The scripts and input files that accompany this demo can be found in the demos/public directory of the Rosetta weekly releases.


Written by Lei Shi. Ray Wang drafted the previous version.

We will use the chemical shifts to improve the fragments from which Rosetta builds up structures, and the NOEs to guide the Rosetta calculations towards the native structure.

Please see references at:

  • Rosetta abinitio: Bradley, P et al Science 2005
  • Chemical shift fragments: Shen Y et al. PNAS 2008;105:4685-4690
  • Chemical shift+NOE+RDC: Raman S, et al Science 2010

In this demo, we will use PDB 2JY7, which is a small protein (for demo purpose) and has experimental data deposited. Several scripts are provided in the scripts folder for formatting purposes:

If you are from David Baker lab, there are scripts available to make setup easier without going through public servers. The following instructions should work just fine without having direct access to any Baker lab cluster.

Running the Demo

  1. Create following folders:

    mkdir starting_inputs
    mkdir rosetta_inputs
    mkdir rosetta_inputs/talos_output
    mkdir rosetta_inputs/pick_cs_fragments
  2. Download protein fasta and experimental data:
    Download fasta from

    wget -O starting_inputs/t000_.fasta
    Download chemical shift data from
    wget -O starting_inputs/raw.cs.bmrb
  3. Format data for Rosetta use:
    Formmatting chemical shift data for TALOS. The required bmrb2talos script is part of the Rosetta toolbox, downloadable from The CS Rosetta web site. The output file is provided here.

    $ bmrb2talos starting_inputs/raw.cs.bmrb > rosetta_inputs/cs.talos
  4. Generating talos predictions using using rosetta_inputs/cs.talos. Save/copy and to rosetta_inputs/talos_output

  5. Generate fragment/profile from RobettaServer using starting_inputs/t000_.fasta. Save/copy t000_.checkpoint to rosetta_inputs/

  6. Pick fragments using secondary structure profile and chemical shift data: (where $ROSETTA3=path-to-Rosetta/main/source and $ROSETTA3_DB=path-to-database)

    $> $ROSETTA3/bin/fragment_picker.default.linuxgccrelease -in::file::vall $ROSETTA3_DB/sampling/small.vall.gz -frags::n_frags 200 -frags::frag_sizes 3 9 -frags::sigmoid_cs_A 2 -frags::sigmoid_cs_B 4 -out::file::frag_prefix rosetta_inputs/pick_cs_fragments/frags.score -frags::describe_fragments rosetta_inputs/pick_cs_fragments/frags.fsc.score -frags::scoring::config scripts/scores.score.cfg -in:file:fasta starting_inputs/t000_.fasta -in:file:checkpoint rosetta_inputs/t000_.checkpoint -in:file:talos_cs rosetta_inputs/cs.talos -frags::ss_pred rosetta_inputs/talos_output/ talos -in::file::talos_phi_psi rosetta_inputs/talos_output/
    The small.vall.gz used here for fragment picking is only used to speed up the demo. You have to change this to the vall database on your system!
  7. Run Rosetta with the fragments chemical shift fragments:

    $> $ROSETTA3/bin/AbinitioRelax.default.linuxgccrelease -in:file:fasta starting_inputs/t000_.fasta -file:frag3 rosetta_inputs/pick_cs_fragments/frags.score.200.3mers -file:frag9 rosetta_inputs/pick_cs_fragments/frags.score.200.9mers -nstruct 1 -abinitio::increase_cycles 0.5 -abinitio::relax -score::weights score13_env_hb -abinitio::rg_reweight 0.5 -abinitio::rsd_wt_helix 0.5 -abinitio::rsd_wt_loop 0.5 -disable_co_filter true -out:file:silent csrosetta.out


  • Change nstruct to generate desired number of models. Larger is better depending on your available computer time, etc.
  • change -abinitio::increase_cycles 0.5 to 10! (0.5 is chosen for tesing purposes only)

Processing the output

  1. Extract the low energy models:

    grep SCORE csrosetta.out | sort –nk2 | head
    The second column contains the energies of the lowest energy 10 models. Select as the cutoff the energy on the last line.
  2. This script: csrosetta.out “score < cutoff”
    will produce which contains the lowest models below cutoff.
  3. Extract pdbs from selected silent file

    $> $ROSETTA3/bin/extract_pdbs.default.linuxgccrelease -in::file::silent
  4. Check convergence by superimposing the ten low energy models in pymol or your favorite molecular graphics

  5. Check convergence by clustering the lowest energy models (see clustering demo for instructions)