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Biology subjects

Ng, A. P.

Publications and source records attributed to Ng, A. P..

9 recordsLinked to original sources

DesignMaster: A Multi-Conditional Diffusion Framework for Rational PROTAC Design

MotivationProteolysis-targeting chimeras (PROTACs) enable targeted protein degradation through ternary complex formation with E3 ubiquitin ligase. However, the rational design of PROTACs remains highly challenging due to limited structure-activity relationship data and the vast conformational diversity of linkers. Existing computational approaches can be broadly divided into structure-based ternary modelling methods and fragment-based linker generation models. Although these approaches have advanced PROTAC design, they typically neglect key physicochemical constraints and linker-length control during the generation process, causing the generated PROTACs to lack balanced structural properties required for effective ternary complex formation with drug-like characteristics. ResultsTo address these limitations, we propose DesignMaster, a diffusion-based generative framework that explicitly incorporates linker length and physicochemical properties as controllable conditioning signals. DesignMaster employs an E(3)-equivariant graph Transformer with a gated multi-condition fusion module to inject linker length and physicochemical constraints throughout the diffusion process, enabling fine-grained and constraint-aware molecular generation. Experiments on PROTAC-DB 2.0 and 3.0 demonstrate that DesignMaster outperforms state-of-the-art baselines, with a 3.2% improvement in validity and a 34.4% improvement in recovery. The Case study shows DesignMaster achieves a 51.78% reduction in RMSD when predicting the linker of PROTAC BCPyr targeting 6W7O, highlighting its potential for practical structure-guided PROTAC design. AvailabilityThe source code and datasets are available at https://github.com/ABILiLab/DesignMaster.

bioinformatics↗

MolX: A Geometric Foundation Model for Protein-Ligand Modelling

Understanding how small molecules interact with protein binding pockets is central to structure-based drug discovery. Accurately modelling these interactions requires capturing the 3D geometry and physicochemical complementarity of binding interfaces, yet existing computational approaches encode proteins and ligands separately or rely on simplified structural representations that do not explicitly model cross-entity spatial relationships. Such decoupled representations restrict their capacity to capture interface-level geometric constraints that arise from protein-ligand coorganisation. Here we present MolX, a Graph Transformer foundation model that jointly learns geometric and chemical representations of protein pockets and ligands from large-scale 3D structural data. Integrating over 3 million protein pockets and 5 million molecules, MolX represents both entities as E(3)-equivariant graphs to preserve spatial geometry and chemical context. The architecture employs dual E(3)-equivariant graph Transformer encoders to model pocket and ligand embeddings, ensuring representations remain invariant to rotation, translation, and reflection. MolX is pretrained using a hybrid learning paradigm that combines supervised biochemical objectives, logP and energy-gap regression, with self-supervised geometric objectives, coordinate re-construction, and atom-type prediction, fostering generalisable molecular understanding. Across eight downstream benchmarks, including antibody-drug conjugates (ADC), proteolysis-targeting chimeras (PROTAC), molecular glue, and PCBA activity prediction, as well as binding affinity and physicochemical property regression, MolX achieves consistent state-of-the-art performance and strong cross-domain generalisation. Furthermore, MolX incorporates a sparse autoencoder module to decompose latent representations into interpretable biological components, thereby revealing the pocket-ligand interactions that drive prediction outcomes. Together, MolX establishes a scalable and interpretable foundation model for molecular representation learning, providing a unified framework for predicting and interpreting complex small-molecule-protein interactions.

bioinformatics↗

Global analysis of cancer cell responses to USP9X inhibition

The ubiquitin specific protease (USP) enzyme USP9X is amongst the best studied human deubiquitinases (DUBs), with a myriad of described targets and cellular roles. In cancer, USP9X has been touted as both an oncogene and a tumour suppressor in different contexts, which has confounded the field and questioned its viability as a cancer target. We here describe WEHI-092, a novel piperazine-based USP9X specific small molecule inhibitor and map its binding site to a unique region in the USP9X fingers subdomain, distinct from known DUB inhibitor binding sites. Using proteomics and ubiquitinomics, we show that USP9X has a distinct set of substrates compared to USP7 indicating remarkable DUB target specificity, yet the substrate profile of USP9X varies significantly across cancer cell lines. Interestingly, we reveal a core set of 17 proteins commonly regulated by USP9X in most or all cell lines, which we consider as proximal biomarkers for USP9X inhibition. Consistent with our proteomic analyses, we show that WEHI-092 treatment arrests cells in metaphase without inducing cell death, which may account for growth suppression seen in long-term clonogenic assays in most cancer cell lines, and positions USP9X inhibitors as a new potential class of selective mitotic poisons.

cell biology↗

A multi-track landscape of haematopoiesis informed by cellular barcoding and agent-based modelling

In classic ball-and-stick models of haematopoiesis the implicit assumption is that all cells within each defined stem or progenitor cell population are equivalent in their fate. Instead, more recent models suggest a haematopoietic stem and progenitor cell (HSPC) continuum of lineage bias and commitment, which is largely inferred through snapshot analysis of single cell gene expression or clonal fate. However, the dynamic assessment of lineage commitment of specific HSPC populations and their clonal output over time in vivo is still lacking but is essential to fully inform accurate models of haematopoiesis. Here, using cellular barcoding we compare the single cell output of long-term haematopoietic stem cells (LT-HSCs), short-term HSCs (ST-HSCs), multipotent progenitors (LMPPs), common myeloid progenitors (CMPs), common lymphoid progenitors (CLPs), and macrophage/dendritic cell progenitors (MDPs). Each population was assessed for their output to multiple haematopoietic cell types spanning a subset of time points from 9 to 112 days of haematopoiesis after transplantation. These analyses revealed a wide range of clonal fate patterns that were inconsistent with their eponymous labels, i.e. stem and multipotent progenitors were rarely multi- or equipotent, and common progenitors were often highly restricted in their fate. To better describe how these clonal patterns integrate into a revised landscape, a novel agent-based mathematical modelling approach that explicitly accounts for haematopoiesis at a clonal level was developed to allow the simulation of growth, timing and branching of clonal trajectories that underlie the process. Rather than a continuum, the proposed model is suggestive of multiple tracks down which clonal trajectories progress, and where fate can branch to a track of lower potency at multiple points down the entire cascade of haematopoiesis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/587126v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@b09137org.highwire.dtl.DTLVardef@de064org.highwire.dtl.DTLVardef@10705e3org.highwire.dtl.DTLVardef@139b6f_HPS_FORMAT_FIGEXP M_FIG C_FIG In a multi-track model, while some HSPCs are multipotent and branch into daughters with committed fate (left trajectory), other HSPCs may already committed or biased to a certain lineage such that their daughters inherit and maintain it in subsequent divisions during haematopoiesis. However, this commitment/fate bias is not evident through current phenotypic definitions of HSPC subsets (background colour) but occurs through putative expression of transcription factors, epigenetic programming or other factor that is currently unresolved (as indicated by the coloured fate potential triangles inside cells).

immunology↗

Iron homeostasis governs erythroid phenotype in Polycythemia Vera

Polycythemia Vera (PV) is a myeloproliferative neoplasm driven by activating mutations in JAK2 that result in unrestrained erythrocyte production, increasing patients hematocrit and hemoglobin concentration, placing them at risk of life-threatening thrombotic events. Our GWAS of 440 PV cases and 403,351 controls utilising UK Biobank data found that SNPs in HFE known to cause hemochromatosis are highly associated with PV diagnosis, linking iron regulation to PV. Analysis of the FinnGen dataset independently confirmed over-representation of homozygous HFE mutations in PV patients. HFE influences expression of hepcidin, the master regulator of systemic iron homeostasis. Through genetic dissection of PV mouse models, we show that the PV erythroid phenotype is directly linked to hepcidin expression: endogenous hepcidin upregulation alleviates erythroid disease whereas hepcidin ablation worsens it. Further, we demonstrate that in PV, hepcidin is not regulated by expanded erythropoiesis but is likely governed by inflammatory cytokines signalling via GP130 coupled receptors. These findings have important implications for understanding the pathophysiology of PV and offer new therapeutic strategies for this disease.

cancer biology↗

Single-cell multi-region dissection of brain vasculature in Alzheimer's Disease

Cerebrovascular breakdown occurs early in Alzheimers Disease (AD), but its cell-type-specific molecular basis remains uncharacterized. Here, we characterize single-cell transcriptomic differences in human cerebrovasculature across 220 AD and 208 control individuals and across 6 brain regions. We annotate 22,514 cerebrovascular cells in 11 subtypes of endothelial, pericyte, smooth muscle, perivascular fibroblast, and ependymal cells, and how they differ in abundance and gene expression between brain regions. We identify 2,676 AD-differential genes, including lower expression of PDGFRB in pericytes, and ABCB1 and ATP10A in endothelial cells. These AD-differential genes reveal common upstream regulators, including MECOM, EP300, and KLF4, whose targeting may help restore vasculature function. We find coordinated vasculature-glial-neuronal co-expressed gene modules supported by ligand-receptor pairs, involved in axon growth/degeneration and neurogenesis, suggesting mechanistic mediators of neurovascular unit dysregulation in AD. Integration with AD genetics reveals 125 AD-differential genes directly linked to AD-associated genetic variants (through vasculature-specific eQTLs, Hi-C, and correlation-based evidence), 559 targeted by AD-associated regulators, and 661 targeted by AD-associated ligand-receptor signaling. Lastly, we show that APOE4-genotype associated differences are significantly enriched among AD-associated genes in capillary and venule endothelial cells, and subsets of pericytes and fibroblasts, which underlie the vascular dysregulation in APOE4-associated cognitive decline. Overall, our multi-region molecular atlas of differential human cerebrovasculature genes and pathways in AD can help guide early-stage AD therapeutics.

neuroscience↗

The clonal and molecular aetiology of emergency dendritic cell development

Extrinsic regulation of single haematopoietic stem and progenitor cell (HSPC) fate is crucial for immune cell development. Here, we examine the aetiology of Flt3 ligand (Flt3L)-mediated emergency development of type 1 conventional dendritic cells (cDC1s), which results in enhanced immunity against infections and cancer. Using cellular barcoding, we demonstrate a predominant role of enhanced clonal expansion and moderate contribution via recruitment of additional cDC1-generating HSPCs. The selective cDC1 expansion occurs primarily via multi-/oligo-potent clones, without compromising output to other lineages. To understand the molecular hallmarks early during a Flt3L response, we develop Divi-Seq to simultaneously profile cell division history, surface phenotype and transcriptional state of single HSPCs. We discover that Flt3L-responsive HSPCs maintain a proliferative early progenitor-like state, which leads to selective emergence of CD11c+cKit+ transitional precursors with high cellular output to cDC1s. These findings inform the mechanistic action of Flt3L in natural immunity and immunotherapy at a clonal level.

immunology↗

An Erg driven transcriptional program controls B-lymphopoiesis

Summary/AbstractB-cell development is initiated by the stepwise differentiation of hematopoietic stem cells into lineage committed progenitors, ultimately generating the mature B-cells that mediate protective immunity. This highly regulated process also generates clonal immunological diversity via recombination of immunoglobulin genes. While several transcription factors that control B-cell development and V(D)J recombination have been defined, how these processes are initiated and coordinated into a precise regulatory network remains poorly understood. Here, we show that the transcription factor ETS Related Gene (Erg) is essential for the earliest steps in B-cell differentiation. Erg initiates a transcriptional network involving the B-cell lineage defining genes, Ebf1 and Pax5, that directly promotes the expression of key genes involved in V(D)J recombination and formation of the B-cell receptor. Complementation of the Erg-deficiency with a productively rearranged immunoglobulin gene rescued B-cell development, demonstrating that Erg is an essential and exquisitely stage specific regulator of the gene regulatory network controlling B-lymphopoiesis.

immunology↗

A data-driven approach to characterising intron signal in RNA-seq data

RNA-seq datasets can contain millions of intron reads per sequenced library that are typically removed from downstream analysis. Only reads overlapping annotated exons are considered to be informative since mature mRNA is assumed to be the major component sequenced, especially when examining poly(A) RNA samples. In this paper, we demonstrate that intron reads are informative and that pre-mRNA is the major source of intron signal. Making use of pre-mRNA signal, our index method combines differential expression analyses from intron and exon counts to categorise changes observed in each count set, giving additional genes with evidence of transcriptional changes when compared to a classic approach. Considering the importance of intron retention in some biological systems, another novel method, superintronic, looks for evidence of intron retention after accounting for the presence of pre-mRNA signal. The results presented here overcomes deficiencies and biases in previous works related to intron reads by exploring multiple sources for intron reads simultaneously using a data-driven approach, and provides a broad overview into how intron reads can be utilised in relation to multiple aspects of transcriptional biology.

bioinformatics↗