Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters.

By Magdalena A. Jonikas, Randall J. Radmer, Alain Laederach, Rhiju Das, Samuel M. Pearlman, Daniel Herschlag, Russ B. Altman. RNA: A publication of the RNA Society, 2009

310 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0acdcbe6bd1e6d67c6550e40ad2d7ef142323b39

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

3D RNA and Functional Interactions from Evolutionary Couplings. (2015, unknown): “Using coarse-grained molecular dynamics implemented in NAST (Butcher and Pyle, 2011; Jonikas et al., 2009) followed by simulated annealing with XPLOR (Schwieters et al., 2003), we predicted candidate all-atom structures (Figure 3A) for representatives from five selected RNA families (selection…”
Recent advances in RNA structurome (2022, methodology): “A group of coarse-grained methods, such as NAST (Jonikas et al., 2009), iFold (Sharma et al., 2008), Vfold (Xu et al., 2014), have been used to model various RNA targets.”
SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction (2015, background): “The history of coarse-grained modeling of RNA is long and multifaceted (16–20), ranging from simple models us-ing one bead per nucleotide with varying levels of sophistication (21,22), two and three bead models (10,23 ) and sometimes additional beads (24–27).”
RNA tertiary structure modeling with BRiQ potential in CASP15 (2023, methodology): “…protein structures, a variety of approaches have been developed for predicting the RNA 3D structure, 7 including homology modeling, 8,9 fragment assembly, 10 – 16 and de novo prediction with coarse grained models 17 – 20 as well as deep learning methods, 21 – 26 following the success of AlphaFold2.”
New metrics for comparing and assessing discrepancies between RNA 3D structures and models. (2009, methodology): “The deformation index is calibrated by the interaction network fidelity, which considers base–base-stacking and base–base-pairing interactions within the target structure.”