Biology subjects

Ng, H. L.

Publications and source records attributed to Ng, H. L..

7 recordsLinked to original sources

bioRxiv2023

Development, Characterization, and Structural Analysis of a Genetically Encoded Red Fluorescent Peroxynitrite Biosensor

Boronic acid-containing fluorescent molecules have been widely used to sense hydrogen peroxide and peroxynitrite, which are important reactive oxygen and nitrogen species in biological systems. However, it has been challenging to gain specificity. Our previous studies developed genetically encoded, green fluorescent peroxynitrite biosensors by genetically incorporating a boronic acid-containing noncanonical amino acid (ncAA), p-boronophenylalanine (pBoF), into the chromophore of circularly permuted green fluorescent proteins (cpGFPs). In this work, we introduced pBoF to amino acid residues spatially close to the chromophore of an enhanced circularly permuted red fluorescent protein (ecpApple). Our effort has resulted in two responsive ecpApple mutants: one bestows reactivity toward both peroxynitrite and hydrogen peroxide, while the other, namely pnRFP, is a selective red fluorescent peroxynitrite biosensor. We characterized pnRFP in vitro and in live mammalian cells. We further studied the structure and sensing mechanism of pnRFP using X-ray crystallography, 11B-NMR, and computational methods. The boron atom in pnRFP adopts an sp2-hybridization geometry in a hydrophobic pocket, and the reaction of pnRFP with peroxynitrite generates a product with a twisted chromophore, corroborating the observed "turn-off" fluorescence response. Thus, this study extends the color palette of genetically encoded peroxynitrite biosensors, provides insight into the response mechanism of the new biosensor, and demonstrates the versatility of using protein scaffolds to modulate chemoreactivity.

biochemistry↗

bioRxiv2023

Targeted engagement of β-catenin-Ikaros complexes in refractory B-cell malignancies

In most cell types, nuclear {beta}-catenin functions as prominent oncogenic driver and pairs with TCF7-family factors for transcriptional activation of MYC. Surprisingly, B-lymphoid malignancies not only lacked expression and activating lesions of {beta}-catenin but critically depended on GSK3{beta} for effective {beta}-catenin degradation. Our interactome studies in B-lymphoid tumors revealed that {beta}-catenin formed repressive complexes with lymphoid-specific Ikaros factors at the expense of TCF7. Instead of MYC-activation, {beta}-catenin was essential to enable Ikaros-mediated recruitment of nucleosome remodeling and deacetylation (NuRD) complexes for transcriptional repression of MYC. To leverage this previously unrecognized vulnerability of B-cell-specific repressive {beta}-catenin-Ikaros-complexes in refractory B-cell malignancies, we examined GSK3{beta} small molecule inhibitors to subvert {beta}-catenin degradation. Clinically approved GSK3{beta}-inhibitors that achieved favorable safety prof les at micromolar concentrations in clinical trials for neurological disorders and solid tumors were effective at low nanomolar concentrations in B-cell malignancies, induced massive accumulation of {beta}-catenin, repression of MYC and acute cell death. Preclinical in vivo treatment experiments in patient-derived xenografts validated small molecule GSK3{beta}-inhibitors for targeted engagement of lymphoid-specific {beta}-catenin-Ikaros complexes as a novel strategy to overcome conventional mechanisms of drug-resistance in refractory malignancies. HIGHLIGHTSO_LIUnlike other cell lineages, B-cells express nuclear {beta}-catenin protein at low baseline levels and depend on GSK3{beta} for its degradation. C_LIO_LIIn B-cells, {beta}-catenin forms unique complexes with lymphoid-specific Ikaros factors and is required for Ikaros-mediated tumor suppression and assembly of repressive NuRD complexes. C_LIO_LICRISPR-based knockin mutation of a single Ikaros-binding motif in a lymphoid MYC superenhancer region reversed {beta}-catenin-dependent Myc repression and induction of cell death. C_LIO_LIThe discovery of GSK3{beta}-dependent degradation of {beta}-catenin as unique B-lymphoid vulnerability provides a rationale to repurpose clinically approved GSK3{beta}-inhibitors for the treatment of refractory B-cell malignancies. C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/532152v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@1b70e4dorg.highwire.dtl.DTLVardef@10bfcc8org.highwire.dtl.DTLVardef@b10eorg.highwire.dtl.DTLVardef@1683a3e_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIAbundant nuclear {beta}-catenin C_LIO_LI{beta}-catenin pairs with TCF7 factors for transcriptional activation of MYC C_LIO_LIB-cells rely on efficient degradation of {beta}-catenin by GSK3{beta} C_LIO_LIB-cell-specific expression of Ikaros factors C_LI Unique vulnerability in B-cell tumors: O_LIGSK3{beta}-inhibitors induce nuclear accumulation of {beta}-catenin. C_LIO_LI{beta}-catenin pairs with B-cell-specific Ikaros factors for transcriptional repression of MYC C_LI

cancer biology↗

bioRxiv2022

A high-performance genetically encoded fluorescent indicator for in vivo cAMP imaging

cAMP is a key second messenger that regulates diverse cellular functions including neural plasticity. However, the spatiotemporal dynamics of intracellular cAMP in intact organisms are largely unknown due to low sensitivity and/or brightness of current genetically encoded fluorescent cAMP indicators. Here, we report the development of the new circularly permuted GFP (cpGFP)-based cAMP indicator G-Flamp1, which exhibits a large fluorescence increase (a maximum {Delta}F/F0 of 1100% in HEK293T cells), relatively high brightness, appropriate affinity (a Kd of 2.17 {micro}M) and fast response kinetics (an association and dissociation half-time of 0.20 s and 0.087 s, respectively). Furthermore, the crystal structure of the cAMP-bound G-Flamp1 reveals one linker connecting the cAMP-binding domain to cpGFP adopts a distorted {beta}-strand conformation that may serve as a fluorescence modulation switch. We demonstrate that G-Flamp1 enables sensitive monitoring of endogenous cAMP signals in brain regions that are implicated in learning and motor control in living organisms such as fruit flies and mice.

molecular biology↗

bioRxiv2020

Mechanisms of allosteric and mixed mode aromatase inhibitors

Aromatase (Cyp19) catalyzes the last biosynthetic step of estrogens in mammals and is a primary drug target for hormone-related breast cancer. However, treatment with aromatase inhibitors is often associated with adverse effects and drug resistance. In this study, we used virtual screening targeting a predicted cytochrome P450 reductase binding site on aromatase to discover four novel non-steroidal aromatase inhibitors. The inhibitors have potencies comparable to the noncompetitive tamoxifen metabolite, endoxifen. Our two most potent inhibitors, AR11 and AR13, exhibit both mixed-type and competitive-type inhibition. The cytochrome P450 reductase-Cyp19 coupling interface likely acts as a transient binding site. Our modeling shows that our inhibitors bind better at different sites near the catalytic site. Our results predict the location of multiple ligand binding sites on aromatase. The combination of modeling and experimental results supports the important role of the reductase binding interface as a low affinity, promiscuous ligand binding site. Our new inhibitors may be useful as alternative chemical scaffolds that may show different adverse effects profiles than current clinically used aromatase inhibitors.

biochemistry↗

bioRxiv2019

Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4

Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) offered a unique opportunity for designing and testing novel methodology for accurate docking and affinity prediction of ligands in an open and blinded manner. We participated in the beta-secretase 1 (BACE) Subchallenge which is comprised of cross-docking and redocking of 20 macrocyclic ligands to BACE and predicting binding affinity for 154 macrocyclic ligands. For this challenge, we developed machine learning models trained specifically on BACE. We developed a deep neural network (DNN) model that used a combination of both structure and ligand-based features that outperformed simpler machine learning models. According to the results released by D3R, we achieved a Spearmans rank correlation coefficient of 0.43(7) for predicting the affinity of 154 ligands. We describe the formulation of our machine learning strategy in detail. We compared the performance of DNN with linear regression, random forest, and support vector machines using ligand-based, structure-based, and combining both ligand and structure-based features. We compared different structures for our DNN and found that performance was highly dependent on fine optimization of the L2 regularization hyperparameter, alpha. We also developed a novel metric of ligand three-dimensional similarity inspired by crystallographic difference density maps to match ligands without crystal structures to similar ligands with known crystal structures. This report demonstrates that detailed parameterization, careful data training and implementation, and extensive feature analysis are necessary to obtain strong performance with more complex machine learning methods. Post hoc analysis shows that scoring functions based only on ligand features are competitive with those also using structural features. Our DNN approach tied for fifth in predicting BACE-ligand binding affinities.

bioinformatics↗

bioRxiv2018

Locating ligand binding sites in G-protein coupled receptors using combined information from docking and sequence conservation

G-protein coupled receptors (GPCRs) are the largest protein family of drug targets. Detailed mechanisms of binding are unknown for many important GPCR-ligand pairs due to the difficulties of GPCR recombinant expression, biochemistry, and crystallography. We describe our new method, ConDock, for predicting ligand binding sites in GPCRs using combined information from surface conservation and docking starting from crystal structures or homology models. We demonstrate the effectiveness of ConDock on well-characterized GPCRs such as the {beta}2 adrenergic and A2A adenosine receptors. We also demonstrate that ConDock successfully predicts ligand binding sites from high-quality homology models. Finally, we apply ConDock to predict ligand binding sites on a structurally uncharacterized GPCR, GPER. GPER is the G-protein coupled estrogen receptor, with four known ligands: estradiol, G1, G15, and tamoxifen. ConDock predicts that all four ligands bind to the same location on GPER, centered on L119, H307, and N310; this site is deeper in the receptor cleft than predicted by previous studies. We compare the sites predicted by ConDock and traditional methods that utilize information from surface geometry, surface conservation, and ligand chemical interactions. Incorporating sequence conservation information in ConDock overcomes errors introduced from physics-based scoring functions and homology modeling.

bioinformatics↗

bioRxiv2017

Recombinant expression in E. coli of human FGFR2 with its transmembrane and extracellular domains

Fibroblast growth factor receptors (FGFRs) are a family of receptor tyrosine kinases containing three domains: an extracellular receptor domain, a single transmembrane helix, and an intracellular tyrosine kinase domain. FGFRs are activated by fibroblast growth factors (FGFs) as part of complex signal transduction cascades regulating angiogenesis, skeletal formation, cell differentiation, proliferation, cell survival, and cancer. We have developed the first recombinant expression system in E. coli to produce a construct of human FGFR2 containing its transmembrane and extracellular receptor domains. We demonstrate that the expressed construct is functional in binding heparin and dimerizing. Size exclusion chromatography demonstrates that the purified FGFR2 does not form a complex with FGF1 or adopts an inactive dimer conformation. Progress towards the successful recombinant production of intact FGFRs will facilitate further biochemical experiments and structure determination that will provide insight into how extracellular FGF binding activates intracellular kinase activity.

biochemistry↗