Biology subjects

McKenna, J.

Publications and source records attributed to McKenna, J..

12 recordsLinked to original sources

bioRxiv2026

An Optimized RNF126-Targeting Covalent Handle for Molecular Glue Degraders

Molecular glue degraders represent a powerful modality for targeting proteins that are refractory to traditional inhibition. However, rational design principles for molecular glue degraders remain poorly defined. Previously, we reported a chemistry-centric strategy to identify covalent degradative handles that, when appended to established ligands, convert non-degradative inhibitors into molecular glue degraders by engaging permissive E3 ligases. This effort identified a fumarate-based electrophilic handle that covalently modified the E3 ligase RNF126, enabling degradation of multiple protein targets when transplanted across diverse ligands. Despite its conceptual impact, the high intrinsic reactivity and cytotoxicity of the fumarate handle limited its translational utility. Here, we report the development of an optimized and metabolically stabilized RNF126-targeting covalent handle incorporating a trans-cyclobutane linker that exhibits reduced glutathione reactivity and diminished cytotoxicity while retaining robust degradative activity. When appended to the BET bromodomain inhibitor JQ1, this optimized handle yielded a potent and selective BRD4 degrader whose activity was dependent on RNF126. Importantly, transplantation of this handle onto a previously non-inhibitory ligand targeting the androgen receptor (AR) and its truncation variant, AR-V7, enabled selective degradation of both AR and AR-V7 in androgen-independent prostate cancer cells, thereby robustly inhibiting AR transcriptional activity beyond the established AR antagonist enzalutamide. Collectively, these findings demonstrate an optimized RNF126-based covalent handle for the rational development of molecular glue degraders against transcriptional regulators, including undruggable variants such as AR-V7.

biochemistry↗

bioRxiv2025

Uncertainty-Aware Deep Learning for Multi-Metric and Dose-Specific Prediction of Drug Synergy

Accurately predicting drug synergy is critical to accelerate the development of combination therapies for cancer and other complex diseases. Yet, the vast combinatorial drug and dose space poses a substantial challenge, even for modern deep learning approaches. Existing approaches often lack generalisability, collapse rich dose-response surfaces into single dose-averaged synergy scores, and fail to quantify predictive uncertainty. Here, we introduce AlgoraeOS, a biologically informed, attention-aware deep neural network designed to address these challenges. Trained on the largest harmonised dataset of experimentally tested drug combinations, AlgoraeOS simultaneously predicts multiple synergy metrics, while preserving their empirical correlations and accurately estimating both aleatoric and epistemic uncertainty. The model achieves state-of-the-art performance and strong out-of-distribution generalisability across diverse tissues and drug mechanisms, including rigorous zero- and few-shot evaluations. Notably, AlgoraeOS predicts the entire dose-response surface, providing dose-specific inhibition profiles with high precision and scalability to multi-million-point datasets. Prospective in vitro validation of dose-specific inhibition was performed using an anchor compound entirely absent from the training corpus, tested in combination with 24 mechanistically diverse partner drugs across three molecularly distinct cancer cell lines, yielding 2,592 dose-combination measurements. This evaluation demonstrated consistent rank-order fidelity (Spearmans {rho} = 0.51-0.59, p < 0.001) and strong directional agreement (Kendalls {tau} = 0.863), confirming reliable prediction under out-of-distribution conditions. Model-derived uncertainty estimates further stratified predictions by expected reliability, with lower-uncertainty predictions showing higher concordance with experimental outcomes. By integrating uncertainty-aware, multi-metric, and dose-resolved prediction into a single unified framework, AlgoraeOS offers a powerful solution for drug-combination discovery and establishes a new standard for model development and validation in the field.

bioinformatics↗

bioRxiv2025

Targeted Transcriptional Repression by Induced Proximity

Most cancer-driving proteins remain "undruggable" due to the absence of ligandable pockets and their reliance on intrinsically disordered or protein-DNA/protein-protein interactions. Transcription factors, which orchestrate oncogenic gene expression programs, are particularly challenging: they turn over rapidly, evade durable pharmacological inhibition, and resist even emerging targeted protein degradation strategies. Here, we describe a new induced-proximity therapeutic modality, Transcriptional Regulation via Active Control of Epigenetic Reprogramming (TRACER), that enforces locus-specific transcriptional silencing by recruiting endogenous corepressor complexes to transcription factor binding sites. We developed small-molecule TRACERs that tether methyl-CpG binding domain protein 2 (MBD2) and the Nucleosome Remodeling and Deacetylase (NuRD) complex to transcription factor-directed ligands. An estrogen receptor (ER) TRACER potently suppressed ER transcriptional activity in breast cancer cells, downregulated canonical ER target genes, and required MBD2 and histone deacetylase (HDAC1/2) for activity, confirming on-target epigenetic repression. Extending this approach to prostate cancer, an androgen receptor (AR) TRACER transcriptionally repressed both full-length AR and the drug-resistant truncation variant AR-V7, achieving >90% inhibition of AR transcriptional activity in androgen-independent prostate cancer cells with locus-specific gene repression. We further show that TRACERs can be modularly reprogrammed to recruit alternative repressors, including PRC2. Collectively, these findings establish TRACERs as a generalizable modality to pharmacologically silence undruggable transcription factors through targeted epigenetic reprogramming, offering a powerful new strategy for treating cancers refractory to existing therapies.

biochemistry↗

bioRxiv2024

Targeted mutagenesis of specific genomic DNA sequences in animals for the in vivo generation of variant libraries

Understanding how the number, placement and affinity of transcription factor binding sites dictates gene regulatory programs remains a major unsolved challenge in biology, particularly in the context of multicellular organisms. To uncover these rules, it is first necessary to find the binding sites within a regulatory region with high precision, and then to systematically modulate this binding site arrangement while simultaneously measuring the effect of this modulation on output gene expression. Massively parallel reporter assays (MPRAs), where the gene expression stemming from 10,000s of in vitro-generated regulatory sequences is measured, have made this feat possible in high-throughput in single cells in culture. However, because of lack of technologies to incorporate DNA libraries, MPRAs are limited in whole organisms. To enable MPRAs in multicellular organisms, we generated tools to create a high degree of mutagenesis in specific genomic loci in vivo using base editing. Targeting GFP integrated in genome of Drosophila cell culture and whole animals as a case study, we show that the base editor AIDevoCDA1 stemming from sea lamprey fused to nCas9 is highly mutagenic. Surprisingly, longer gRNAs increase mutation efficiency and expand the mutating window, which can allow the introduction of mutations in previously untargetable sequences. Finally, we demonstrate arrays of >20 gRNAs that can efficiently introduce mutations along a 200bp sequence, making it a promising tool to test enhancer function in vivo in a high throughput manner.

genetics↗

bioRxiv2022

Detecting population continuity and ghost admixture among ancient genomes

Ancient DNA (aDNA) can prove a valuable resource when investigating the evolutionary relationships between ancient and modern populations. Performing demographic inference using datasets that include aDNA samples however, requires statistical methods that explicitly account for the differences in drift expected among a temporally distributed sample. Such drift due to temporal structure can be challenging to discriminate from admixture from an unsampled, or "ghost", population, which can give rise to very similar summary statistics and confound methods commonly used in population genetics. Sequence data from ancient individuals also have unique characteristics, including short fragments, increased sequencing-error rates, and often limited genome-coverage that poses further challenges. Here we present a novel and conceptually simple approach for assessing questions of population continuity among a temporally distributed sample. We note that conditional on heterozygote sites in an individual genome at a particular point in time, the mean proportion of derived variants at those sites in other individuals has different expectations forwards in time and backwards in time. The difference in these processes enables us to construct a statistic that can detect population continuity in a temporal sample of genomes. We show that the statistic is sensitive to historical admixture events from unsampled populations. Simulations are used to evaluate the power of this approach. We investigate a set of ancient genomes from Early Neolithic Scandinavia to assess levels of population continuity to an earlier Mesolithic individual.

genetics↗

bioRxiv2022

Protein Structure Prediction with Expectation Reflection

Sequence covariation in multiple sequence alignments of homologous proteins has been used extensively to obtain insights into protein structure. However, global statistical inference is required in order to ascertain direct relationships between amino acid positions in these sequences that are not simply secondary correlations induced by interactions with a third residue. Methods for statistical inference of such covariation have been developed to exploit the growing availability of sequence data. These hints about the folded protein structure provide critical a priori information for more detailed 3D predictions by neural networks. We present a novel method for protein structure inference using an iterative parameter-free model estimator which uses the formalism of statistical physics. With no tunable learning rate, our method scales to large system sizes while providing improved performance in the regime of small sample sizes. We apply this method to 40974 PDB structures and compare its performance to that of other methods. Our method outperforms existing methods for 76% of analysed proteins.

molecular biology↗

bioRxiv2021

Collaboration Between Host and Viral Factors Shape SARS-CoV-2 Evolution

SARS-CoV-2 continues to evolve, resulting in several variants of concern with novel properties. The factors driving SARS-CoV-2 fitness and evolution in the human respiratory tract remain poorly defined. Here, we provide evidence that both viral and host factors co-operate to shape SARS-CoV-2 genotypic and phenotypic change. Through viral whole-genome sequencing, we explored the evolution of two clinical isolates of SARS-CoV-2 during passage in unmodified Vero-derived cell lines and in parallel, in well-differentiated primary nasal epithelial cell (WD-PNEC) cultures. We identify a consistent, rich genetic diversity arising in vitro, variants of which could rapidly rise to near-fixation with 2 passages. Within isolates, SARS-CoV-2 evolution was dependent on host cells, with Vero-derived cells facilitating more profound genetic changes. However, most mutations were not shared between strains. Furthermore, comparison of both Vero-grown isolates on WD-PNECs disclosed marked growth attenuation mapping to the loss of the polybasic cleavage site (PBCS) in Spike while the strain with mutations in NSP12 (T293I), Spike (P812R) and a truncation of ORF7a remained viable in WD-PNECs. Our work highlights the significant genetic plasticity of SARS-CoV-2 while uncovering an influential role for collaboration between viral and host cell factors in shaping viral evolution and fitness in human respiratory epithelium.

microbiology↗

bioRxiv2021

Copper signaling promotes proteostasis and animal development via allosteric activation of ubiquitin E2D conjugases

Nutrient copper supply is critical for cell growth and differentiation, and its disturbance is associated with major pathologies including cancer and neurodegeneration. Although increasing copper bioavailability in late Precambrian facilitated emergence of novel cuproproteins, their intricate regulation by this essential trace element remains largely cryptic. We found that subtle rises in cellular copper strikingly increase polyubiquitination and accelerate protein degradation within 30 minutes in numerous mammalian cell lines. We track this surprising observation to allostery induced in the UBE2D ubiquitin conjugase clade through a conserved CXXXC sub-femtomolar-affinity Cu+ binding motif. Thus, physiologic fluctuation in cytoplasmic Cu+ is coupled to the prompt degradation of UBE2D protein targets, including p53. In Drosophila harboring a larval-lethal knockdown of the nearly identical fly orthologue UbcD1, complementation with human UBE2D2 restored near-normal development, but mutation of its CXXXC Cu+ binding motif profoundly disrupted organogenesis. Nutrient Cu+ emerges as a trophic allosteric modulator of UBE2D activity through a structural motif whose evolution coincides with animal multicellularity. One Sentence SummaryModulation of nutrient copper impacts protein turnover and animal morphogenesis through conserved allostery of ubiquitin E2D conjugases. HilightsO_LINutrient copper supply is critical for cell growth and differentiation C_LIO_LIThe E2D clade of ubiquitin conjugases contains a sub-femtomolar-affinity Cu+ binding motif C_LIO_LIAllosteric activation by Cu+ markedly accelerates protein polyubiquitination C_LIO_LIThis sensor couples physiologic fluctuations in cytoplasmic Cu+ with the degradation rate of E2D targets, including p53 C_LIO_LIThis metazoan signaling mechanism is critical for drosophila morphogenesis C_LI In BriefConserved allostery of ubiquitin E2D conjugases links nutrient copper signaling to protein degradation and animal morphogenesis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/431211v2_ufig1.gif" ALT="Figure 1"> View larger version (76K): org.highwire.dtl.DTLVardef@17009fforg.highwire.dtl.DTLVardef@a07f8eorg.highwire.dtl.DTLVardef@11664c7org.highwire.dtl.DTLVardef@1327ab7_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

biochemistry↗

bioRxiv2020

Estimating divergence times from DNA sequences

The patterns of genetic variation within and among individuals and populations can be used to make inferences about the evolutionary forces that generated those patterns. Numerous population genetic approaches have been developed in order to infer evolutionary history. Here, we present the Two-Two (TT) and the Two-Two-outgroup (TTo) methods; two closely related approaches for estimating divergence time based in coalescent theory. They rely on sequence data from two haploid genomes (or a single diploid individual) from each of two populations. Under a simple population-divergence model, we derive the probabilities of the possible sample configurations. These probabilities form a set of equations that can be solved to obtain estimates of the model parameters, including population split-times, directly from the sequence data. This transparent and computationally efficient approach to infer population divergence time makes it possible to estimate time scaled in generations (assuming a mutation rate), and not as a compound parameter of genetic drift. Using simulations under a range of demographic scenarios, we show that the method is relatively robust to migration and that the TTo-method can alleviate biases that can appear from drastic ancestral population size changes. We illustrate the utility of the approaches with some examples, including estimating split times for pairs of human populations as well as providing further evidence for the complex relationship among Neandertals and Denisovans and their ancestors.

genomics↗

bioRxiv2017

Targeting the Untargetable: Predicting Pramlintide Resistance Using a Neural Network Based Cellular Automata

De novo resistance is a major issue for the use of targeted anticancer drugs in the clinic. By integrating experimental data we have created a hybrid neural network/agent-based model to simulate the evolution and spread of resistance to the drug Pramlintide in cutaneous squamous cell carcinoma. Our model can eventually be used to predict patient responses to the drug and thus enable clinicians to make decisions regarding personalized, precision treatment regimes for patients.

cancer biology↗

bioRxiv2017

SOD1A4V aggregation alters ubiquitin homeostasis in ALS

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease involving the selective death of upper and lower motor neurons in the primary motor cortex and spinal cord. A hallmark of ALS pathology is the accumulation of ubiquitinated protein inclusions within motor neurons. Previous studies suggest the sequestration of ubiquitin (Ub) into inclusions reduces the availability of free Ub, which is essential for cellular function and survival. However, the dynamics of the Ub landscape in ALS have not yet been described. Here we show that Ub homeostasis is altered in a SOD1 cell model of ALS. Utilising fluorescently tagged Ub, we followed the distribution of Ub in living cells expressing SOD1 and show that Ub is present at the earliest stages of SOD1 aggregation. We also report that cells containing aggregates of mutant SOD1 have greater ubiquitin-proteasome system (UPS) dysfunction as measured by the accumulation of the fluorescent proteasome reporter tdTomatoCL1. Furthermore, SOD1 aggregation is associated with the redistribution of Ub and depletion of the free Ub pool. Ubiquitomics analysis indicates that mutant SOD1 is associated with a shift of Ub to a pool of supersaturated proteins including those associated with oxidative phosphorylation and metabolism, corresponding with altered mitochondrial morphology and function. Taken together, these results suggest misfolded SOD1 contributes to UPS dysfunction and that Ub homeostasis is an important target for monitoring pathological changes in ALS.

molecular biology↗

bioRxiv2016

The E3 ubiquitin ligase UBR5 regulates centriolar satellite stability and primary cilia formation via ubiquitylation of CSPP-L.

Primary cilia are crucial for signal transduction in a variety of pathways, including Hedgehog and Wnt. Disruption of primary cilia formation (ciliogenesis) is linked to numerous developmental disorders (known as ciliopathies) and diseases, including cancer. The Ubiquitin-Proteasome System (UPS) component UBR5 was previously identified as a putative modulator of ciliogenesis in a functional genomics screen. UBR5 is an E3 Ubiquitin ligase that is frequently deregulated in tumours, but its biological role in cancer is largely uncharacterised, partly due to a lack of understanding of interacting proteins and pathways. We validated the effect of UBR5 depletion on primary cilia formation using a robust model of ciliogenesis, and identified CSPP1, a centrosomal and ciliary protein required for cilia formation, as a UBR5-interacting protein. We show that UBR5 ubiquitylates CSPP1, and that UBR5 is required for cytoplasmic organization of CSPP1-comprising centriolar satellites in centrosomal periphery. Hence, we have established a key role for UBR5 in ciliogenesis that may have important implications in understanding cancer pathophysiology.

cell biology↗