Propagating the same mutation over “different” chains

[Anonymous]

I’m really new to Rosetta, and I don’t know very much about coding or script writing.  I am trying to do a point mutation on a protein that forms a viral capsid, but because of the way the protein assembles, I have three identical protein chains forming a trimer that are named A.B. and C.  They have very slightly different conformations but the same sequence, and they assemble in a specific way.  I’m trying to see how a point mutation affects assembly, possibly by looking at changes in the Rosetta score.  Anyway, the first step is to have the same mutation occur on all three chains, and scan for improvements in energy score, but I’m not sure how to do that in pmut_scan.  I can only do mutations on a single monomer that isn’t part of an actual structure.  Is there a way to have the same mutation appear in all three chains?

Thank you!

Marla

[Anonymous]

It depends a bit on what you’re interested for the downstream application, but if you’re interested in assessing the mutational stability of the mutations, I might recommend using the cartesian_ddg application: https://www.rosettacommons.org/docs/latest/cartesian-ddG

This allows input of a mutfile format which allows you to specifiy multiple simultaneous mutations. Now, you actually have only a single mutation, but you can just get Rosetta to treat it effectively as three point mutations, one for each monomer you’re modeling. The only tricky bit with the format is that it expects the residue numbers in Rosetta or Pose numbering, which starts at 1 for the first amino acid, and increases consecutively ignoring chains. So you’ll have to do a bit of math to figure out the numbers for the second and third chain.

 

[Anonymous]

Thank you, rmoretti.  I am mostly interested in identifying mutations that are “tolerated”–they don’t need to improve the energy score significantly, they just need to be close enough to the score of the original to have a good chance of being stable and not changing the conformation significantly.  The idea is to see where mutations are best tolerated for engineering the protein to whatever purpose you like, while still having the capsid form.  I want to identify all of the locations where a mutation (and the identity of that mutation) could probably be added successfully. 

[Anonymous]

Sounds like cartesian_ddg should work for you.  The correlation with the absoulte energy values can sometimes be spotty. (So you can’t necessary tell mutations which destabilize by 3 kcal/mol versus 4 kcal/mol apart). But it should be decent for doing three category (stabilizing/neutral/destabilizing) classification. You can read the reference to get more details about thresholds, but if your calculated ddG is close to zero, it’s a decent chance that the mutation is neutral/tolerated.

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