Large-scale bioactivity analysis of the small-molecule assayed proteome.
This study presents an analysis of the small molecule bioactivity profiles across large quantities of diverse protein families represented in PubChem BioAssay. We compared the bioactivity profiles of FDA approved drugs to non-FDA approved compounds, and report several distinct patterns characteristic of the approved drugs. We found that a large fraction of the previously reported higher target promiscuity among FDA approved compounds, compared to non-FDA approved bioactives, was frequently due to cross-reactivity within rather than across protein families. We identified 804 potentially novel protein target candidates for FDA approved drugs, as well as 901 potentially novel target candidates with active non-FDA approved compounds, but no FDA approved drugs with activity against these targets. We also identified 486348 potentially novel compounds active against the same targets as FDA approved drugs, as well as 153402 potentially novel compounds active against targets without active FDA approved drugs. By quantifying the agreement among replicated screens, we estimated that more than half of these novel outcomes are reproducible. Using biclustering, we identified many dense clusters of FDA approved drugs with enriched activity against a common set of protein targets. We also report the distribution of compound promiscuity using a Bayesian statistical model, and report the sensitivity and specificity of two common methods for identifying promiscuous compounds. Aggregator assays exhibited greater accuracy in identifying highly promiscuous compounds, while PAINS substructures were able to identify a much larger set of "middle range" promiscuous compounds. Additionally, we report a large number of promiscuous compounds not identified as aggregators or PAINS. In summary, the results of this study represent a rich reference for selecting novel drug and target protein candidates, as well as for eliminating candidate compounds with unselective activities.
Department of Bioengineering, University of California Riverside, Riverside, California, United States of America.|Institute for Integrative Genome Biology, University of California Riverside, Riverside, California, United States of America.
Tyler William H
TW
Backman
California Pacific Medical Center Research Institute, San Francisco, California, United States of America.
Daniel S
DS
Evans
Institute for Integrative Genome Biology, University of California Riverside, Riverside, California, United States of America.
Thomas
http://orcid.org/0000-0003-0710-3777
T
Girke
D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D057225:Data Mining; D055808:Drug Discovery / Q000379:methods; D057166:High-Throughput Screening Assays; D015233:Models, Statistical; D011485:Protein Binding; D020543:Proteome; D040901:Proteomics / Q000379:methods; D015203:Reproducibility of Results; D054852:Small Molecule Libraries
D020543:Proteome; D054852:Small Molecule Libraries