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Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UC Riverside Electronic Theses and Dissertations (2016)

In this dissertation, I present several strategies to leverage experimental data towards a quantitative understanding of small molecule bioactivity that can inform the discovery of drug candidates. I review two cheminformatics tools I co-developed, present a comprehensive cross-target analysis of large public small molecule bioactivity data, and present a mathematical model of leptin transport across the mammalian blood-brain barrier.

First I present ChemMine Tools, a web service which provides both programmable and interactive online interfaces to a diverse set of analysis tools useful for analyzing small molecule structural data. ChemMine Tools allows users to import a set of small molecule structures, compute pairwise compound similarities, search for similar compounds, cluster compounds by structure or physical properties, and compute physicochemical properties.

The second software tool is bioassayR, a software package for large scale cross-target analysis of small molecule bioactivity profiles. bioassayR systematically analyzes data from thousands of screening experiments to identify target selective drug candidates and druggable protein targets. By simultaneously leveraging data from both custom small molecule screening efforts and public databases, bioassayR helps identify regions of the genome and proteome accessible to small molecule probes, elucidate novel mechanisms of action for bioactive molecules, and predict off-target effects which currently lead to a high attrition rate in drug discovery efforts.

The systematic cross-target analysis of public bioactivity data uses the bioassayR tool to analyze data from PubChem BioAssay. This groups small molecules into three groups based on an increasing number of active targets in these data- highly selective, family selective, and promiscuous binding, and find that FDA approved drugs are strongly represented among the family selective group. I also show that the compound-target space can be organized into biclusters, where shared activity tends to occur across protein targets sharing common Molecular Function Gene Ontology (MF GO) terms.

The leptin transport kinetic model extends current mathematical models of receptor endocytosis to transcytosis, and behaves similar to the experimentally observed dynamics of this system. A computational model is provided which allows for in-silico perturbation, to predict the potential effects of pathological states, or therapeutic small molecules.

  • 7 supplemental ZIPs
  • 1 supplemental file
Cover page: Small Molecule Interaction With Biological TargetsCreative Commons 'BY' version 4.0 license
  • Article
  • Peer Reviewed
UC Riverside Previously Published Works (2016)
Despite a large and rapidly growing body of small molecule bioactivity screens available in the public domain, systematic leverage of the data to assess target druggability and compound selectivity has been confounded by a lack of suitable cross-target analysis software. We have developed bioassayR, a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data. bioassayR is implemented as an open-source R/Bioconductor package available from https://bioconductor.org/packages/bioassayR/ .
    Cover page: bioassayR: Cross-Target Analysis of Small Molecule Bioactivity
    • Article
    • Peer Reviewed
    UC Riverside Previously Published Works (2017)
    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.
      Cover page: Large-scale bioactivity analysis of the small-molecule assayed proteome
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2020)
      Polyketide synthase (PKS) engineering is an attractive method to generate new molecules such as commodity, fine and specialty chemicals. A significant challenge is re-engineering a partially reductive PKS module to produce a saturated β-carbon through a reductive loop (RL) exchange. In this work, we sought to establish that chemoinformatics, a field traditionally used in drug discovery, offers a viable strategy for RL exchanges. We first introduced a set of donor RLs of diverse genetic origin and chemical substrates  into the first extension module of the lipomycin PKS (LipPKS1). Product titers of these engineered unimodular PKSs correlated with chemical structure similarity between the substrate of the donor RLs and recipient LipPKS1, reaching a titer of 165 mg/L of short-chain fatty acids produced by the host Streptomyces albus J1074. Expanding this method to larger intermediates that require bimodular communication, we introduced RLs of divergent chemosimilarity into LipPKS2 and determined triketide lactone production. Collectively, we observed a statistically significant correlation between atom pair chemosimilarity and production, establishing a new chemoinformatic method that may aid in the engineering of PKSs to produce desired, unnatural products.
        Cover page: Chemoinformatic-Guided Engineering of Polyketide Synthases
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