Biology subjects

Kuang, W.

Publications and source records attributed to Kuang, W..

21 records · Page 2Linked to original sources

bioRxiv2019

Divide and Conquer: Real-time maximum likelihood fitting of multiple emitters for super-resolution localization microscopy

Multi-emitter localization has great potential for maximizing the imaging speed of super-resolution localization microscopy. However, the slow image analysis speed of reported multi-emitter localization algorithms limits their usage in mostly off-line image processing with small image size. Here we adopt the well-known divide and conquer strategy in computer science and present a fitting-based method called QC-STORM for fast multi-emitter localization. Using simulated and experimental data, we verify that QC-STORM is capable of providing real-time full image processing on raw images with 100 {micro}m x 100 {micro}m field of view and 10 ms exposure time, with comparable spatial resolution as the popular fitting-based ThunderSTORM and the up-to-date non-iterative WindSTORM. This study pushes the development and practical use of super-resolution localization microscopy in high-throughput or high-content imaging of cell-to-cell differences or discovering rare events in a large cell population.

biophysics↗

bioRxiv2018

Resistance from Afar: Distal Mutation V36M Allosterically Modulates the Active Site to Accentuate Drug Resistance in HCV NS3/4A Protease

Hepatitis C virus rapidly evolves, conferring resistance to direct acting antivirals. While resistance via active site mutations in the viral NS3/4A protease has been well characterized, the mechanism for resistance of non-active site mutations is unclear. R155K and V36M often co-evolve and while R155K alters the electrostatic network at the binding site, V36M is more than 13 [A] away. In this study the mechanism by which V36M confers resistance, in the context of R155K, is elucidated with drug susceptibility assays, crystal structures, and molecular dynamics (MD) simulations for three protease inhibitors: telaprevir, boceprevir and danoprevir. The R155K and R155K/V36M crystal structures differ in the -2 helix and E2 strand near the active site, with alternative conformations at M36 and side chains of active site residues D168 and R123, revealing an allosteric coupling, which persists dynamically in MD simulations, between the distal mutation and the active site. This allosteric modulation validates the network hypothesis and elucidates how distal mutations confer resistance through propagation of conformational changes to the active site.

molecular biology↗

bioRxiv2017

A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information

The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. Systematic integration of these heterogeneous data not only serves as a promising tool for identifying new drug-target interactions (DTIs), which is an important step in drug development, but also provides a more complete understanding of the molecular mechanisms of drug action. In this work, we integrate diverse drug-related information, including drugs, proteins, diseases and side-effects, together with their interactions, associations or similarities, to construct a heterogeneous network with 12,015 nodes and 1,895,445 edges. We then develop a new computational pipeline, called DTINet, to predict novel drug-target interactions from the constructed heterogeneous network. Specifically, DTINet focuses on learning a low-dimensional vector representation of features for each node, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then predicts the likelihood of a new DTI based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for DTI prediction. Moreover, we have experimentally validated the novel interactions between three drugs and the cyclooxygenase (COX) protein family predicted by DTINet, and demonstrated the new potential applications of these identified COX inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs. The source code of DTINet and the input heterogeneous network data can be downloaded from http://github.com/luoyunan/DTINet.

bioinformatics↗