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Center for Nonlinear Studies |
RNA structure dictates RNA function. Therefore, understanding RNA utility inside the cell and acting on that utility requires knowledge of a RNA’s 3-Dimensional structure. However, computational RNA structure modeling is difficult due to the existence of highly competitive energy landscapes and long timescales inherent in RNA folding. I will present RACER, an efficient coarse-grained RNA model, which is capable of both accurate structure prediction and capturing relevant free energy landscapes. RACER was constructed from statistical potentials and comparison to experimental thermodynamic data. RACER employs several novel potential functions including an effective short range interaction and a directional hydrogen bonding potential. We found that separating base pairing and base stacking interactions was essential to distinguish folded and unfolded states. Using a simulated annealing protocol, RACER achieves accurate native structure prediction for a set of 14 RNAs (average RMSD of 4.15 Å). Further, from extensive equilibrium pulling simulations (0.5ms total), RACER’s sequence-specific variation of free energy is in excellent agreement with experimentally measured stabilities (R2=0.96) for a set of 11 hairpins and duplexes.
Host: Gnana Gnanakaran