Showing only posts tagged AI. Show all posts.

Jun 09 2023 RNA-protein complex refinement using AI modeling and docking

This study presents an efficient technique for refining protein-RNA complexes using 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

Jul 26 2022 Musashi-1 RNA-binding protein's role in RNA recognition

In this contribution we study the association complexes of the RNA-binding protein Musashi-1 with different RNA motifs, using molecular dynamics simulations

Dec 16 2021 Current deep learning methods 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