Biology subjects

Skepner, A.

Publications and source records attributed to Skepner, A..

9 recordsLinked to original sources

bioRxiv2026

Discovery of GluA3 preferring AMPA receptor positive allosteric modulator BRD3290

Schizophrenia is a debilitating neuropsychiatric disease that lacks effective treatments for many symptom domains including negative, cognitive and sleep disturbances. Lack of clear disease etiology has hampered the development of new, effective treatments for the unmet needs of people with schizophrenia. Large scale human genetics have identified rare loss of function mutations that substantially increase risk of developing schizophrenia, including in GRIA3, the gene that encodes the GluA3 receptor subunit of the AMPA receptor (AMPAR). Several drug discovery programs have been aimed at developing AMPAR positive allosteric modulators (PAMs) as a novel treatment for schizophrenia. Despite intense drug discovery efforts, there are no FDA approved AMPAR PAMs. We therefore hypothesized that selectively targeting GluA3, the AMPAR subunit implicated by human genetics, could yield a safer and more effective AMPAR PAM for the potential treatment of schizophrenia. Using a combination of medicinal chemistry, in vitro, and in vivo studies, we discovered BRD3290, a GluA3-preferring AMPAR PAM with reasonable potency in heterologous cells, as well as favorable tolerability and brain exposure. Peripheral administration of BRD3290 engaged an established AMPAR PAM target engagement biomarker but did not improve performance of wildtype mice in the novel object recognition task (NOR), in contrast to the nonselective AMPAR PAM PF-4778574, which improved mouse NOR. These findings suggest that the GluA3 selectivity profile of BRD3290 was insufficient to enhance cognitive function in this mouse NOR paradigm. This work highlights the challenges of AMPAR subtype-selective modulation and provides molecular insights into the ability to develop subtype-selective AMPAR PAMs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/740780v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1577f62org.highwire.dtl.DTLVardef@16c1750org.highwire.dtl.DTLVardef@16eaef3org.highwire.dtl.DTLVardef@19fdb6f_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2026

Explainable Physicochemical Determinants of Protein Ligand Binding via Non-Covalent Interactions

Protein-ligand binding governs enzymatic catalysis, metabolic homeostasis, and therapeutic modulation. Thus, the accurate prediction of these interactions underpins modern rational drug discovery. However, existing deep-learning frameworks largely operate as black-box predictors that fail to resolve the individual residues mediating binding or decode the fundamental non-covalent forces that drive molecular recognition. To address these limitations, we present ExplainBind, an interaction-aware framework that predicts binding likelihood, localizes specific binding residues at single-amino-acid resolution rather than coarse pocket-level regions, and decodes the underlying non-covalent interaction patterns, all zero-shot, without requiring prior three-dimensional structural inputs. To support residue- and interaction-level training and evaluation, we construct InteractBind, a protein-ligand benchmark with residue-atom interaction maps. Simultaneously, benchmarking experiments demonstrate that ExplainBind consistently outperforms state-of-the-art baselines across diverse protein and ligand spaces, maintaining high precision when generalized to entirely novel sequences and chemical scaffolds. When applied to two unseen therapeutic targets, ExplainBind successfully ranks potent angiotensin-converting enzyme (ACE) inhibitors and clarifies differences in their potency via affinity-stratified interaction landscapes. Furthermore, we demonstrate the prospective utility of ExplainBind by discovering novel inhibitors and activators of L-2-hydroxyglutarate dehydrogenase (L2HGDH) through wet-lab validation, with mechanistically distinct interaction profiles providing a clear molecular rationale for their divergent functional outcomes. Collectively, these results establish ExplainBind as a powerful, generalizable tool for mechanistically informed, interpretable drug discovery.

molecular biology↗

bioRxiv2025

Phenotypic screening converges on CDK9 inhibition as a therapeutic strategy in translocation renal cell carcinoma

Translocation renal cell carcinoma (tRCC) is an aggressive kidney cancer driven by gene fusions of the TFE3 transcription factor. TFE3 is essential in tRCC but dispensable in normal cells, presenting an attractive but pharmacologically challenging therapeutic target. We show that the basic helix-loop-helix (bHLH) domain of TFE3 is crucial for chromatin binding and transcriptional function. Via a phenotypic screen of 25,000 compounds, we identified molecules that either displace or retain chromatin-bound TFE3. BRD6866, a compound trapping TFE3 on chromatin, emerged as a pan-CDK inhibitor. Mechanistically, its inhibition of CDK9 - a key regulator of transcriptional elongation - was linked to impaired TFE3 fusion activity. These effects were recapitulated by the CDK9-selective inhibitor enitociclib, which downregulated TFE3 targets and suppressed tRCC cell growth. Our findings nominate CDK9 inhibition as a therapeutic strategy in tRCC and demonstrate the utility of mechanism-informed phenotypic screening for challenging targets.

cancer biology↗

bioRxiv2024

Identification of Small Molecule Inhibitors of PPM1D Using a Novel Drug Discovery Platform

Protein phosphatase, Mg2+/Mn2+ dependent 1D (PPM1D), is a serine/threonine phosphatase that is recurrently activated in cancer, regulates the DNA damage response (DDR), and suppresses the activation of p53. Consistent with its oncogenic properties, genetic loss or pharmacologic inhibition of PPM1D impairs tumor growth and sensitizes cancer cells to cytotoxic therapies in a wide range of preclinical models. Given the therapeutic potential of targeting PPM1D specifically and the DDR and p53 pathway more generally, we sought to deepen our biological understanding of PPM1D as a drug target and determine how PPM1D inhibition differs from other therapeutic approaches to activate the DDR. We performed a high throughput screen to identify new allosteric inhibitors of PPM1D, then generated and optimized a suite of enzymatic, cell-based, and in vivo pharmacokinetic and pharmacodynamic assays to drive medicinal chemistry efforts and to further interrogate the biology of PPM1D. Importantly, this drug discovery platform can be readily adapted to broadly study the DDR and p53. We identified compounds distinct from previously reported allosteric inhibitors and showed in vivo on-target activity. Our data suggest that the biological effects of inhibiting PPM1D are distinct from inhibitors of the MDM2-p53 interaction and standard cytotoxic chemotherapies. These differences also highlight the potential therapeutic contexts in which targeting PPM1D would be most valuable. Therefore, our studies have identified a series of new PPM1D inhibitors, generated a suite of in vitro and in vivo assays that can be broadly used to interrogate the DDR, and provided important new insights into PPM1D as a drug target.

cancer biology↗

bioRxiv2023

nELISA: A high-throughput, high-plex platform enables quantitative profiling of the secretome

We present the nELISA, a high-throughput, high-fidelity, and high-plex protein profiling platform. DNA oligonucleotides are used to pre-assemble antibody pairs on spectrally encoded microparticles and perform displacement-mediated detection. Spatial separation between non-cognate antibodies prevents the rise of reagent-driven cross-reactivity, while read-out is performed cost-efficiently and at high-throughput using flow cytometry. nELISA can measure both protein concentration and their post-translational modifications. We assembled an inflammatory panel of 191 targets that were multiplexed without cross-reactivity nor impact on performance vs 1-plex signals, with sensitivities as low as 0.1 pg/mL and measurements spanning 7 orders of magnitude. We then performed a large-scale inflammatory-secretome perturbation screen of peripheral blood mononuclear cells (PBMCs), with cytokines as both perturbagens and read-outs, measuring 7,392 samples and generating [~]1.4M protein data points in under a week; a significant advance in throughput compared to other highly multiplexed immunoassays. We uncovered 447 significant cytokine responses, including multiple putatively novel ones, that were conserved across donors and stimulation conditions. We validate nELISA for phenotypic screening, where its capacity to faithfully report hundreds of proteins make it a powerful tool across multiple stages of drug discovery.

bioengineering↗

bioRxiv2022

Optimizing the Cell Painting assay for image-based profiling

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations based on their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells, and predicting assay outcomes using machine learning, among many others. Here we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum, and mitochondria. The original protocol was updated in 2016 based on several years experience running it at two sites, after optimizing it by visual stain quality. Here we describe the work of the Joint Undertaking for Morphological Profiling (JUMP) Cell Painting Consortium, aiming to improve upon the assay via quantitative optimization, based on the measured ability of the assay to detect morphological phenotypes and group similar perturbations together. We find that the assay gives very robust outputs despite a variety of changes to the protocol and that two vendors dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for a typically sized batch of 20 or fewer plates; feature extraction and data analysis take an additional 1-2 weeks. Key references using this protocolVirtual screening for small-molecule pathway regulators by image-profile matching (https://doi.org/10.1016/j.cels.2022.08.003) - recent work examining the ability to use collected Cell Painting profiles to screen for regulators of a number of diverse biological pathways. JUMP Cell Painting dataset: images and profiles from two billion cells perturbed by 140,000 chemical and genetic perturbations (DOI) - the description of the main JUMP master public data set, using this protocol in the production of >200 TB of image data and >200 TB of measured profiles. Key data used in this protocolCell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes (https://doi.org/10.1038/nprot.2016.105) - this paper provides the first step-by-step Cell Painting protocol ever released.

bioinformatics↗

bioRxiv2022

Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations

Identifying genetic and chemical perturbations with similar impacts on cell morphology can reveal compounds mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here, we create such a Resource dataset, CPJUMP1, where each perturbed gene is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same gene, and we find that identifying matches between chemical perturbations and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling, such as functional genomics and drug discovery.

bioinformatics↗

bioRxiv2021

Discovery of a first-in-class inhibitor of the PRMT5-substrate adaptor interaction

PRMT5 and its substrate adaptor proteins (SAPs), pICln and Riok1, are synthetic lethal dependencies in MTAP-deleted cancer cells. SAPs share a conserved PRMT5 binding motif (PBM) which mediates binding to a surface of PRMT5 distal to the catalytic site. This interaction is required for methylation of several PRMT5 substrates, including histone and spliceosome complexes. We screened for small molecule inhibitors of the PRMT5-PBM interaction and validated a compound series which binds to the PRMT5-PBM interface and directly inhibits binding of SAPs. Mode of action and structure determination studies revealed that these compounds form a covalent bond between a halogenated pyridazinone group and cysteine 278 of PRMT5. Optimization of the starting hit produced a lead compound, BRD0639, which engages the target in cells, disrupts the PRMT5-RIOK1 complex, and reduces substrate methylation. BRD0639 is a first-in-class PBM-competitive small molecule that can support studies of PBM-dependent PRMT5 activities and the development of novel PRMT5 inhibitors that selectively target these functions.

cancer biology↗

bioRxiv2020

Molecular basis for substrate recruitment to the PRMT5 methylosome

PRMT5 is an arginine methyltransferase and a therapeutic target in MTAP null cancers. PRMT5 utilizes adaptor proteins for substrate recruitment through a previously undefined mechanism. Here, we identify an evolutionarily conserved peptide sequence shared among the three known substrate adaptors (pICln/CLNS1A, RIOK1 and COPR5) and show it is necessary and sufficient for interaction with PRMT5. We structurally resolve the interface with PRMT5 and show via genetic perturbation that it is required for methylation of adaptor-recruited substrates including the spliceosome, histones, and ribosome assembly complexes. Genetic disruption of the PRMT5-substrate adaptor interface leads to a hypomorphic decrease in growth of MTAP null tumor cells and is thus a novel site for development of therapeutic inhibitors of PRMT5.

cancer biology↗