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Scholarly Works (7 results)

  • Thesis
  • Peer Reviewed
UC San Francisco Electronic Theses and Dissertations (2022)

There is always a new therapy to discovered, either by overcoming side effects or other liabilities in current drugs, or through exploring new technologies, proteins, or diseases. Focusing on small molecules, the initial stages of the drug discovery process require the identification of molecules modulating a protein of interest. In Chapter 1, one approach available for discovery is described – molecular docking to discover new small molecules of interest from virtual chemical databases. This thesis applies the approach to overcome side effects of current opioid pain drugs, to identify new antivirals for SARS-CoV-2, and to probe larger applications of the growing chemical space.

Chapter 2 describes how the computational approach identifies new nonopioid pain therapeutics acting through the α2A-adrenergic receptor (α2AAR). Here, we show the translational impact of computational predictions as many are efficacious in mice. We argue a case for further drug discovery focused on this protein, because not only were the opioid side effects avoided, but also a characteristic α2AAR-caused side effect is not seen with new compounds.

Chapter 3 demonstrates application of the computational technique to an understudied viral protein, the SARS-CoV-2 nonstructural protein 14 (nsp14), using different subsets of the chemical databases for targeted results. Here, ‘traditional’ noncovalent ‘lead-like’ subsets, smaller ‘fragment’ molecules, and covalent molecules are utilized and successfully identify nsp14 inhibitors. We show in a pandemic-driven project that multiple techniques are needed to progress towards antivirals inhibiting an understudied protein.

Chapter 4 continues the themes of Chapter 3, but with the SARS-CoV-2 main protease (MPro). Here, we use a similar approach with different types of chemistry found in the databases and identify numerous inhibitors. Structural studies demonstrate the computationally predicted and experimental ligand geometries are in high agreement. Finally, we begin to probe the antiviral efficacy of these new compounds and contribute the knowledge to the field for further antiviral development.

Chapter 5 focuses on a new application of the virtual screening of chemical databases. Here, we ask if this approach can be applied prospectively to discover molecules with polypharmacology – having designed activities on multiple proteins of interest. We simulate the effect of the growing chemical databases to understand their effect on docking for polypharmacology. This chapter also blends into the most immediate future directions of the thesis work and is described in context of pain therapeutic discovery.

    Cover page: Molecular docking in epidemics and pandemics
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2021)
    Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.
      Cover page: A practical guide to large-scale docking
      • Article
      • Peer Reviewed
      UC San Francisco Previously Published Works (2023)
      The lipid prostaglandin E2 (PGE2) mediates inflammatory pain by activating G protein-coupled receptors, including the prostaglandin E2 receptor 4 (EP4R). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce nociception by inhibiting prostaglandin synthesis, however, the disruption of upstream prostanoid biosynthesis can lead to pleiotropic effects including gastrointestinal bleeding and cardiac complications. In contrast, by acting downstream, EP4R antagonists may act specifically as anti-inflammatory agents and, to date, no selective EP4R antagonists have been approved for human use. In this work, seeking to diversify EP4R antagonist scaffolds, we computationally dock over 400 million compounds against an EP4R crystal structure and experimentally validate 71 highly ranked, de novo synthesized molecules. Further, we show how structure-based optimization of initial docking hits identifies a potent and selective antagonist with 16 nanomolar potency. Finally, we demonstrate favorable pharmacokinetics for the discovered compound as well as anti-allodynic and anti-inflammatory activity in several preclinical pain models in mice.
        Cover page: Docking for EP4R antagonists active against inflammatory pain.
        • Article
        • Peer Reviewed
        UC San Francisco Previously Published Works (2022)
        Because nonopioid analgesics are much sought after, we computationally docked more than 301 million virtual molecules against a validated pain target, the α2A-adrenergic receptor (α2AAR), seeking new α2AAR agonists chemotypes that lack the sedation conferred by known α2AAR drugs, such as dexmedetomidine. We identified 17 ligands with potencies as low as 12 nanomolar, many with partial agonism and preferential Gi and Go signaling. Experimental structures of α2AAR complexed with two of these agonists confirmed the docking predictions and templated further optimization. Several compounds, including the initial docking hit '9087 [mean effective concentration (EC50) of 52 nanomolar] and two analogs, '7075 and PS75 (EC50 4.1 and 4.8 nanomolar), exerted on-target analgesic activity in multiple in vivo pain models without sedation. These newly discovered agonists are interesting as therapeutic leads that lack the liabilities of opioids and the sedation of dexmedetomidine.
          • Article
          • Peer Reviewed
          UC San Francisco Previously Published Works (2023)
          An under-explored target for SARS-CoV-2 is the S-adenosyl methionine (SAM)-dependent methyltransferase Nsp14, which methylates the N7-guanosine of viral RNA at the 5'-end, allowing the virus to evade host immune response. We sought new Nsp14 inhibitors with three large library docking strategies. First, up to 1.1 billion lead-like molecules were docked against the enzyme's SAM site, leading to three inhibitors with IC50 values from 6 to 50 μM. Second, docking a library of 16 million fragments revealed 9 new inhibitors with IC50 values from 12 to 341 μM. Third, docking a library of 25 million electrophiles to covalently modify Cys387 revealed 7 inhibitors with IC50 values from 3.5 to 39 μM. Overall, 32 inhibitors encompassing 11 chemotypes had IC50 values < 50 μM and 5 inhibitors in 4 chemotypes had IC50 values < 10 μM. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing starting points for future optimization.
            Cover page: Structure-Based Discovery of Inhibitors of the SARS-CoV‑2 Nsp14 N7-Methyltransferase
            • Article
            • Peer Reviewed
            UC San Francisco Previously Published Works (2023)
            Antiviral therapeutics to treat SARS-CoV-2 are needed to diminish the morbidity of the ongoing COVID-19 pandemic. A well-precedented drug target is the main viral protease (MPro ), which is targeted by an approved drug and by several investigational drugs. Emerging viral resistance has made new inhibitor chemotypes more pressing. Adopting a structure-based approach, we docked 1.2 billion non-covalent lead-like molecules and a new library of 6.5 million electrophiles against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC50 of 29 and 20 μM, respectively. Several series were optimized, resulting in low micromolar inhibitors. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. While the new chemotypes may aid further optimization of MPro inhibitors for SARS-CoV-2, the modest success rate also reveals weaknesses in our approach for challenging targets like MPro versus other targets where it has been more successful, and versus other structure-based techniques against MPro itself.
              Cover page: Large library docking for novel SARS‐CoV‐2 main protease non‐covalent and covalent inhibitors
              • Article
              • Peer Reviewed
              UC San Francisco Previously Published Works (2020)
              Protein conformations are shaped by cellular environments, but how environmental changes alter the conformational landscapes of specific proteins in vivo remains largely uncharacterized, in part due to the challenge of probing protein structures in living cells. Here, we use deep mutational scanning to investigate how a toxic conformation of α-synuclein, a dynamic protein linked to Parkinson's disease, responds to perturbations of cellular proteostasis. In the context of a course for graduate students in the UCSF Integrative Program in Quantitative Biology, we screened a comprehensive library of α-synuclein missense mutants in yeast cells treated with a variety of small molecules that perturb cellular processes linked to α-synuclein biology and pathobiology. We found that the conformation of α-synuclein previously shown to drive yeast toxicity-an extended, membrane-bound helix-is largely unaffected by these chemical perturbations, underscoring the importance of this conformational state as a driver of cellular toxicity. On the other hand, the chemical perturbations have a significant effect on the ability of mutations to suppress α-synuclein toxicity. Moreover, we find that sequence determinants of α-synuclein toxicity are well described by a simple structural model of the membrane-bound helix. This model predicts that α-synuclein penetrates the membrane to constant depth across its length but that membrane affinity decreases toward the C terminus, which is consistent with orthogonal biophysical measurements. Finally, we discuss how parallelized chemical genetics experiments can provide a robust framework for inquiry-based graduate coursework.
                Cover page: Robust Sequence Determinants of α‑Synuclein Toxicity in Yeast Implicate Membrane Binding
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