- This topic has 4 replies, 2 voices, and was last updated 9 years, 6 months ago by Anonymous.
May 20, 2014 at 9:37 am #1895Anonymous
I am a new Rosetta (3.5) user and I would like to study the effects of some point mutations on a monomeric transmembrane protein.
Can ddg_monomer properly predict the change in stability induced by point mutations in transmembrane proteins? If yes, should I specify a particular score function file using -ddg:minimization_scorefunction option? I would like to study mutations in both, the transmembrane (membrane-facing and not) and the extracellular region of the protein, should I treat them separately?
If ddg_monomer can not properly predict the change in stability in transmembrane proteins, is there any other rosetta application that I can use for this purpose?
I have to parametrize some cofactors/ligands before I can start to study these mutations so I would like to know if it’s really worth it.
Thank very much you for your time.
May 20, 2014 at 3:07 pm #10061Anonymous
If you’re interested in looking at the effect of mutations on stability, the ddG_monomer application would probably be the one to use. It hasn’t been benchmarked for membrane proteins, though, so it’s a little up in the air as to whether it would adequately model such mutations. That’s not to say it can’t, it’s just that I wouldn’t be surprised if performance on membrane proteins is less than that on soluble ones. (I also wouldn’t be surprised if the performance was the same as with soluble proteins, either.) I’m going to guess that you’re more likely to have success with the buried portions of the molecule or the water-exposed portions than with the lipid-exposed portions. (Again, not saying you will have problems, just that the lipid-exposed residues would most likely be the ones to give you problems if you had any.)
For the exposed and buried residues you may be able to get away with using the standard soluble protein scorefunction, but you certainly may want to consider using a membrane scorefunction. To change the scorefunction, you’d want to specify both -ddg:minimization_scorefunction and -ddg::weight_file (The latter affects the initial repacking step.) I’d recommend trying the membrane_highres_Menv_smooth.wts weights file. This is the recommended full atom hard-repulsive scorefunction for membrane proteins, and can handle both the membrane embedded and water exposed portions, if the appropriate “span file” is used to specify the topology. Unfortunately, there really isn’t a soft repulsive score function for membrane proteins. You may be able to combine the regular soft-rep scorefunctions (either soft_rep_design.wts or ddg.wts – the two should be identical for fixed backbone repacking usage) with the membrane hard-rep scorefunction.
The big caveat is that with a different scorefunction the correlation ratio with experimental ddG values will no longer hold, so if your intent was to attempt to use information from Kellogg et al. to convert Rosetta-produced REU values into kcal values, then changing the scorefunction will invalidate that regression.
One thing you may want to consider is to take a membrane protein (preferably close to your system of interest) with a known structure and ddG values and run a short benchmark run with that known system. You can then see if the protocol and parameters that you are intending to use would might applicable for your system of interest.
May 21, 2014 at 11:24 am #10062Anonymous
thank you very much for your detailed reply.
I’ve almost finished to parametrize my cofactors/ligands so I’m ready for some tests. I’ll follow your advise and I’ll switch to a membrane scorefunction specifying a membrane span file. I can use membrane_highres_Menv_smooth.wts file for both -ddg:minimization_scorefunction and -ddg::weight_file options, isn’t it?
“…You may be able to combine the regular soft-rep scorefunctions (either soft_rep_design.wts or ddg.wts – the two should be identical for fixed backbone repacking usage) with the membrane hard-rep scorefunction…”
That seems an interesting solution. To combine two scorefunctions I have to simply use two different files for -ddg:minimization_scorefunction and -ddg::weight_file options, or am I taking this wrong? If so, please point me to the proper documentation on scorefunctions combination.
“…It hasn’t been benchmarked for membrane proteins, though, so it’s a little up in the air as to whether it would adequately model such mutations…”
That’s what I was afraid of and, as you also suggest, I’ll set-up a mini benchmark with some related proteins if I’ll be able to find some published data.
Thank you again
May 21, 2014 at 7:33 pm #10063Anonymous
Right – as I read things, it should be sufficient to change those two flags to control ddg_monomers scoring behavior. And you’re correct that you should be able to use different weights files for each of them. (-ddg::weight_file controls the scorefunction used for packing, and -ddg:minimization_scorefunction controls the score function used for minimization and the final evaluation.)
May 22, 2014 at 8:18 am #10064Anonymous
thank you again for your time and help.
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