|The Rosetta++ software suite focuses on the prediction and design of protein structures, protein folding mechanisms, and protein-protein interactions. The Rosetta codes have been repeatedly successful in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) competition as well as the CAPRI competition and have been modified to address additional aspects of protein design, docking and structure.|
- 3.3: /usr/local/rosetta3.3 (Jul 2011).
- 3.2: /usr/local/rosetta3.2 (Jan 2011).
- 3.1: /usr/local/rosetta3.1 (Jul 2010).
- 3.0: /usr/local/rosetta3.0 (Mar 2009).
Rosetta 3.4 Documentation
To run a set of demos, do the following:
[biowulf]$ qsub -I -l nodes=1 [p2]$ mkdir rosetta_demos [p2]$ cd rosetta_demos [p2]$ tar xzvf /usr/local/rosetta3.4/helix_demos_3.4.tgz [p2]$ ./run_demos.sh
The script run_demos.sh will run through common protocols, each taking no more than a few minutes to complete. The input and output will give a good idea on how to use Rosetta 3.x.
Rosetta v3.4 contains the same core libraries and algorithms as the version 2 series, but is significantly streamlined and simplified. The two major differences are:
- There are multiple executables to perform the different functionalities, rather than one monolithic executable
- Options and input can be encapsulated in a single 'flag' file, rather than given on the command line and in the paths.txt file
The shared binary directory is /usr/local/rosetta3.4/bin. As with the version 2 series, it is best to add this directory to your path:
csh/tcsh:set path = ($path /usr/local/rosetta3.4/bin)bash:PATH=$PATH:/usr/local/rosetta3.4/bin
The main Rosetta v3.4 executables are:
Performs de novo protein structure predictionIdentifies low free energy sequences for target protein backbonesPredicts the structure of a protein-protein complex from the individual structures of the monomer componentsScore a structure with the Rosetta energy functionRelaxes a structure into a minimal energy stateBuild and score internal loops for homology modelling
In addition, there are protocols for:
- comparative modeling
- enzyme design
- flexible peptide docking
- membrane abinitio folding
- symmetric docking
Fragment files can be generated locally using the make_fragments.pl script. This will generate four secondary structure predictions using SAM, JUFO, Psipred, and Porter.
Fragment files can also be generated at the Robetta Server Site.
Supporting Programs and Scripts
Here are some supporting programs and scripts for streamlining certain tasks:
Manipulate input and output filesEvaluating Rosetta outputCluster decoys and models
Run as a batch job
Create a batch input file, e.g. 'rosettaRun':
#!/bin/bash #PBS -N rosetta #PBS -e rosetta.err #PBS -o rosetta.log cd $PBS_O_WORKDIR relax @flags > relax.log
Submit this job using the PBS 'qsub' command. Example:
qsub -l nodes=1 rosettaRun
See here for more information about PBS.
Run as a swarm
Create a swarmfile, e.g. 'rosettaSwarm':
AbinitioRelax @flags -out:file:silent abinito1.out > abinitio1.log AbinitioRelax @flags -out:file:silent abinito2.out > abinitio2.log AbinitioRelax @flags -out:file:silent abinito3.out > abinitio3.log AbinitioRelax @flags -out:file:silent abinito4.out > abinitio4.log AbinitioRelax @flags -out:file:silent abinito5.out > abinitio5.log AbinitioRelax @flags -out:file:silent abinito6.out > abinitio6.log AbinitioRelax @flags -out:file:silent abinito7.out > abinitio7.log AbinitioRelax @flags -out:file:silent abinito8.out > abinitio8.log
Submit this job using the 'swarm' command. Example:
swarm -f rosettaSwarm
See here for more information about using swarm.
cat_silent.pl: concatenate silentfiles
changeChain.pl: change the chain id of a PDB
compose_score_silent.py: generate a silentfile from a set of PDBs
createLoop.pl: create a dummy structure from a sequence of amino acids
createTemplate.pl: create a homology model template from a FASTA file and a homologous structure
make_coords_file.py: generate .coords format from native pdb (for input to cluster_info_silent.out, see below)
molecule.exe: generate JUFO file and rename ligand atoms (with addhydrogens.inp, mdl2rosetta.inp, and pdb2mdl.inp)
pdb_fasta.pl: generate a FASTA from a PDB
pdb2tag.pl: rename a PDB or set of PDBs to their tag names (as shown in the silentfile)
reconstruct_PDB_by_index: generate PDBs from abinitio-format silentfile
renumberPDBandchains.pl: renumber the residues of a PDB sequentially, starting at 1
renumberPDBatoms.pl: renumber the atoms of a PDB sequentially, starting at 1
silentDock2pdb.pl: generate PDBs from a docking silentfile
TMalign: aligns structures based on CA-CA distances
VMD: X-Windows molecular graphics viewer
getColumn.pl: display silentfile and scorefile columns
gnuplot: graphically display data
cluster_plot.pl: generate a gnuplot input file to plot the score versus another field
histogram.pl: generate a quick histogram from STDIN data
cluster.pl: automatic clustering of an abinitio-format silentfile
cluster_info_silent.out: fully configurable silentfile clustering
cluster_pdbs.pl: cluster a set of PDBs
cluster_variation.pl: find per-residue variation within a cluster
make_color_trees.py: make a dendrogram of the clusters
make_new_plot.py: make a contacts plot