Biology subjects

Ng, M. Y.

Publications and source records attributed to Ng, M. Y..

8 recordsLinked to original sources

bioRxiv2026

A druggable redox switch on SHP1 controls macrophage inflammation

Immunological proteins are major disease targets, yet most remain undrugged. Post-translational redox modification of cysteine residues has emerged as an important mode of immune cell regulation, particularly in macrophage cytokine responses. Here, we develop a strategy for systematic discovery and small-molecule functionalization of redox-regulated cysteines on immunological proteins. Using deep redox proteomics, we annotate 788 in vivo redox-regulated cysteines across diverse immune-relevant protein domains. We demonstrate how these sites enable cysteine-directed pharmacology through discovery of a novel cysteine activation site on the immune regulator SHP1. Targeting Cys102, we develop a highly selective covalent agonist, SCA, which binds the N-SH2 domain to relieve autoinhibition and activate SHP1. In mouse and human macrophages, SCA selectively engages SHP1 Cys102, antagonizing IRAK signaling and LPS-induced pro-inflammatory cytokine production. Together, this work identifies a druggable cysteine redox switch controlling macrophage cytokine responses and provides a compendium of redox-regulated sites for therapeutic development.

biochemistry↗

bioRxiv2025

Genome modeling and design across all domains of life with Evo 2

All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation--from noncoding pathogenic mutations to clinically significant BRCA1 variants--without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon-intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.

genomics↗

bioRxiv2024

The RAB27A effector SYTL5 regulates mitophagy and mitochondrial metabolism

SYTL5 is a member of the Synaptotagmin-Like (SYTL) protein family that differs from the Synaptotagmin family by having a unique N-terminal Synaptotagmin homology domain that directly interacts with the small GTPase RAB27A. Several SYTL protein family members have been implicated in plasma membrane transport and exocytosis, but the specific function of SYTL5 remains unknown. We here show that SYTL5 is a RAB27A effector and that both proteins localise to mitochondria and vesicles containing mitochondrial material. Mitochondrial recruitment of SYTL5 depends on its interaction with functional RAB27A. We demonstrate that SYTL5-RAB27A positive vesicles containing mitochondrial material, autophagy proteins and LAMP1 form during hypoxia and that depletion of SYTL5 and RAB27A reduces mitophagy under hypoxia mimicking conditions, indicating a role for these proteins in mitophagy. Indeed, we find that SYTL5 interacts with proteins involved in vesicle-mediated transport and cellular response to stress and that its depletion compromises mitochondrial respiration and increases glucose uptake. Intriguingly, SYTL5 expression is significantly reduced in tumours of the adrenal gland, and correlates positively with survival for patients with adrenocortical carcinoma.

cell biology↗

bioRxiv2024

Single-Molecule Studies of Cognate and Near-Cognate Elongation in an in vitro Eukaryotic Translation System

The ribosome plays a central role in translation of the genetic code into amino acid sequences during synthesis of polypeptides. During each cycle of peptide elongation, the ribosome must discriminate between correct and incorrect aminoacyl-tRNAs according to the codon present in its A-site. Ribosomes rely on a complex sequence of proofreading mechanisms to minimize erroneous selection of incorrect aminoacyl-tRNAs that would lead to mistakes in translation. These mechanisms have been studied extensively in prokaryotic organisms, but eukaryotic elongation is less well understood. Here, we use single-molecule fluorescence resonance energy transfer (smFRET) with an in vitro eukaryotic translation system to investigate tRNA selection and subsequent steps during peptide elongation. We compared accommodation of a tryptophan-aminoacyl-tRNA into the ribosomal A-site containing either a cognate or near-cognate codon and unexpectedly found that, following an initial slow sampling event, subsequent near-cognate sampling events proceeded more rapidly than the initial event. Further, we found a strong negative correlation between the concentration of near-cognate aminoacyl-tRNA and the efficiency of tRNA accommodation. These novel characteristics of near-cognate interaction with the eukaryotic ribosome suggest that rejection of a near-cognate tRNAs leads to formation of an altered ribosomal conformation that assists in rejecting subsequent incorrect tRNA interactions.

biophysics↗

bioRxiv2024

Sequence modeling and design from molecular to genome scale with Evo

The genome is a sequence that completely encodes the DNA, RNA, and proteins that orchestrate the function of a whole organism. Advances in machine learning combined with massive datasets of whole genomes could enable a biological foundation model that accelerates the mechanistic understanding and generative design of complex molecular interactions. We report Evo, a genomic foundation model that enables prediction and generation tasks from the molecular to genome scale. Using an architecture based on advances in deep signal processing, we scale Evo to 7 billion parameters with a context length of 131 kilobases (kb) at single-nucleotide, byte resolution. Trained on 2.7M prokaryotic and phage genomes, Evo can generalize across the three fundamental modalities of the central dogma of molecular biology to perform zero-shot function prediction that is competitive with, or outperforms, leading domain-specific language models. Evo also excels at multi-element generation tasks, which we demonstrate by generating synthetic CRISPR-Cas molecular complexes and entire transposable systems for the first time. Using information learned over whole genomes, Evo can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to 650 kb in length, orders of magnitude longer than previous methods. Advances in multi-modal and multiscale learning with Evo provides a promising path toward improving our understanding and control of biology across multiple levels of complexity.

synthetic biology↗

bioRxiv2023

Cytosolic and mitochondrial translation elongation are coordinated through the molecular chaperone TRAP1 for the synthesis and import of mitochondrial proteins

A complex interplay between mRNA translation and cellular respiration has been recently unveiled, but its regulation in humans is poorly characterized in either health or disease. Cancer cells radically reshape both biosynthetic and bioenergetic pathways to sustain their aberrant growth rates. In this regard, we have shown that the molecular chaperone TRAP1 not only regulates the activity of respiratory complexes, behaving alternatively as an oncogene or a tumor suppressor, but also plays a concomitant moonlighting function in mRNA translation regulation. Herein we identify the molecular mechanisms involved, demonstrating that TRAP1: i) binds both mitochondrial and cytosolic ribosomes as well as translation elongation factors, ii) slows down translation elongation rate, and iii) favors localized translation in the proximity of mitochondria. We also provide evidence that TRAP1 is coexpressed in human tissues with the mitochondrial translational machinery, which is responsible for the synthesis of respiratory complex proteins. Altogether, our results show an unprecedented level of complexity in the regulation of cancer cell metabolism, strongly suggesting the existence of a tight feedback loop between protein synthesis and energy metabolism, based on the demonstration that a single molecular chaperone plays a role in both mitochondrial and cytosolic translation, as well as in mitochondrial respiration.

biochemistry↗

bioRxiv2022

An AI-assisted Tool For Efficient Prostate Cancer Diagnosis

Pathologists diagnose prostate cancer by core needle biopsy. For low-grade and low-volume cases, the pathologists look for the few malignant glands out of hundreds within a core. They may miss the few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep learning pipeline detecting malignant glands in core needle biopsies to help pathologists effectively and accurately diagnose prostate cancer in low-grade and low-volume cases. The pipeline consisted of two stages: the gland segmentation model detected the glands within the sections and the multi-resolution model classified each detected gland into benign vs. malignant. Analyzing a gland at multiple resolutions provided the classification model to exploit both morphology information (of nuclei and glands) and neighborhood information (for architectural patterns), important in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. The images were made publicly available, becoming the first digital histopathology dataset of prostatic carcinoma patients of Asian ancestry. Our pipeline successfully classified the core needle biopsy parts (81 parts: 50 benign and 31 malignant) into benign vs. malignant. It achieved an AUROC value of 0.997 (95% CI: 0.987 - 1.000). Moreover, it produced heatmaps highlighting the malignancy of each gland in core needle biopsies. Hence, our pipeline can effectively assist pathologists in core needle biopsy analysis.

cancer biology↗

bioRxiv2018

A new in vitro assay measuring direct interaction of nonsense suppressors with the eukaryotic protein synthesis machinery

Nonsense suppressors (NonSups) induce \"readthrough\", i.e., the selection of near cognate tRNAs at premature termination codons and insertion of the corresponding amino acid into nascent polypeptide. Prior readthrough measurements utilized contexts in which NonSups can promote readthrough directly, by binding to one or more of the components of the protein synthesis machinery, or indirectly, by several other mechanisms. Here we utilize a new, highly-purified in vitro assay to measure exclusively direct nonsense suppressor-induced readthrough. Of 16 NonSups tested, 12 display direct readthrough, with results suggesting that such NonSups act by at least two different mechanisms. In preliminary work we demonstrate the potential of single molecule fluorescence energy transfer measurements to elucidate mechanisms of NonSup-induced direct readthrough, which will aid efforts to identify NonSups having improved clinical efficacy.\n\nTable of Contents artwork\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC=\"FIGDIR/small/330506_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (18K):\norg.highwire.dtl.DTLVardef@a548dborg.highwire.dtl.DTLVardef@15237bcorg.highwire.dtl.DTLVardef@c93d3forg.highwire.dtl.DTLVardef@1d40e2c_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗