# All-at-once RNA folding with 3D motif prediction framed by evolutionary information

## TL;DR

This paper introduces a new method for predicting RNA 3D structures by combining evolutionary data with motif prediction in a single process.

## Contribution

The novel contribution is a probabilistic grammar, CaCoFold-R3D, that predicts RNA 3D motifs and secondary structure simultaneously using evolutionary information.

## Key findings

- CaCoFold-R3D reliably identifies canonical helices and non-Watson-Crick motifs using covariation in RNA alignments.
- The method can predict over fifty known RNA motifs in any non-helical loop region, including complex junctions.
- CaCoFold-R3D is shown to be a fast and customizable alternative for predicting RNA 3D structures.

## Abstract

Structural RNAs exhibit a vast array of recurrent short 3D elements involving non-Watson-Crick interactions that help arrange canonical double helices into tertiary structures. We present CaCoFold-R3D, a probabilistic grammar that predicts these RNA 3D motifs (also termed modules) jointly with RNA secondary structure over a sequence or alignment. CaCoFold-R3D uses evolutionary information present in an RNA alignment to reliably identify canonical helices (including pseudoknots) by covariation. We further introduce the R3D grammars, which also exploit helix covariation that constrains the positioning of the mostly non-covarying RNA 3D motifs. Our method runs predictions over an almost-exhaustive list of over fifty known RNA motifs (everything). Motifs can appear in any non-helical loop region (including 3-way, 4-way and higher junctions) (everywhere). All structural motifs as well as the canonical helices are arranged into one single structure predicted by one single joint probabilistic grammar (all-at-once). Our results demonstrate that CaCoFold-R3D is a valid alternative for predicting the all-residue interactions present in a RNA 3D structure. Furthermore, CaCoFold-R3D is fast and easily customizable for novel motif discovery.