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Code and Demo

The membrane ddG application is packaged with PyRosetta. The released version cab be found in: PyRosetta/app/membrane/compute_ddG.py.

The developmental version can be found in the Rosetta source code in source/src/python/bindings/app/membrane/compute_ddG.py

A demo for this application can be found in Rosetta/demos/protocol_capture/mp_ddG

Rosetta Revision #58096

Background

Measuring free energy changes upon mutation can inform our understanding of membrane protein stability and variation and is a step toward design. In this application, we predict ddGs by measuring the difference in Rosetta energy for the native and mutated conformation. This application uses a modified version of the all atom energy function for membrane proteins, which includes the fa_elec term and pH energy (see below). The Membrane ddG application is part of the RosettaMP Framework.

Algorithm Description

The Membrane ddG application predicts the ddG by taking the difference in Rosetta energy between the native and mutated conformations. Several variations of this protocol are available:

  • Default: Predict the ddG, repacking only at the mutated position
  • Include Repacking: Predict the ddG, repacking residues within a given radius of the mutated position (recommended radius is 8Å by Kellogg et al. 2011)
  • Include pH Calculations: Use a modified version of the Rosetta energy function that corrects for system pH and electrostatics. This option will load in an additional set of rotamers The default pH is 7.0. Additional information on pH calculations can be found in Kilambi et al. 2013 (ref below).

Options

The following options can be used to adjust settings for ∆∆G predictions

General options

Flag Short Flag Description Type
--in_pdb -p Input PDB file String
--in_span -s Input spanfile (transmembrane spanning regions of the protein) String
--out -o Output filename for ddG data. ddG predictions referenced by pose numbering. Default: ddG.out String
--res -r Pose residue number to mutate Int
--mut -m One-letter code of residue identity of the mutant. Example: A181F would be 'F' Char
--output_breakdown -b Output ddG score breakdown by weighted energy term into a scorefile. Default: score.sc String

Repacking

Flag Short Flag Description Type
--repack_radius -a Repack the residues within this radius (in Å). Default value is 0Å Real

pH Options

Flag Short Flag Description Type
--include_pH -t Include pH energy terms: e_pH and fa_elec. Default value is false. Bool
--pH_value -v pH Value at which to predict ddGs. Default value is pH 7. Must pass -include_pH first Real

Sample Command Lines

Below is a sample commandline using inputs provided in the 2015 MPddG protocol Capture. In this command, all residues are repacked within 8.0Å of the mutated position and calculations are performed at pH 4: 

./compute_ddG.py --in_pdb inputs/1qd6_tr.pdb --in_span inputs/1qd6_tr_C.span --res 181 --repack_radius 8.0 --include_pH true --pH_value 4.0

The columns in the output file are the following:

  1. input PDB file
  2. residue number in pose numbering (should start from 1 in your PDB file)
  3. amino acid one-letter code of the mutation
  4. Rosetta score of the native. A more negative number is more stable.
  5. Rosetta score of the mutant.
  6. ddG value as the difference between the native and mutant score. Apparently and when I am looking at those numbers, the ddG is computed as native minus mutant where a negative number mean

References

  1. Alford RF, Koehler Leman J, Weitzner BD, Duran A, Tiley DC, Gray JJ (2015). An integrated framework advancing membrane protein modeling and design. PLoS Comput. Biol. - In Press

  2. Chaudhury S, Lyskov S, Gray JJ (2010) PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta.

  3. Moon CP, Fleming KG (2011) Side-chain hydrophobicity scale derived from transmembrane protein folding into lipid bilayers. Proc Natl Acad Sci.

  4. Kellogg, Elizabeth H., Leaver-Fay A, and Baker D. “Role of Conformational Sampling in Computing Mutation-Induced Changes in Protein Structure and Stability.” Proteins 79, no. 3 (March 2011): 830–38. doi:10.1002/prot.22921.

  5. Kilambi, KP, and Gray JJ. “Rapid Calculation of Protein pKa Values Using Rosetta.” Biophysical Journal 103, no. 3 (August 8, 2012): 587–95. doi:10.1016/j.bpj.2012.06.044.

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