Simulated Landscape

Generate in silico a mutational landscape of a protein



By using the Rosetta application pmut_scan one can obtain a mutational landscape of the ∆G for every possible mutation at each position.
In the case of enzymes, by repeating the same operation on the enzyme without substrate docked, with substrate docked, with transition state and with product one can find which residues are increase the reactivity.
This tool gets the output of different point-mutation scans, aligns them into a single table and plots them. A few cases will fail entirely, hence why the datasets are aligned by the tool.
As the energies are relative to wild type, it does not matter if in the unbound state the ligand is not in the system or that in the transition state has a strong constraint.
The quality of the results depends on the model, so it is important the unconstrained scores form a typical Gibbs free energy curve, with negative ∆Grxn and positive ∆G.
Due to the fact that preparing the models cannot be automated without affecting the results, these are not done here.
If unfamiliar with Rosetta, feel free to contact Matteo Ferla (author), who might be able to parameterise your ligands, prepare your models and run them for you.


The input files for this tool are the outputs from a pmut_scan run launched along with relevant constraints and params files thusly:

pmut_scan_parallel -ex1 -ex1aro -ex2 -extrachi_cutoff 1 -DDG_cutoff 999 -mute basic core -s your_structure.pdb > scores.txt
The -DDG_cutoff 999 flag is high because we want all mutants. Actually, despite the memory, adding the -output_mutant_structures true flag is also rather handy for the analysis.
Provide one or more files (do not worry about header rows; the PDB numbering will be used. No negative positions):

AA oder