Computational modelling of the 3D structure of RNA - small molecule ligand complexes

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BACKGROUND: Ribonucleic Acids (RNAs) are essential molecules found in all forms of life. Their role ranges from carrier of the genetic information (i.e. messenger RNA that are translated into proteins) to regulator of gene expression (e.g. micro RNA) or catalyzer of biochemical reactions (e.g. ribozymes). To date, we estimate that up to 97% of the RNA transcribed in cells is not used to encode proteins. Often, these non-coding RNAs (ncRNAs) use sophisticated structures to fulfil their non-coding functions. RNA structures are hierarchically organized and can be described at multiple levels. First, the secondary structure is the set of all canonical base-pairing interactions between Adenine (A) and Uracil (U), Guanine (G) and Cytosine (C), or Guanine and Uracil bases. Though, other types of base pairs are also found in unpaired regions of the secondary structure, where they form complex networks used to stabilize the 3D structures of these local motifs. We call a 2.5D structure the collection of stems built from stacks of canonical base pairs and connected by local 3D motifs. This 2.5D structure forms the scaffold of the complete 3D structure of the molecule that we denote as the tertiary structure. The structure but also the dynamics of the folding are important properties used to control molecular functions. In particular, another molecule (e.g. protein, RNA, nucleotide) binds the target RNA to change its conformation and activate its molecular function. Recent studies identified small molecules as important non-covalent regulators of RNA function in many cellular pathways. These discoveries contribute to a better understanding of molecular mechanisms regulating biological systems, but also pose RNA molecules as a large class of promising novel drug targets, with applications including novel antibiotics, antivirals, and CRISPR activators. CONTRIBUTION: This proposal aims to develop algorithms for predicting RNA 2.5D and 3D structures and leverage this information to predict small molecules binding RNAs. These met