AI affects RNA structure and RNA-protein modeling in very different ways depending on whether the task concerns ranking, geometry generation, or mechanistic inference, and whether the output remains anchored in physics and experiment.
This review article surveys how computational modeling, molecular dynamics, and AI-derived structures help explain Musashi-RNA recognition in both cellular regulation and viral pathogenesis.
This article explains a workflow for refining protein-RNA complexes by combining AI-based structural models with flexible docking and enhanced sampling.
This study combines computational and biophysical analysis to characterize a long-range RNA-RNA interaction in Japanese encephalitis virus and to test the role of the conserved cyclization sequence.
Molecular dynamics and binding-energy calculations are used here to compare how Musashi-1 recognizes different RNA motifs and to identify determinants of binding specificity.