DNA-encoded library-enabled discovery of proximity-inducing small molecules.

Small molecules that induce protein-protein associations represent powerful tools to modulate cell circuitry. We sought to develop a platform for the direct discovery of compounds able to induce association of any two preselected proteins, using the E3 ligase von Hippel-Lindau (VHL) and bromodomains as test systems. Leveraging the screening power of DNA-encoded libraries (DELs), we synthesized ~1 million DNA-encoded compounds that possess a VHL-targeting ligand, a variety of connectors and a diversity element generated by split-and-pool combinatorial chemistry. By screening our DEL against bromodomains in the presence and absence of VHL, we could identify VHL-bound molecules that simultaneously bind bromodomains. For highly barcode-enriched library members, ternary complex formation leading to bromodomain degradation was confirmed in cells. Furthermore, a ternary complex crystal structure was obtained for our most enriched library member with BRD4BD1 and a VHL complex. Our work provides a foundation for adapting DEL screening to the discovery of proximity-inducing small molecules.

Diversity-oriented synthesis encoded by deoxyoligonucleotides.

Diversity-oriented synthesis (DOS) is a powerful strategy to prepare molecules with underrepresented features in commercial screening collections, resulting in the elucidation of novel biological mechanisms. In parallel to the development of DOS, DNA-encoded libraries (DELs) have emerged as an effective, efficient screening strategy to identify protein binders. Despite recent advancements in this field, most DEL syntheses are limited by the presence of sensitive DNA-based constructs. Here, we describe the design, synthesis, and validation experiments performed for a 3.7 million-member DEL, generated using diverse skeleton architectures with varying exit vectors and derived from DOS, to achieve structural diversity beyond what is possible by varying appendages alone. We also show screening results for three diverse protein targets. We will make this DEL available to the academic scientific community to increase access to novel structural features and accelerate early-phase drug discovery.

Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells.

Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes.

Predicting compound activity from phenotypic profiles and chemical structures.

Predicting assay results for compounds virtually using chemical structures and phenotypic profiles has the potential to reduce the time and resources of screens for drug discovery. Here, we evaluate the relative strength of three high-throughput data sources-chemical structures, imaging (Cell Painting), and gene-expression profiles (L1000)-to predict compound bioactivity using a historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity for 6-10% of assays, and in combination they predict 21% of assays with high accuracy, which is a 2 to 3 times higher success rate than using a single modality alone. In practice, the accuracy of predictors could be lower and still be useful, increasing the assays that can be predicted from 37% with chemical structures alone up to 64% when combined with phenotypic data. Our study shows that unbiased phenotypic profiling can be leveraged to enhance compound bioactivity prediction to accelerate the early stages of the drug-discovery process.

Mapping the landscape of genetic dependencies in chordoma.

Identifying the spectrum of genes required for cancer cell survival can reveal essential cancer circuitry and therapeutic targets, but such a map remains incomplete for many cancer types. We apply genome-scale CRISPR-Cas9 loss-of-function screens to map the landscape of selectively essential genes in chordoma, a bone cancer with few validated targets. This approach confirms a known chordoma dependency, TBXT (T; brachyury), and identifies a range of additional dependencies, including PTPN11, ADAR, PRKRA, LUC7L2, SRRM2, SLC2A1, SLC7A5, FANCM, and THAP1. CDK6, SOX9, and EGFR, genes previously implicated in chordoma biology, are also recovered. We find genomic and transcriptomic features that predict specific dependencies, including interferon-stimulated gene expression, which correlates with ADAR dependence and is elevated in chordoma. Validating the therapeutic relevance of dependencies, small-molecule inhibitors of SHP2, encoded by PTPN11, have potent preclinical efficacy against chordoma. Our results generate an emerging map of chordoma dependencies to enable biological and therapeutic hypotheses.

Reference compounds for characterizing cellular injury in high-content cellular morphology assays.

Robust, generalizable approaches to identify compounds efficiently with undesirable mechanisms of action in complex cellular assays remain elusive. Such a process would be useful for hit triage during high-throughput screening and, ultimately, predictive toxicology during drug development. Here we generate cell painting and cellular health profiles for 218 prototypical cytotoxic and nuisance compounds in U-2 OS cells in a concentration-response format. A diversity of compounds that cause cellular damage produces bioactive cell painting morphologies, including cytoskeletal poisons, genotoxins, nonspecific electrophiles, and redox-active compounds. Further, we show that lower quality lysine acetyltransferase inhibitors and nonspecific electrophiles can be distinguished from more selective counterparts. We propose that the purposeful inclusion of cytotoxic and nuisance reference compounds such as those profiled in this resource will help with assay optimization and compound prioritization in complex cellular assays like cell painting.

A general approach to identify cell-permeable and synthetic anti-CRISPR small molecules.

The need to control the activity and fidelity of CRISPR-associated nucleases has resulted in a demand for inhibitory anti-CRISPR molecules. The small-molecule inhibitor discovery platforms available at present are not generalizable to multiple nuclease classes, only target the initial step in the catalytic activity and require high concentrations of nuclease, resulting in inhibitors with suboptimal attributes, including poor potency. Here we report a high-throughput discovery pipeline consisting of a fluorescence resonance energy transfer-based assay that is generalizable to contemporary and emerging nucleases, operates at low nuclease concentrations and targets all catalytic steps. We applied this pipeline to identify BRD7586, a cell-permeable small-molecule inhibitor of SpCas9 that is twofold more potent than other inhibitors identified to date. Furthermore, unlike the reported inhibitors, BRD7586 enhanced SpCas9 specificity and its activity was independent of the genomic loci, DNA-repair pathway or mode of nuclease delivery. Overall, these studies describe a general pipeline to identify inhibitors of contemporary and emerging CRISPR-associated nucleases.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function.

DNA-encoded library (DEL) screening and quantitative structure-activity relationship (QSAR) modeling are two techniques used in drug discovery to find novel small molecules that bind a protein target. Applying QSAR modeling to DEL selection data can facilitate the selection of compounds for off-DNA synthesis and evaluation. Such a combined approach has been done recently by training binary classifiers to learn DEL enrichments of aggregated "disynthons" in order to accommodate the sparse and noisy nature of DEL data. However, a binary classification model cannot distinguish between different levels of enrichment, and information is potentially lost during disynthon aggregation. Here, we demonstrate a regression approach to learning DEL enrichments of individual molecules, using a custom negative-log-likelihood loss function that effectively denoises DEL data and introduces opportunities for visualization of learned structure-activity relationships. Our approach explicitly models the Poisson statistics of the sequencing process used in the DEL experimental workflow under a frequentist view. We illustrate this approach on a DEL dataset of 108,528 compounds screened against carbonic anhydrase (CAIX), and a dataset of 5,655,000 compounds screened against soluble epoxide hydrolase (sEH) and SIRT2. Due to the treatment of uncertainty in the data through the negative-log-likelihood loss used during training, the models can ignore low-confidence outliers. While our approach does not demonstrate a benefit for extrapolation to novel structures, we expect our denoising and visualization pipeline to be useful in identifying structure-activity trends and highly enriched pharmacophores in DEL data. Further, this approach to uncertainty-aware regression modeling is applicable to other sparse or noisy datasets where the nature of stochasticity is known or can be modeled; in particular, the Poisson enrichment ratio metric we use can apply to other settings that compare sequencing count data between two experimental conditions.

Phenotypic Screening for Small Molecules that Protect ß-Cells from Glucolipotoxicity.

Type 2 diabetes is marked by progressive ß-cell failure, leading to loss of ß-cell mass. Increased levels of circulating glucose and free fatty acids associated with obesity lead to ß-cell glucolipotoxicity. There are currently no therapeutic options to address this facet of ß-cell loss in obese type 2 diabetes patients. To identify small molecules capable of protecting ß-cells, we performed a high-throughput screen of 20,876 compounds in the rat insulinoma cell line INS-1E in the presence of elevated glucose and palmitate. We found 312 glucolipotoxicity-protective small molecules (1.49% hit rate) capable of restoring INS-1E viability, and we focused on 17 with known biological targets. 16 of the 17 compounds were kinase inhibitors with activity against specific families including but not limited to cyclin-dependent kinases (CDK), PI-3 kinase (PI3K), Janus kinase (JAK), and Rho-associated kinase 2 (ROCK2). 7 of the 16 kinase inhibitors were PI3K inhibitors. Validation studies in dissociated human islets identified 10 of the 17 compounds, namely, KD025, ETP-45658, BMS-536924, AT-9283, PF-03814735, torin-2, AZD5438, CP-640186, ETP-46464, and GSK2126458 that reduced glucolipotoxicity-induced ß-cell death. These 10 compounds decreased markers of glucolipotoxicity including caspase activation, mitochondrial depolarization, and increased calcium flux. Together, these results provide a path forward toward identifying novel treatments to preserve ß-cell viability in the face of glucolipotoxicity.

The Use of Informer Sets in Screening: Perspectives on an Efficient Strategy to Identify New Probes.

Small-molecule discovery typically involves large-scale screening campaigns, spanning multiple compound collections. However, such activities can be cost- or time-prohibitive, especially when using complex assay systems, limiting the number of compounds tested. Further, low hit rates can make the process inefficient. Sparse coverage of chemical structure or biological activity space can lead to limited success in a primary screen and represents a missed opportunity by virtue of selecting the "wrong" compounds to test. Thus, the choice of screening collections becomes of paramount importance. In this perspective, we discuss the utility of generating "informer sets" for small-molecule discovery, and how this strategy can be leveraged to prioritize probe candidates. While many researchers may assume that informer sets are focused on particular targets (e.g., kinases) or processes (e.g., autophagy), efforts to assemble informer sets based on historical bioactivity or successful human exposure (e.g., repurposing collections) have shown promise as well. Another method for generating informer sets is based on chemical structure, particularly when the compounds have unknown activities and targets. We describe our efforts to screen an informer set representing a collection of 100,000 small molecules synthesized through diversity-oriented synthesis (DOS). This process enables researchers to identify activity early and more extensively screen only a few chemical scaffolds, rather than the entire collection. This elegant and economic outcome is a goal of the informer set approach. Here, we aim not only to shed light on this process, but also to promote the use of informer sets more widely in small-molecule discovery projects.

An expanded universe of cancer targets.

The characterization of cancer genomes has provided insight into somatically altered genes across tumors, transformed our understanding of cancer biology, and enabled tailoring of therapeutic strategies. However, the function of most cancer alleles remains mysterious, and many cancer features transcend their genomes. Consequently, tumor genomic characterization does not influence therapy for most patients. Approaches to understand the function and circuitry of cancer genes provide complementary approaches to elucidate both oncogene and non-oncogene dependencies. Emerging work indicates that the diversity of therapeutic targets engendered by non-oncogene dependencies is much larger than the list of recurrently mutated genes. Here we describe a framework for this expanded list of cancer targets, providing novel opportunities for clinical translation.

Targeted brachyury degradation disrupts a highly specific autoregulatory program controlling chordoma cell identity.

Chordomas are rare spinal tumors addicted to expression of the developmental transcription factor brachyury. In chordomas, brachyury is super-enhancer associated and preferentially downregulated by pharmacologic transcriptional CDK inhibition, leading to cell death. To understand the underlying basis of this sensitivity, we dissect the brachyury transcription regulatory network and compare the consequences of brachyury degradation with transcriptional CDK inhibition. Brachyury defines the chordoma super-enhancer landscape and autoregulates through binding its super-enhancer, and its locus forms a transcriptional condensate. Transcriptional CDK inhibition and brachyury degradation disrupt brachyury autoregulation, leading to loss of its transcriptional condensate and transcriptional program. Compared with transcriptional CDK inhibition, which globally downregulates transcription, leading to cell death, brachyury degradation is much more selective, inducing senescence and sensitizing cells to anti-apoptotic inhibition. These data suggest that brachyury downregulation is a core tenet of transcriptional CDK inhibition and motivates developing strategies to target brachyury and its autoregulatory feedback loop.

Plasticity of ether lipids promotes ferroptosis susceptibility and evasion.

Ferroptosis-an iron-dependent, non-apoptotic cell death process-is involved in various degenerative diseases and represents a targetable susceptibility in certain cancers1. The ferroptosis-susceptible cell state can either pre-exist in cells that arise from certain lineages or be acquired during cell-state transitions2-5. However, precisely how susceptibility to ferroptosis is dynamically regulated remains poorly understood. Here we use genome-wide CRISPR-Cas9 suppressor screens to identify the oxidative organelles peroxisomes as critical contributors to ferroptosis sensitivity in human renal and ovarian carcinoma cells. Using lipidomic profiling we show that peroxisomes contribute to ferroptosis by synthesizing polyunsaturated ether phospholipids (PUFA-ePLs), which act as substrates for lipid peroxidation that, in turn, results in the induction of ferroptosis. Carcinoma cells that are initially sensitive to ferroptosis can switch to a ferroptosis-resistant state in vivo in mice, which is associated with extensive downregulation of PUFA-ePLs. We further find that the pro-ferroptotic role of PUFA-ePLs can be extended beyond neoplastic cells to other cell types, including neurons and cardiomyocytes. Together, our work reveals roles for the peroxisome-ether-phospholipid axis in driving susceptibility to and evasion from ferroptosis, highlights PUFA-ePL as a distinct functional lipid class that is dynamically regulated during cell-state transitions, and suggests multiple regulatory nodes for therapeutic interventions in diseases that involve ferroptosis.

Selective covalent targeting of GPX4 using masked nitrile-oxide electrophiles.

We recently described glutathione peroxidase 4 (GPX4) as a promising target for killing therapy-resistant cancer cells via ferroptosis. The onset of therapy resistance by multiple types of treatment results in a stable cell state marked by high levels of polyunsaturated lipids and an acquired dependency on GPX4. Unfortunately, all existing inhibitors of GPX4 act covalently via a reactive alkyl chloride moiety that confers poor selectivity and pharmacokinetic properties. Here, we report our discovery that masked nitrile-oxide electrophiles, which have not been explored previously as covalent cellular probes, undergo remarkable chemical transformations in cells and provide an effective strategy for selective targeting of GPX4. The new GPX4-inhibiting compounds we describe exhibit unexpected proteome-wide selectivity and, in some instances, vastly improved physiochemical and pharmacokinetic properties compared to existing chloroacetamide-based GPX4 inhibitors. These features make them superior tool compounds for biological interrogation of ferroptosis and constitute starting points for development of improved inhibitors of GPX4.

Small-Molecule and CRISPR Screening Converge to Reveal Receptor Tyrosine Kinase Dependencies in Pediatric Rhabdoid Tumors.

Cancer is often seen as a disease of mutations and chromosomal abnormalities. However, some cancers, including pediatric rhabdoid tumors (RTs), lack recurrent alterations targetable by current drugs and need alternative, informed therapeutic options. To nominate potential targets, we performed a high-throughput small-molecule screen complemented by a genome-scale CRISPR-Cas9 gene-knockout screen in a large number of RT and control cell lines. These approaches converged to reveal several receptor tyrosine kinases (RTKs) as therapeutic targets, with RTK inhibition effective in suppressing RT cell growth in vitro and against a xenograft model in vivo. RT cell lines highly express and activate (phosphorylate) different RTKs, creating dependency without mutation or amplification. Downstream of RTK signaling, we identified PTPN11, encoding the pro-growth signaling protein SHP2, as a shared dependency across all RT cell lines. This study demonstrates that large-scale perturbational screening can uncover vulnerabilities in cancers with "quiet" genomes.

DNA Barcoding a Complete Matrix of Stereoisomeric Small Molecules.

It is challenging to incorporate stereochemical diversity and topographic complexity into DNA-encoded libraries (DELs) because DEL syntheses cannot fully exploit the capabilities of modern synthetic organic chemistry. Here, we describe the design, construction, and validation of DOS-DEL-1, a library of 107 616 DNA-barcoded chiral 2,3-disubsituted azetidines and pyrrolidines. We used stereospecific C-H arylation chemistry to furnish complex scaffolds primed for DEL synthesis, and we developed an improved on-DNA Suzuki reaction to maximize library quality. We then studied both the structural diversity of the library and the physicochemical properties of individual compounds using Tanimoto multifusion similarity analysis, among other techniques. These analyses revealed not only that most DOS-DEL-1 members have "drug-like" properties, but also that the library more closely resembles compound collections derived from diversity synthesis than those from other sources (e.g., commercial vendors). Finally, we performed validation screens against horseradish peroxidase and carbonic anhydrase IX, and we developed a novel, Poisson-based statistical framework to analyze the results. A set of assay positives were successfully translated into potent carbonic anhydrase inhibitors (IC50 = 20.1-68.7 nM), which confirmed the success of the synthesis and screening procedures. These results establish a strategy to synthesize DELs with scaffold-based stereochemical diversity and complexity that does not require the development of novel DNA-compatible chemistry.

A High-Throughput Platform to Identify Small-Molecule Inhibitors of CRISPR-Cas9.

The precise control of CRISPR-Cas9 activity is required for a number of genome engineering technologies. Here, we report a generalizable platform that provided the first synthetic small-molecule inhibitors of Streptococcus pyogenes Cas9 (SpCas9) that weigh <500 Da and are cell permeable, reversible, and stable under physiological conditions. We developed a suite of high-throughput assays for SpCas9 functions, including a primary screening assay for SpCas9 binding to the protospacer adjacent motif, and used these assays to screen a structurally diverse collection of natural-product-like small molecules to ultimately identify compounds that disrupt the SpCas9-DNA interaction. Using these synthetic anti-CRISPR small molecules, we demonstrated dose and temporal control of SpCas9 and catalytically impaired SpCas9 technologies, including transcription activation, and identified a pharmacophore for SpCas9 inhibition using structure-activity relationships. These studies establish a platform for rapidly identifying synthetic, miniature, cell-permeable, and reversible inhibitors against both SpCas9 and next-generation CRISPR-associated nucleases.