Showing only posts tagged new method. Show all posts.

RNA-protein complex refinement using AI modeling and docking

This study presents an efficient technique for refining protein-RNA complexes using artifilial intelligence (AI) based modeling and flexible docking. The method, utilizing parallel cascade selection molecular dynamics (PaCS-MD), accelerates conformational sampling of flexible RNA regions and produces high-quality complex models. Experimental validation demonstrates its superiority over template-based modeling, suggesting its potential for constructing complexes with non-canonical RNA-protein interactions

Co-transcriptional riboswitch modleing with ViennaRNA

Riboswitches are RNA molecules that regulate gene expression by sensing metabolites, presenting an interesting target for synthetic biology applications. We present a computational approach based on ViennaRNA tools to dissect and model RNA-ligand interaction dynamics under kinetic control, enabling simulation of riboswitch folding

In silico design of ligand triggered RNA switches

In the world of synthetic biology, the design of RNA switches holds immense promise for various applications, ranging from diagnostics to therapeutics. This paper presents a comprehensive workflow for designing RNA switches that can dynamically alter their structural conformations in response to specific ligands

SHAPE directed RNA folding with the ViennaRNA Package

The ViennaRNA Package 2.0 brings powerful dynamic programming algorithms to researchers studying nucleic acid folding. In this post, we explore three SHAPE-guided methods—Deigan, Zarringhalam, and Washietl—that have been integrated into our toolkit to improve predictions of base pair interactions and minimum free energy (MFE) structures for RNA molecules. By combining chemical probing data with in silico modeling, these approaches help capture real-world folding behaviors and enhance the accuracy of computational RNA structure predictions

page 1 | older articles »