CHARACTERIZING THE ENERGY LANDSCAPE AND THE FOLDING TRANSITION ENSEMBLES OF THE GCAA RNA TETRALOOP.

Molecular dynamics (MD) simulations have proven to be a powerful technique for investigating the intricate folding and unfolding events of biomolecules at the atomic level. While some proteins can rapidly fold following a funnel-shaped energy landscape, other proteins and RNAs may present a frustrated, rough energy landscape. Describing these complex landscapes using a limited number of reaction coordinates remain a significant challenge. In this study, we explore the folding landscape of the RNA tetraloop gcGCAAgc – a highly stable stem-loop structural motif with critical roles in RNA folding, stability and function. To address this question, we used classical Molecular Dynamics simulations to generate extensive folding trajectories (~120 microseconds), and the Energy Landscape Visualization Method (ELViM) to provide a comprehensive visualization of the conformational space, which enables clear differentiation between folded, unfolded, and near-folded states. Remarkably, the ELViM projection also allows the identification of four distinct transition state regions that establish folding pathways bridging the unfolded and folded basin. Our analysis further characterizes the representative structures and transition states in terms of base pairing and stacking interactions. Finally, we demonstrate that the ELViM projection has an excellent clustering capability and provides details beyond the usual two-dimensional landscape based on reaction coordinates. This detailed analysis of the tetraloop's folding process carries important implications for understanding RNA folding, and shows that the ELViM approach may be useful in the study of larger RNAs and other biomolecules.

This work was supported by São Paulo State Research Foundation (FAPESP) and by the National Council for Scientific and Technological Development (CNPq).