FARFAR2 Server Documentation





Overview:

FARFAR2 is a tool for building a 'full model' of a medium-size noncoding RNA. The goal of FARFAR2 is to provide a flexible and extensible fragment assembly protocol, capable of stitching together multiple subsections of an RNA structure or building a model entirely fron scratch. We have exposed options sufficient to allow the user to reproduce any benchmark case run in Watkins and Das, 2019, with helix flexibility provided in a kinematically realistic way using libraries of base pair steps.

Inputs to 'FARFAR2' may include entire sections of RNA structure, individual helices, or just a fasta file, though ideally some secondary structure (in dot-bracket notation) is available. A portion of the native structure may be provided as a basis for approximate alignment, particularly useful in homology modeling. The native structure may be provided as well to gauge native RMSD.


Tips:

  1. You don't need to provide the secondary structure for any region of RNA for which you're also providing an input PDB.

  2. BLAST versus the PDB to find homologous RNAs. For example, there are many tandem glycine riboswitches that can be built from portions of 3p49 (most of one found in F. nucleatum).



Please cite the following article when referring to results from our ROSIE server:

  1. Watkins, A. M.; Das, R. "An automated and customizable RNA fragment assembly protocol in Rosetta. bioRxiv 223305; doi: https://doi.org/10.1101/223305

  2. Lyskov S, Chou FC, Conchúir SÓ, Der BS, Drew K, Kuroda D, Xu J, Weitzner BD, Renfrew PD, Sripakdeevong P, Borgo B, Havranek JJ, Kuhlman B, Kortemme T, Bonneau R, Gray JJ, Das R., "Serverification of Molecular Modeling Applications: The Rosetta Online Server That Includes Everyone (ROSIE)". PLoS One. 2013 May 22;8(5):e63906. doi: 10.1371/journal.pone.0063906. Print 2013. Link

We welcome scientific and technical comments on our server. For support please contact us at Rosetta Forums with any comments, questions or concerns.


Modeling tools developed by the Das Lab at Stanford University. The Rosie implementation was developed by Andrew Watkins and Sergey Lyskov.