Dear Rosetta Users,
I have three questions regarding the RosettaLigand application.
1) As far as I can understand, the total_score is a measure of the overall quality of the models generated by the docking run. The lower the score value and the better the model.
In my limited experience I've always had negative score values, but for a particular protein target I'm always getting positive values (even with different pockets in the same structure).
The total_score must be always negative?
A positive score means that the interaction is unlikely?
2) I'd like to study a protein-ligand interaction for which I have no information about.
The only thing I know is that the protein has various binding pockets that could accomodate the ligand with the same probability.
I've docked the ligand in each of the pockets and I want to compare the results of each individual docking run.
Is it reasonable to identify the best pocket by comparing the energy (interface_delta_X) vs. total_score plots?
3) In order to obtain the Rosetta’s best prediction for the ligand-docking experiment, I've read different approaches.
For instance, in Combs et al. 2013, the top 10% of models by energy (interface_delta_X) is considered. In other cases the the top 10% of models by total_score is selected. Then, regardless the parameter considered, the top 10% of models is clustered and the best energy model(s) from the largest cluster(s) is considered as putative binding mode(s).
There is a reason to prefer the top 10% energy models over the top 10% score models or vice-versa?
Moreover, I'd like to know if someone is aware of a more recent (and perhaps 'standardized') protocol to select the best model.
Thanks in advance!