brute-force" to a large number of targets within the family. The tools that have proven to be the most useful in our work are those that can extract trends from the computational data such as docking and clustering or data mining large amounts of structure activity or high throughput screening data. Finally, areas where improvements are needed in the computational tools available for target class focusing are highlighted. These areas include tools to extract the relevant patterns from all available information for a family of targets, tools to efficiently apply models for all targets in the family rather than just a small subset, mining tools to extract the relevant information from the computational absorption, distribution, metabolism, excretion and toxicity (ADMET) and targeting data, and tools to allow interactive exploration of the virtual space around a library to facilitate the selection of the library that best suits the needs of the design team.

Decision trees have been used extensively in cheminformatics for modeling various biochemical endpoints including receptor-ligand binding, ADME properties, environmental impact, and toxicity. The traditional approach to inducing decision trees based upon a given training set of data involves recursive partitioning which selects partitioning variables and their values in a greedy manner to optimize a given measure of purity. This methodology has numerous benefits including classifier interpretability and the capability of modeling nonlinear relationships. The greedy nature of induction, however, may fail to elucidate underlying relationships between the data and endpoints. Using evolutionary programming, decision trees are induced which are significantly more accurate than trees induced by recursive partitioning. Furthermore, when assessed on previously unseen data in a 10-fold cross-validated manner, evolutionary programming induced trees exhibit a significantly higher accuracy on previously unseen data. This methodology is compared to single-tree and multiple-tree recursive partitioning in two domains (aerobic biodegradability and hepatotoxicity) and shown to produce less complex classifiers with average increases in predictive accuracy of 5-10% over the traditional method.

In the present study, values of the binding energy (BE) were calculated for the rat androgen receptor on a data set of 25 steroidal and nonsteroidal compounds for which published values of the observed binding affinity (K(i)) are available. A correlation between BE and pK(i) was evident (r(2) = 0.50) for the entire data set and became more pronounced when the steroids and nonsteroids were plotted separately (r(2) congruent with 0.76). Including BE as an additional descriptor to supplement the default steric-electrostatic descriptors in comparative molecular field analysis dramatically improved the predictive ability of the resulting three-dimensional quantitative structure-activity relationship models. We also demonstrate that the observed loss in ligand specificity between the wild-type (wt) AR and the T877A mutant AR associated with androgen-independent prostate cancer is reflected in decreased BE values (i.e., higher binding affinity) for the antiandrogen pharmaceutical hydroxyflutamide and for several nonandrogenic endogenous steroids, most notably cortisol, corticosterone, 17beta-estradiol, progesterone, and 17alpha-hydroxyprogesterone.

Identification of several environmental chemicals capable of binding to the androgen receptor (AR) and interfering with its normal function has heightened concern about adverse effects across a broad spectrum of environmental chemicals. We previously demonstrated AR antagonist activity of the organophosphate (OP) pesticide fenitrothion. In this study, we characterized AR activity of analogues of fenitrothion to probe the structural requirements for AR activity among related chemicals. AR activity was measured using HepG2 human hepatoma cells transfected with human AR plus an androgen-responsive luciferase reporter gene, MMTV-luc. AR antagonist activity decreased as alkyl chain length of the phosphoester increased, whereas electron-donating properties of phenyl substituents of the tested compounds did not influence AR activity. Oxon derivatives of fenitrothion, which are more likely to undergo hydrolytic degradation, had no detectable AR antagonist activity. Molecular modeling results suggest that hydrogen-bond energies and the maximum achievable interatomic distance between two terminal H-bond capable sites may influence both the potential to interact with the AR and the nature of the interaction (agonist vs. antagonist) within this series of chemicals. This hypothesis is supported by the results of recent AR homology modeling and crystallographic studies relative to agonist- and antagonist-bound AR complexes. The present results are placed in the context of structure-activity knowledge derived from previous modeling studies as well as studies aimed toward designing nonsteroidal antiandrogen pharmaceuticals. Present results extend understanding of the structural requirements for AR activity to a new class of nonsteroidal, environmental, OP-related chemicals.

Estrogen is a steroid hormone playing critical roles in physiological processes such as sexual differentiation and development, female and male reproductive processes, and bone health. Numerous natural and synthetic environmental compounds have been shown capable of estrogenic effects. This area has been the focus of significant fundamental and applied research due both to the potential detrimental effects of these compounds upon normal physiological processes and to the potential beneficial effects of tissue-selective estrogen agonists/antagonists for the prevention and treatment of numerous diseases. Genomic effects of the active form of estrogen, 17beta-estradiol, are mediated through at least two members of the steroid hormone receptor superfamily, estrogen receptor subtype alpha (ER-alpha) and estrogen receptor subtype beta (ER-beta). At the time of this work, the X-ray crystal structure of the ER-alpha had been elucidated, however, coordinates of the ER-beta were not publicly available. Based upon the significant structural conservation across members of the steroid hormone receptor family, and the high sequence homology between ER-alpha and ER-beta (>60%), we have developed a homology model of the ER-beta structure. Using the crystal structure of ER-alpha and the homology model of ER-beta, we demonstrate a strong correlation between computed values of the binding-energy and published values of the observed relative binding affinity (RBA) for a variety of compounds for both receptors, as well as the ability to identify receptor subtype selective compounds. Furthermore, using the recently available crystal structure of ER-beta for comparison purposes, we show that not only is the predicted homology model structurally accurate, but that it can be used to assess ligand binding affinities.

A ligand-inducible transactivation function (AF-2) exists in the extreme carboxyl terminus of the vitamin D receptor (VDR) that is essential for 1alpha,25-dihydroxyvitamin D3 (1,25-(OH)2D3)-activated transcription and p160 coactivator interaction. Crystallographic data of related nuclear receptors suggest that binding of 1, 25-(OH)2D3 by VDR induces conformational changes in the ligand-binding domain (LBD), the most striking of which is a packing of the AF-2 helix onto the LBD adjacent to helices H3 and H4. In this study, a panel of VDR helix H3 mutants was generated, and residues in helix H3 that are important for ligand-activated transcription by the full-length VDR were identified. In particular, one mutant (VDR (Y236A)) exhibited normal ligand binding and heterodimerization with the retinoid X receptor (RXR) but was transcriptionally inactive. Yeast two-hybrid studies and in vitro protein interaction assays demonstrated that VDR (Y236A) was selectively impaired in interaction with AF-2-interacting coactivator proteins such as SRC-1 and GRIP-1. These data indicate an importance of helix H3 in the mechanism of VDR-mediated transcription, and they support the concept that helix H3 functions in concert with the AF-2 domain to form a transactivation surface for binding the p160 class of nuclear receptor coactivators.

Background and Purpose: We have previously shown that traumatic injury of hippocampal cells triggers release of a soluble neurotoxin that can be transferred to an uninjured culture. The mechanism of this trauma-induced neurotoxicity is independent of glutamate receptor activation. We extended this observation to study the mechanism of this neurotoxicity., Methods: Dissociated rat hippocampal neurons were traumatized by disrupting the culture by scratching the plate. The toxicity expressed by the injured culture was studied by transferring the medium to an uninjured culture and assessing the death rate by trypan blue exclusion., Results: This neurotoxin is stable in the medium at room temperature for several hours and withstands boiling. The molecular weight is between 100 and 500. The release and the effect of this toxin seem to be independent of glutamate receptor activation. The toxicity is unaffected by removal of extracellular calcium. However, dantrolene dose-dependently blocked the toxicity in the recipient culture, suggesting that the release of intracellular stores of calcium is involved in the toxic effect. This release of calcium is likely to be followed by an activation of nitric oxide synthase because competitive nitric oxide synthase inhibitors attenuated this toxicity. Consistent with this result, cholecystokinin octapeptide significantly reduced cell death when combined with this toxic medium., Conclusions: Traumatic injury of dissociated cells can propagate neurotoxicity in uninjured cells by a soluble toxin released into the extracellular space. This toxin causes a rise in cytosolic calcium that activates nitric oxide synthase that can be blocked by cholecystokinin.

The vitamin D receptor (VDR) heterodimerizes with retinoid X receptors (RXR) on many vitamin D-responsive promoter elements, suggesting that this complex is the active factor in vitamin D-mediated transcription. However, the mechanism of transcriptional regulation following VDR-RXR binding to DNA is not well characterized. Using a yeast two-hybrid protein interaction assay, we demonstrate that VDR forms specific protein: protein contacts with the basal transcription factor TFIIB. Deletion analysis indicated that the carboxyl-terminal ligand binding domain of VDR interacted with a 43-residue amino-terminal domain in TFIIB. The interaction with TFIIB showed selectivity for the ligand binding domain of VDR as similar regions of RXR alpha or of retinoic acid receptor alpha did not couple with TFIIB. Binding assays with purified proteins showed a direct interaction between VDR and TFIIB in vitro. These data suggest a mechanism for VDR-dependent transcription in which protein contacts between VDR and TFIIB may impart regulatory information to the transcription preinitiation complex.