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This study explores the integration of experimental data and computational methods to enhance structural predictions of RNA molecules. Leveraging tools like RiboWeb, DMS Footprinting, and EPR, we discuss how various biochemical approaches yield accurate structural insights. Additionally, we highlight the significance of structural motifs, base pairs, and long-range interactions while using phylogenetic co-variance analysis and M-fold methodologies. By populating instances from the Protein Data Bank (PDB) and experimental results, we create a more robust framework for RNA structure prediction, benefiting both biochemists and computational biologists.
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Experimental Annotations in ROC Russ Altman, Alain Laederach Based on our experiences with RiboWeb and our participation in a Program Project Grant
Structural Information • PDB • Evidence for Structural Motifs • Base Pairs, Long range interactions etc. • Experiments • Many Biochemists getting more or less accurate structural evidence as well • DMS Footprinting, Hydroxyl Radical Footprinting • Tethered BABE probes, FRET, NMR, EPR • Even computational folks predict structure • Phylogenetic co-variance analysis • M-Fold
Why is this useful? • We can populate instances from PDB • We can also populate from experiments • Biochemical • Computational • For structure prediction: • Make PDB from ensemble of biochemical data
