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 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

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 Deep learning methods are unable to predict RNA secondary structures

RNA structure prediction might seem like an ideal fit for machine learning, but it's more challenging than you might think. In this paper, we explore these difficulties by using synthetic data generated by ViennaRNA's RNAfold, offering a controlled environment to study how neural networks handle RNA secondary structure prediction. What we found suggests that the limitations seen in artificial settings can directly translate to real-world data, raising important questions about the effectiveness of current machine learning approaches in this field.