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- W3132938587 abstract "Ribonucleic acids (RNAs), as one of the most versatile and important molecules in molecular biology, are associated with an extensive range of cellular functions. Folding into secondary and more often tertiary structures is essential for the functional activity of RNAs such as ribozymes. These functional structures of negatively charged RNAs call for a neutralizing ion-atmosphere and are frequently stabilized by metal ions, especially divalent cations. However, the role of metal ions in the folding dynamics of ribozymes has not been fully understood and requires an atomistic resolution analysis. Molecular dynamics (MD) combined with enhanced sampling methods have been used to conduct such analysis; however, usage of relatively simple reaction coordinates has been a drawback to sample the complex energy landscape of these biomolecular systems. This study investigates the structural dynamics of RNAs in the presence of Mg2+ and monovalent ions. In order to do so, machine learning (ML)-based enhanced sampling methods were integrated with MD and oscillating chemical potential Grand Canonical Monte Carlo (GCMC) simulations. The ML-based sampling methods such as ‘Automatic Mutual Information Noise Omission’ (AMINO) and 'Reweighted auto-encoded variational Bayes for enhanced sampling’ (RAVE)were used to accelerate the sampling of unfolding/folding configurations by iteratively improving the reaction coordinates, and GCMC method was used to enhance the sampling of metal ions in the system. We will implement this for RNAs of varying sizes and will evaluate the efficiency of our new approach. Preliminary simulations show promising results suggesting that application of our sampling framework to different RNA structures to understand ion-driven folding can help uncover deeper insights in RNA folding pathways." @default.
- W3132938587 created "2021-03-01" @default.
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- W3132938587 date "2021-02-01" @default.
- W3132938587 modified "2023-09-26" @default.
- W3132938587 title "Enhanced Sampling of RNA Folding/Unfolding Configurations using Machine Learning/Grand Canonical Monte Carlo" @default.
- W3132938587 doi "https://doi.org/10.1016/j.bpj.2020.11.1340" @default.
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