Showing only posts tagged ViennaRNA. Show all posts.

Deep learning methods are unable to predict RNA secondary structures

Machine learning of RNA structure is more challenging than you might think. Using synthetic data from ViennaRNA's RNAfold to study the capabilities and shortcomings of neural networks for RNA secondary structure prediction in a controlled setting, we argue that shortcomings in the artificial setting will translate to real data

Posted by Michael T. Wolfinger on in publications. updated Tags: ViennaRNA, AI.

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