Studying the dynamical properties of small RNA molecules with computational techniques

The role of ribonucleic acid (RNA) in molecular biology is shifting from a mere messenger between DNA (deoxyribonucleic acid) and proteins to an important player in many cellular activities. The central role of RNA molecules calls for a precise characterization of their structural and dynamical properties. Nowadays, experiments can be efficiently complemented by computational approaches. This thesis deals with the study of the dynamical properties of small RNA molecules, exploiting various computational techniques. Specifically we investigate two different complementary methods, elastic network models (ENMs) and Markov state models (MSMs). ENMs are valuable and efficient tools for characterizing the collective internal dynamics of biomolecules. We evaluate their performance by comparing their predictions with the results of atomistic molecular dynamics (MD) simulations and selective 2’-hydroxyl analyzed by primer extension (SHAPE) experiments. We identify the optimal parameters that should be adopted when putting into use such models. MSMs are tools that allow to probe long-term molecular kinetics based on short-time MD simulations. We make use of MSMs and MD simulations to measure the kinetics and the timescale of the stacking-unstacking motion for a collection of short RNA oligonucleotides, comparing the results with previously published relaxation experiments. We then move to the study of the process of the fraying of the terminal base pair in a helix, characterizing the different involved pathways and the sequence dependence of the process timescale.