PT  - JOURNAL ARTICLE
AU  - Andrew M. Watkins
AU  - Rhiju Das
TI  - FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds
AID  - 10.1101/764449
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 764449
4099  - http://biorxiv.org/content/early/2019/09/10/764449.1.short
4100  - http://biorxiv.org/content/early/2019/09/10/764449.1.full
AB  - Methods to predict RNA 3D structures from sequence are needed to understand the exploding number of RNA molecules being discovered across biology. As assessed during community-wide RNA-Puzzles trials, Rosetta’s Fragment Assembly of RNA with Full-Atom Refinement (FARFAR) enables accurate prediction of complex folds, but it remains unclear how much human intervention and experimental guidance is needed to achieve this performance. Here, we present FARFAR2, a protocol integrating recent innovations with updated RNA fragment libraries and helix modeling. In 16 of 21 RNA-Puzzles revisited without experimental data or expert intervention, FARFAR2 recovers structures that are more accurate than the original models submitted by our group and other participants during the RNA-Puzzles trials. In five prospective tests, pre-registered FARFAR2 models for riboswitches and adenovirus VA-I achieved 3–8 Å RMSD accuracies. Finally, we present a server and three large model archives (FARFAR2-Classics, FARFAR2-Motifs, and FARFAR2-Puzzles) to guide future applications and advances.