pull_into_place 1.2.2

A rosetta pipeline to position important protein sidechains with sub-angstrom accuracy.

Pull Into Place (PIP) is a protocol to design protein functional groups with sub-angstrom accuracy. The protocol is based on two ideas: 1) using restraints to define the geometry you’re trying to design and 2) using an unrestrained simulations to test designs.

The design pipeline orchestrated by PIP has the following steps:

1. Define your project. This entails creating an input PDB file and preparing it for use with rosetta, creating a restraints file that specifies your desired geometry, creating a resfile that specifies which residues are allowed to design, and creating a loop file that specifies where backbone flexibility will be considered:

   $ pull_into_place 01_setup_workspace ...
   $ pull_into_place 02_setup_model_fragments ...

2. Build a large number of models that plausibly support your desired geometry by running flexible backbone Monte Carlo simulations restrained to stay near said geometry. The goal is to strike a balance between finding models that are realistic and finding models that satisfy your restraints:

   $ pull_into_place 03_build_models ...

3. Filter out models that don’t meet your quality criteria:

   $ pull_into_place 04_pick_models_to_design ...

4. Generate a number of designs for each model remaining:

   $ pull_into_place 05_design_models ...

5. Pick a small number of designs to validate. Typically I generate 100,000 designs and can only validate 50-100. I’ve found that randomly picking designs according to the Boltzmann weight of their rosetta score gives a nice mix of designs that are good but not too homogeneous:

   $ pull_into_place 06_pick_designs_to_validate ...

6. Validate the designs using unrestrained Monte Carlo simulations. Designs that are “successful” will have a funnel on the left side of their score vs rmsd plots:

   $ pull_into_place 07_setup_design_fragments ...
   $ pull_into_place 08_validate_designs ...

7. Optionally take the decoys with the best geometry from the validation run (even if they didn’t score well) and feed them back into step 4. Second and third rounds of simulation usually produce much better results than the first, because the models being designed are more realistic. Additional rounds of simulation give diminishing returns, and may be more effected by some of rosetta’s pathologies (i.e. it’s preference for aromatic residues):

   $ pull_into_place 04_pick_models_to_design ...
   $ pull_into_place 05_design_models ...
   $ pull_into_place 06_pick_designs_to_validate ...
   $ pull_into_place 07_setup_design_fragments ...
   $ pull_into_place 08_validate_designs ...

8. Generate a report summarizing a variety of quality metrics for each design. This report is meant to help you pick designs to test experimentally:

   $ pull_into_place 09_compare_best_designs ...