autoDRRAFTER Server Documentation
Overview:
This webserver runs "step 4" described in the autoDRRAFTER documentation, i.e., the computationally demanding stage where you submit many CPU-intensive jobs to generate an ensemble of model structures. It does not automate the relatively quick, single CPU steps of job setup and inter-round processing.
The initial job set-up for autoDRRAFTER should be run locally, for three reasons. First, some of its dependencies (like EMAN2) cannot be installed on the ROSIE backend. Second, the set-up script is really fast -- it helps determine initial helix positions -- and its output should be inspected manually before computational resources are devoted to a DRRAFTER round. Finally, advanced users familiar with autoDRRAFTER may wish to try multiple possible "fits" -- correspondences between helices and parts of the density map -- and so each setup run may correspond to multiple DRRAFTER runs.
If you want to use the Python setup script, it will help automatically determine initial helix placements. If you already have a good idea of where a helix (or other piece of your RNA structure) sits in the density map, you can skip running the setup script and instead set the job up manually.
Run local job setup via a command like:
auto-DRRAFTER_setup.py -map_thr 30 -full_dens_map map.mrc -full_dens_map_reso 10.0 -fasta fasta.txt -secstruct secstruct.txt -out_pref example -rosetta_directory ~/src/Rosetta/main/source/bin/ -repeats 1 -nstruct_per_job 10 -cycles 200 -fit_only_one_helixFull documentation for auto-DRRAFTER_setup.py may be found in the Rosetta documentation.
The script will produce PDB files for all RNA helices, with one aligned to the density map, a fasta file, a flags file, and a secondary structure file. Supply these, as well as your map, to this server, which will run just one round of autoDRRAFTER. Then, you can use the link "Full set of decoy structures created on this run" and download the resulting silent file (extension *.out). You will use this silent file to set up the next round of autoDRRAFTER using auto-DRRAFTER_setup_next_round.py.
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.