The membrane ddG application is packaged with PyRosetta. The released version cab be found in:
The developmental version can be found in the Rosetta source code in
A demo for this application can be found in
Rosetta Revision #58096
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.
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:
The following options can be used to adjust settings for ∆∆G predictions
|--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|
|--repack_radius||-a||Repack the residues within this radius (in Å). Default value is 0Å||Real|
|--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|
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:
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
Chaudhury S, Lyskov S, Gray JJ (2010) PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta.
Moon CP, Fleming KG (2011) Side-chain hydrophobicity scale derived from transmembrane protein folding into lipid bilayers. Proc Natl Acad Sci.
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.
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.
Questions and comments to: