publication used Rosetta version d9cb8b9f3db54455ea0f200b5c55ee18679ee03a 2013-10-14 01:05:39 -0700 from git@github.com:RosettaCommons/main.git

this demo is updated with current rosetta file paths so as to work properly with the next public release

- Each design simulation is basically 4 steps.
- Steps 1-3 have already been performed for you, but are described here for completeness.
- Step 4 is the actual protein design simulation. You already have all the files you need to run it.
- "PDB4" must be replaced with the 4-letter pdb code for the structure in each directory
- We're assuming you have set an alias for "rosetta_scripts" pointing to your rosetta_scripts executable
- We're assuming you have set an alias for "relax" pointing to your relax executable

1. Pre-relax the structure
- all structures downloaded from the PDB were first relaxed with sidechain constraints using the relax application as follows
>> relax -s PDB4.pdb @prelax.args
the pre-relaxed structure has been provided as *.prelax.pdb

2. Predict epitopes in native sequence
>> rosetta_scripts -s PDB4.prelax.pdb @args -parser:protocol:nmersvm_table.xml
 - here, PDB4 is the name of the structure file, usually a 4-letter pdb code

3. generate a resfile defining designable residues from predicted epitopes in nmer_svm_table
>> nmersvmtable2resfile.py nmer_svm_table.PDB4.prelax_0001.tab CUTOFF CHAIN > resfile
 - the python script used above is included in the top directory
 - call it with no arguments to see the input argument order
 - the cutoff can be entered as rosetta score or in nM binding constant, e.g. you could enter "0.425" or "500nm" to get the same results

4. run a protein design simulation
>> rosetta_scripts -s PDB4.prelax.pdb @args -parser:protocol deimm.xml -suffix .deimm
 - depending on the simulation and your computer, this may take up to a few hours, particularly with large allele sets or long proteins. Note that the GreedyOptMutationMover can be parallelized with openmpi to speed the calculation. See Rosetta Scripts documentation wiki for details.

!!!!NOTES about large_scale_benchmarking directory designs
 - for the large_scale_benchmarking designs, there are two different xml files, svm_deimm.xml and natseq_deimm.xml, use one of these oinstead of "deimm.xml"
 - for the large_scale_benchmarking designs, the weight on the score being calibrated is a rosetta scripts variable named "wt"
 - to set this rosetta scripts variable to (for instance) 1.0, you must add the flag "-parser:script_vars wt=1.0"

