MARS-RNA: An Automated Pipeline for Correlating RNA Molecular Dynamics Trajectories with Chemical Probing Data

Abstract Ribonucleic acid (RNA) molecules fold into complex, highly dynamic three-dimensional conformational structures that govern their biological activities. Chemical probing methods, such as SHAPE (selective 2′-hydroxyl acylation analyzed by primer extension), provide structural insights in the form of one-dimensional (1D) nucleotide reactivity profiles. However, mapping these one-dimensional profiles back to the underlying dynamic structures remains a key challenge, as traditional analyses rely on a static view based on three-dimensional (3D) high-resolution structures from X-ray crystallography or nuclear magnetic resonance, rather than accounting for the dynamic nature of the system. In this Application Note, we introduce MARS-RNA (Molecular dynamics Analysis for Reactivity and Structure), an automated pipeline that extracts and integrates multiscale structural and geometric features from RNA Molecular Dynamics (MD) trajectories, combining them with secondary (2D) and 3D annotations from X3DNA-DSSR to identify features that correlate with experimental reactivity. MARS-RNA processes raw GROMACS trajectories, performs frame-by-frame structural annotation, calculates spatial centroids and physical distance fluctuations, computes Pearson/Spearman correlation coefficients against SHAPE data, and preprocesses results for machine learning pipelines. We show its value on the RNA 3′-UTR (PDB ID: 1AUD, chain A), by highlighting several single correlation with SHAPE reactivity.