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• W4381854927 endingPage "3098" @default.
• W4381854927 startingPage "3089" @default.
• W4381854927 abstract "Atomically detailed simulations of RNA folding have proven very challenging in view of the difficulties of developing realistic force fields and the intrinsic computational complexity of sampling rare conformational transitions. As a step forward in tackling these issues, we extend to RNA an enhanced path-sampling method previously successfully applied to proteins. In this scheme, the information about the RNA's native structure is harnessed by a soft history-dependent biasing force promoting the generation of productive folding trajectories in an all-atom force field with explicit solvent. A rigorous variational principle is then applied to minimize the effect of the bias. Here, we report on an application of this method to RNA molecules from 20 to 47 nucleotides long and increasing topological complexity. By comparison with analog simulations performed on small proteins with similar size and architecture, we show that the RNA folding landscape is significantly more frustrated, even for relatively small chains with a simple topology. The predicted RNA folding mechanisms are found to be consistent with the available experiments and some of the existing coarse-grained models. Due to its computational performance, this scheme provides a promising platform to efficiently gather atomistic RNA folding trajectories, thus retain the information about the chemical composition of the sequence." @default.
• W4381854927 created "2023-06-25" @default.
• W4381854927 creator A5037610067 @default.
• W4381854927 creator A5060988001 @default.
• W4381854927 creator A5062848902 @default.
• W4381854927 creator A5083284785 @default.
• W4381854927 date "2023-08-01" @default.
• W4381854927 modified "2023-10-18" @default.
• W4381854927 title "RNA folding pathways from all-atom simulations with a variationally improved history-dependent bias" @default.
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• W4381854927 doi "https://doi.org/10.1016/j.bpj.2023.06.012" @default.
• W4381854927 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37355771" @default.
• W4381854927 hasPublicationYear "2023" @default.
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