The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry techniques are well-suited to high-throughput characterization of natural products, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social molecular networking (GNPS, http://gnps.ucsd.edu), an open-access knowledge base for community wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of ‘living data’ through continuous reanalysis of deposited data.
High-throughput screening (HTS) of chemical compounds to identify modulators of molecular targets is a mainstay of pharmaceutical development. Increasingly, HTS is being used to identify chemical probes of gene, pathway, and cell functions, with the ultimate goal of comprehensively delineating relationships between chemical structures and biological activities. Achieving this goal will require methodologies that efficiently generate pharmacological data from the primary screen and reliably profile the range of biological activities associated with large chemical libraries. Traditional HTS, which tests compounds at a single concentration, is not suited to this task, because HTS is burdened by frequent false positives and false negatives and requires extensive follow-up testing. We have developed a paradigm, quantitative HTS (qHTS), tested with the enzyme pyruvate kinase, to generate concentration-response curves for >60,000 compounds in a single experiment. We show that this method is precise, refractory to variations in sample preparation, and identifies compounds with a wide range of activities. Concentration-response curves were classified to rapidly identify pyruvate kinase activators and inhibitors with a variety of potencies and efficacies and elucidate structure-activity relationships directly from the primary screen. Comparison of qHTS with traditional single-concentration HTS revealed a high prevalence of false negatives in the single-point screen. This study demonstrates the feasibility of qHTS for accurately profiling every compound in large chemical libraries (>10 5 compounds). qHTS produces rich data sets that can be immediately mined for reliable biological activities, thereby providing a platform for chemical genomics and accelerating the identification of leads for drug discovery.1,536-well ͉ chemical genomics ͉ enzyme assay ͉ PubChem ͉ pyruvate kinase T he first description of biological effect versus chemical compound concentration was made by Paracelsus ca. 1534 and quantified by A. V. Hill in 1910 (1). The basis of these observations is that ligands affecting biological systems have optimal ranges of activity (EC 50 ), and give rise to concentration-effect relationships that can be complex, varying in potency, efficacy, and steepness of response. Far below an EC 50 , no effect may be seen (referred to as the no observable effect level or NOEL), and much above it, toxic or ''off-target'' effects may be observed. This well known behavior of chemical compounds in biological systems requires a specific dose of a compound to achieve a desired biological effect, whether in basic or clinical applications (2, 3).Historically, new compounds with medicinal qualities were discovered through laborious testing of samples using low-throughput assays including animal and isolated tissue models. In the early 1990s, the advent of combinatorial chemistry and commercial consolidation of small molecule collections resulted in a tremendous increase in compound numbers, requiring the development of high-throughput ...
High-throughput screening (HTS) is the primary technique for new lead identification in drug discovery and chemical biology. Unfortunately, it is susceptible to false-positive hits. One common mechanism for such false-positives is the congregation of organic molecules into colloidal aggregates, which nonspecifically inhibit enzymes. To both evaluate the feasibility of large-scale identification of aggregate-based inhibition and quantify its prevalence among screening hits, we tested 70 563 molecules from the National Institutes of Health Chemical Genomics Center (NCGC) library for detergent-sensitive inhibition. Each molecule was screened in at least seven concentrations, such that dose-response curves were obtained for all molecules in the library. There were 1274 inhibitors identified in total, of which 1204 were unambiguously detergentsensitive. We identified these as aggregate-based inhibitors. Thirty-one library molecules were independently purchased and retested in secondary low-throughput experiments; 29 of these were confirmed as either aggregators or nonaggregators, as appropriate. Finally, with the dose-response information collected for every compound, we could examine the correlation between aggregate-based inhibition and steep doseresponse curves. Three key results emerge from this study: first, detergent-dependent identification of aggregate-based inhibition is feasible on the large scale. Second, 95% of the actives obtained in this screen are aggregate-based inhibitors. Third, aggregate-based inhibition is correlated with steep dose-response curves, although not absolutely. The results of this screen are being released publicly via the PubChem database.
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