The Relax API runs the Rosetta FastRelax protocol. This is useful for taking a structure not generated by Rosetta and “relaxing” it into the Rosetta scorefunction preparatory to further modeling, or for generating a backbone ensemble for a further modeling experiment.

1 Quickstart
- 1.1 Command Line Examples
- 1.2 Python Examples
2 Inputs
3 Options
4 Outputs
5 Notes
- 5.1 Rosetta FastRelax
- 5.2 Output File interpretation

Quickstart

Command Line Examples

Create 10 new relaxed models of input.pdb:

cyrus engine submit relax input.pdb --repeats 10

Python Examples

Create 10 new relaxed models of input.pdb:

from engine.relax.client import RelaxClient

client = RelaxClient()
job_id = client.submit(pdb_path="input.pdb", repeats=10)

Inputs

--pdb-file
- Input PDB file
- CLI argument: --pdb-file input.pdb
- Python submit() argument: pdb-file=”input.pdb”
- Do not include nonprotein residues.
- Do not include multimodel (NMR-sourced) PDBs.

Options

--repeats
- The number of different relaxed conformations the loop modeling API will return.
- CLI argument: --repeats 5
- Python submit() argument: repeats=5
- default = 1

Outputs

models (directory)
- PDB files with relaxed structures. The models will be in the form input_####.pdb, where #### is an index (0001, increasing).
- Note that Rosetta will renumber the residues monotonically starting from 1, contiguous across chains.
score.sc
- A text file containing the Rosetta scores for each generated model.

Notes

Rosetta FastRelax

Quoting from the RosettaCommons documentation:

“This finds low-energy backbone and side-chain conformations near a starting conformations by performing many rounds of packing and minimizing, with the repulsive weight in the scoring function gradually increased from a very low value to the normal value from one round to the next.”

Output File interpretation

Results can be downloaded if and only if a job has succeeded – DONE state

Model quality can be assessed via scores. Broad documentation on interpreting scores can be found here. The score.sc file is a space-delimited data table, padded for easier reading. It can be parsed with pandas dataframes, excel, or your tool of choice. In the score.sc file, pay particular attention to the total_score column. The total_score is Rosetta’s overall grade for a model. The total_score should have a negative value for good models, although this will depend enormously on the quality of the input model.

Usually you should sort your models by total_score, look at the lowest-scoring 5 or so, pick the one your biophysical intuition says is best, and proceed with it for whatever your further experiment is.