In Silico Molecular Comparisons of C. elegans and Mammalian Pharmacology Identify Distinct Targets That Regulate Feeding

Lemieux, George A.; Keiser, Michael J.; Sassano, Maria F.; Laggner, Christian; Mayer, Fahima; Bainton, Roland J.; Werb, Zena; Roth, Bryan L.; Shoichet, Brian K.; Ashrafi, Kaveh

doi:10.1371/journal.pbio.1001712

Cited by 18 publications

(15 citation statements)

References 62 publications

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“…We therefore investigated the occurrence of colloidal aggregators among three recent whole organism phenotypic screens, two on zebra fish embryos 41,42 and one on worms. 43 Of 93 active, machine readable compounds identified in the three screens, nine would be predicted to aggregate. This is a rate far below that expected for compounds emerging from biochemical, target-oriented HTS campaigns, at least from primary screens, consistent with the idea that at least whole organism screens are much less likely to enrich colloidal aggregators than are target-based screens (we have not compared with cell-based phenotypic screens, where aggregators may well be more prevalent, though the direction of their effects is hard to predict).…”

Section: Resultsmentioning

confidence: 99%

An Aggregation Advisor for Ligand Discovery

et al. 2015

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Colloidal aggregation of organic molecules is the dominant mechanism for artifactual inhibition of proteins, and controls against it are widely deployed. Notwithstanding an increasingly detailed understanding of this phenomenon, a method to reliably predict aggregation has remained elusive. Correspondingly, active molecules that act via aggregation continue to be found in early discovery campaigns and remain common in the literature. Over the past decade, over 12 thousand aggregating organic molecules have been identified, potentially enabling a precedent-based approach to match known aggregators with new molecules that may be expected to aggregate and lead to artifacts. We investigate an approach that uses lipophilicity, affinity, and similarity to known aggregators to advise on the likelihood that a candidate compound is an aggregator. In prospective experimental testing, five of seven new molecules with Tanimoto coefficients (Tc’s) between 0.95 and 0.99 to known aggregators aggregated at relevant concentrations. Ten of 19 with Tc’s between 0.94 and 0.90 and three of seven with Tc’s between 0.89 and 0.85 also aggregated. Another three of the predicted compounds aggregated at higher concentrations. This method finds that 61 827 or 5.1% of the ligands acting in the 0.1 to 10 µM range in the medicinal chemistry literature are at least 85% similar to a known aggregator with these physical properties and may aggregate at relevant concentrations. Intriguingly, only 0.73% of all drug-like commercially available compounds resemble the known aggregators, suggesting that colloidal aggregators are enriched in the literature. As a percentage of the literature, aggregator-like compounds have increased 9-fold since 1995, partly reflecting the advent of high-throughput and virtual screens against molecular targets. Emerging from this study is an aggregator advisor database and tool (http://advisor.bkslab.org), free to the community, that may help distinguish between fruitful and artifactual screening hits acting by this mechanism.

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Section: Resultsmentioning

confidence: 99%

An Aggregation Advisor for Ligand Discovery

et al. 2015

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“…We explored which combinations of conformer generation and E3FP parameters produced the most effective 3D fingerprints for the task of recovering correct ligand binders for over 2,000 protein targets using the Similarity Ensemble Approach (SEA). SEA compares sets of fingerprints against each other using Tanimoto coefficients (TC) and determines a p-value for the similarity among the two sets; it has been used to predict drug off-targets 4,5,37,38 , small molecule mechanisms of action [39][40][41] , and adverse drug reactions 4,42,43 . For the training library, we assembled a dataset of small molecule ligands that bind to at least one of the targets from the ChEMBL database with an IC 50 of 10 μM or better.…”

Section: Sea 3d Fingerprint Performance Exceeds That Of 2d In Bindingmentioning

confidence: 99%

A simple representation of three-dimensional molecular structure

Axen

Huang

Cáceres

et al. 2017

Preprint

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Statistical and machine learning approaches predict drug-to-target relationships from 2D smallmolecule topology patterns. One might expect 3D information to improve these calculations.Here we apply the logic of the Extended Connectivity FingerPrint (ECFP) to develop a rapid, alignment-invariant 3D representation of molecular conformers, the Extended ThreeDimensional FingerPrint (E3FP). By integrating E3FP with the Similarity Ensemble Approach (SEA), we achieve higher precision-recall performance relative to SEA with ECFP on ChEMBL20, and equivalent receiver operating characteristic performance. We identify classes of molecules for which E3FP is a better predictor of similarity in bioactivity than is ECFP.Finally, we report novel drug-to-target binding predictions inaccessible by 2D fingerprints and confirm three of them experimentally with ligand efficiencies from 0.442 -0.637 kcal/mol/heavy atom.

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“…This approach could enumerate testable hypotheses about how poorly understood and novel compounds affect behavioral phenotypes (Figure 3c). For example, in a recent study, we predicted targets for compounds found to modify C. elegans feeding behavior 61 . In the first stage, a screen yielded 84 phenotypically-related but structurally-diverse compounds, which we compared against more than two thousand human targets.…”

Section: Mining Phenotypically Related Compounds For Multi-target Mecmentioning

confidence: 99%

Leveraging Large-scale Behavioral Profiling in Zebrafish to Explore Neuroactive Polypharmacology

et al. 2016

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Many psychiatric drugs modulate the nervous system through multi-target mechanisms. However, systematic identification of multi-target compounds has been difficult using traditional in vitro screening assays. New approaches to phenotypic profiling in zebrafish can help researchers identify novel compounds with complex polypharmacology. For example, large-scale behavior-based chemical screens can rapidly identify large numbers of structurally diverse and phenotype-related compounds. Once these compounds have been identified, a systems-level analysis of their structures may help to identify statistically enriched target pathways. Together, systematic behavioral profiling and multi-target predictions may help researchers identify new behavior-modifying pathways and CNS therapeutics.

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In Silico Molecular Comparisons of C. elegans and Mammalian Pharmacology Identify Distinct Targets That Regulate Feeding

Abstract: This paper takes advantage of similarities between the C. elegans and human pharmacopeia to identify and validate pharmacological targets that regulate C. elegans feeding rates.

Cited by 18 publications

References 62 publications

An Aggregation Advisor for Ligand Discovery

An Aggregation Advisor for Ligand Discovery

A simple representation of three-dimensional molecular structure

Leveraging Large-scale Behavioral Profiling in Zebrafish to Explore Neuroactive Polypharmacology

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