Biology subjects

Qiu, J.-H.

Publications and source records attributed to Qiu, J.-H..

3 recordsLinked to original sources

bioRxiv2026

Visualizing single base RNA mutations in living cells through DNA nanostructure mediated amplification

Capturing RNA dynamics in living cells would provide critical insights into transcriptional control and cellular adaptation, but remains technically formidable -- particularly at single-base precision. Here, we introduce a DNA tetrahedron based three-dimensional catalytic hairpin assembly (3D@CHA) nanoplatform that couples target recognition with catalytic activation in a spatially organized framework. Three cascaded hairpins (H-AN, H1, and H2) then enable localized and efficient signal amplification. Without external carriers or transfection, the platform exhibits robust biocompatibility, distinguishing highly homologous insulin I (Ins1) and insulin II (Ins2) mRNAs in living cells and tracking their redistribution and intercellular transfer during metabolic changes. Introducing a single-base mismatch site into H1 and coupling it with a Forster resonance energy transfer (FRET) readout yielded a KRAS-3D@CHA probe capable of detecting KRASG12D mutations at the RNA level with single-base resolution. This platform establishes a programmable framework for precise RNA imaging and mutation discrimination, opening new avenues for RNA-level diagnostics and precision oncology.

cell biology↗

bioRxiv2025

SAKPE: A Site Attention Kinetic Parameters Prediction Method for Enzyme Engineering

0.The quantitative determination of enzyme kinetic parameters traditionally relies on experimental methods that are both time-intensive and costly. Machine learning models have demonstrated significant potential for predicting enzyme kinetic parameters in recent years. Despite this promise, these methods face challenges, including limited training data, inadequate sensitivity to subtle mutations, and poor alignment with practical enzyme engineering contexts. Here, we introduce SAKPE (Site Attention Kinetic Parameters Prediction Method for Enzyme Engineering), a novel machine-learning framework designed to predict enzyme kinetic parameters with enhanced accuracy in practical application scenarios. By incorporating protein representation, substrate representation, and protein representation with weights for important sites, SAKPE significantly outperforms existing methods in predicting enzymatic kinetic parameters, including turnover number (kcat), Michaelis constant (Km), and inhibition constant (Ki). Incorporating protein representation with weights for important sites enables SAKPE to effectively capture the impact of mutations, especially mutations of important sites and their surrounding amino acids of interest in enzyme engineering, on enzyme kinetics parameters. SAKPE offers a robust and practical tool for predicting enzyme kinetic parameters, providing a superior tool for enzyme engineering scenarios such as enzyme design, directed evolution, and industrial applications.

bioinformatics↗

bioRxiv2025

A De Novo Design Strategy to Convert FAcD from Dimer to Active Monomer

Enzymes largely exist in various oligomeric states, but monomeric enzymes are more conducive to industrial applications. Converting an oligomeric enzyme into an active monomer is a significant challenge. In this study, we present a de novo design strategy to convert fluoroacetate dehalogenase (FAcD) from its native dimeric form to an active monomer. Using the AI-based method ProteinMPNN, we identified critical protein-protein interaction (PPI) sites at the dimer interface. ArDCA, another AI tool, was employed to pinpoint catalytic hotspots. Six mutants, Mu1-Mu6, were designed. Molecular dynamics (MD) simulations, coupled with mass spectrometry, confirmed that these mutants form stable monomers. The pre-reaction state (PRS) model predicted that three of these mutants exhibited catalytic activity. In particular, Mu5 with 11 mutations from the wild-type, was predicted to have high catalytic activity, and was subsequently confirmed by kinetics experiment, with a kcat of 672.2 min-1 and a T5030 > 100 {degrees}C, comparable to the wild-type enzyme (kcat = 676.3 min-1, T5030 = 84 {degrees}C). Notably, the Y149M mutation increased catalytic activity nearly forty-fold, demonstrating the effectiveness of our design strategy.

bioengineering↗