Identifying Ligand Binding Conformations of the β2-Adrenergic Receptor by Using Its Agonists as Computational Probes

Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β2-adrenergic receptor (β2AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of β2AR. We show that the select ligands bind preferentially to different predicted conformers of β2AR, and identify a role of β2AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as "computational probes" to systematically identify protein conformers with likely biological significance. © 2012 Isin et al

Quantitative assessment of the expanding complementarity between public and commercial databases of bioactive compounds

<p>Abstract</p> <p>Background</p> <p>Since 2004 public cheminformatic databases and their collective functionality for exploring relationships between compounds, protein sequences, literature and assay data have advanced dramatically. In parallel, commercial sources that extract and curate such relationships from journals and patents have also been expanding. This work updates a previous comparative study of databases chosen because of their bioactive content, availability of downloads and facility to select informative subsets.</p> <p>Results</p> <p>Where they could be calculated, extracted compounds-per-journal article were in the range of 12 to 19 but compound-per-protein counts increased with document numbers. Chemical structure filtration to facilitate standardised comparisons typically reduced source counts by between 5% and 30%. The pair-wise overlaps between 23 databases and subsets were determined, as well as changes between 2006 and 2008. While all compound sets have increased, PubChem has doubled to 14.2 million. The 2008 comparison matrix shows not only overlap but also unique content across all sources. Many of the detailed differences could be attributed to individual strategies for data selection and extraction. While there was a big increase in patent-derived structures entering PubChem since 2006, GVKBIO contains over 0.8 million unique structures from this source. Venn diagrams showed extensive overlap between compounds extracted by independent expert curation from journals by GVKBIO, WOMBAT (both commercial) and BindingDB (public) but each included unique content. In contrast, the approved drug collections from GVKBIO, MDDR (commercial) and DrugBank (public) showed surprisingly low overlap. Aggregating all commercial sources established that while 1 million compounds overlapped with PubChem 1.2 million did not.</p> <p>Conclusion</p> <p>On the basis of chemical structure content <it>per se </it>public sources have covered an increasing proportion of commercial databases over the last two years. However, commercial products included in this study provide links between compounds and information from patents and journals at a larger scale than current public efforts. They also continue to capture a significant proportion of unique content. Our results thus demonstrate not only an encouraging overall expansion of data-supported bioactive chemical space but also that both commercial and public sources are complementary for its exploration.</p

A global view of drug-therapy interactions

Network science is already making an impact on the study of complex systems and offers a promising variety of tools to understand their formation and evolution (1-4) in many disparate fields from large communication networks (5,6), transportation infrastructures (7) and social communities (8,9) to biological systems (1,10,11). Even though new highthroughput technologies have rapidly been generating large amounts of genomic data, drug design has not followed the same development, and it is still complicated and expensive to develop new single-target drugs. Nevertheless, recent approaches suggest that multi-target drug design combined with a network-dependent approach and large-scale systems-oriented strategies (12-14) create a promising framework to combat complex multigenetic disorders like cancer or diabetes. Here, we investigate the human network corresponding to the interactions between all US approved drugs and human therapies, defined by known drug-therapy relationships. Our results show that the key paths in this network are shorter than three steps, indicating that distant therapies are separated by a surprisingly low number of chemical compounds. We also identify a sub-network composed by drugs with high centrality measures (15), which represent the structural back-bone of the drug-therapy system and act as hubs routing information between distant parts of the network. These findings provide for the first time a global map of the largescale organization of all known drugs and associated therapies, bringing new insights on possible strategies for future drug development. Special attention should be given to drugs which combine the two properties of (a) having a high centrality value and (b) acting on multiple targets.Comment: 16 pages, 4 figures. It was submitted to peer review on August 15, 200

Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: Ranking 1654 human protein targets by assayed compounds and molecular scaffolds

<p>Abstract</p> <p>Background</p> <p>Since the classic Hopkins and Groom druggable genome review in 2002, there have been a number of publications updating both the hypothetical and successful human drug target statistics. However, listings of research targets that define the area between these two extremes are sparse because of the challenges of collating published information at the necessary scale. We have addressed this by interrogating databases, populated by expert curation, of bioactivity data extracted from patents and journal papers over the last 30 years.</p> <p>Results</p> <p>From a subset of just over 27,000 documents we have extracted a set of compound-to-target relationships for biochemical <it>in vitro </it>binding-type assay data for 1,736 human proteins and 1,654 gene identifiers. These are linked to 1,671,951 compound records derived from 823,179 unique chemical structures. The distribution showed a compounds-per-target average of 964 with a maximum of 42,869 (Factor Xa). The list includes non-targets, failed targets and cross-screening targets. The top-278 most actively pursued targets cover 90% of the compounds. We further investigated target ranking by determining the number of molecular frameworks and scaffolds. These were compared to the compound counts as alternative measures of chemical diversity on a per-target basis.</p> <p>Conclusions</p> <p>The compounds-per-protein listing generated in this work (provided as a supplementary file) represents the major proportion of the human drug target landscape defined by published data. We supplemented the simple ranking by the number of compounds assayed with additional rankings by molecular topology. These showed significant differences and provide complementary assessments of chemical tractability.</p

A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism

Effects of the high-density lipoprotein mimetic agent CER-001 on coronary atherosclerosis in patients with acute coronary syndromes: A randomized trial

AIM: High-density lipoproteins (HDLs) have several potentially protective vascular effects. Most clinical studies of therapies targeting HDL have failed to show benefits vs. placebo. OBJECTIVE: To investigate the effects of an HDL-mimetic agent on atherosclerosis by intravascular ultrasonography (IVUS) and quantitative coronary angiography (QCA). DESIGN AND SETTING: A prospective, double-blinded, randomized trial was conducted at 51 centres in the USA, the Netherlands, Canada, and France. Intravascular ultrasonography and QCA were performed to assess coronary atherosclerosis at baseline and 3 (2-5) weeks after the last study infusion. PATIENTS: Five hundred and seven patients were randomized; 417 and 461 had paired IVUS and QCA measurements, respectively. INTERVENTION: Patients were randomized to receive 6 weekly infusions of placebo, 3 mg/kg, 6 mg/kg, or 12 mg/kg CER-001. MAIN OUTCOME MEASURES: The primary efficacy parameter was the nominal change in the total atheroma volume. Nominal changes in per cent atheroma volume on IVUS and coronary scores on QCA were also pre-specified endpoints. RESULTS: The nominal change in the total atheroma volume (adjusted means) was -2.71, -3.13, -1.50, and -3.05 mm(3) with placebo, CER-001 3 mg/kg, 6 mg/kg, and 12 mg/kg, respectively (primary analysis of 12 mg/kg vs. placebo: P = 0.81). There was also no difference among groups for the nominal change in per cent atheroma volume (0.02, -0.02, 0.01, and 0.19%; nominal P = 0.53 for 12 mg/kg vs. placebo). Change in the coronary artery score was -0.022, -0.036, -0.022, and -0.015 mm (nominal P = 0.25, 0.99, 0.55), and change in the cumulative coronary stenosis score was -0.51, 2.65, 0.71, and -0.77% (compared with placebo, nominal P = 0.85 for 12 mg/kg and nominal P = 0.01 for 3 mg/kg). The number of patients with major cardiovascular events was 10 (8.3%), 16 (13.3%), 17 (13.7%), and 12 (9.8%) in the four groups. CONCLUSION: CER-001 infusions did not reduce coronary atherosclerosis on IVUS and QCA when compared with placebo. Whether CER-001 administered in other regimens or to other populations could favourably affect atherosclerosis must await further study. Name of the trial registry: Clinicaltrials.gov; Registry's URL: http://clinicaltrials.gov/ct2/show/NCT01201837?term=cer-001&rank=2; TRIAL REGISTRATION NUMBER: NCT01201837

Effects of dietary carotenoids on mouse lung genomic profiles and their modulatory effects on short-term cigarette smoke exposures

Male C57BL/6 mice were fed diets supplemented with either β-carotene (BC) or lycopene (LY) that were formulated for human consumption. Four weeks of dietary supplementations results in plasma and lung carotenoid (CAR) concentrations that approximated the levels detected in humans. Bioactivity of the CARs was determined by assaying their effects on the activity of the lung transcriptome (~8,500 mRNAs). Both CARs activated the cytochrome P450 1A1 gene but only BC induced the retinol dehydrogenase gene. The contrasting effects of the two CARs on the lung transcriptome were further uncovered in mice exposed to cigarette smoke (CS) for 3 days; only LY activated ~50 genes detected in the lungs of CS-exposed mice. These genes encoded inflammatory-immune proteins. Our data suggest that mice offer a viable in vivo model for studying bioactivities of dietary CARs and their modulatory effects on lung genomic expression in both health and after exposure to CS toxicants

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference