Relax API

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.

Command Line Interface

Examples

Create 10 new relaxed models of input.pdb:

lev engine submit relax input.pdb --repeats 10

Flags

  • --pdb-file (str) (Required)
    • Input PDB file
    • Do not include nonprotein residues.
    • Do not include multimodel (NMR-sourced) PDBs.
    • You can specify this flag as the first positional argument or with the --pdb-file flag
  • --repeats (int) (Default: 1)
    • The number of different relaxed conformations the loop modeling API will return.

Python Interface

Examples

Create 10 new relaxed models of input.pdb:

from engine import EngineClient

client = EngineClient()
client.authorize()

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

Flags

  • pdb_path (str) (Required)
    • Input PDB file
  • repeats (int) (Default: 1)
    • The number of different relaxed conformations the loop modeling API will return.

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.

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