i have selected 14 ligand out of 207 data base from Lipophilic and ligand efficiency profiling and choose final ligand on the basis of consensus scoring. I have preformed Docking from Gold and now pose analysis is the problem i want to solve hERG symmetry problem so that all ligand align on same subunit and i can obtain a common pharmacophore. Please help
Dear Saba,
As far as I understood you have 14 ligands which you already docked. Now you have two options,
1) either select the ligands with same scaffolds and generate the pharmacophore model. this is the easiest way.
2) Another way is to generate separate pharmacophore models for best ligands lets say 5 (select them from molecular docking based on interactions responsible for activity of the enzyme) and further used these generated pharamcophore models. Align them and find out the common pharamcophoric features among them. Use this final model obtained after clustering all the pharmacophoric features for your further purposes. Make sure that the final model should have all the features responsible for the activity of the ligand molecules.
Good luck,
Gaurao
I would add one point to Sinisa Vukovic's excellent advice and reference (and in passing note that I certainly worked with a computer 30 years ago at a time he believes they didn't exist [wink]): don't assume that all your docking solutions are the same. That is, before pursuing any pharmacophore model inspect your solutions to convince yourself that the docking software didn't discover multiple separate classes of possible positions. I recall one situation where a post-doc went about that blindly and the pharmacophore ended up with a donor atom where none of the docking ensembles had any, because it was some-sort of averaging of two very distinct conformations.
My suggestion will be to develop multiple pharmacophore models for each pose (some will be similar, and some will be different), instead of trying to identify a common pharmacophore for all poses. If you are planning to use the models as search queries for screening 3D databases, you can then rate the models and hit lists with an external metric (using GH-score methodology) and prioritize your hits effectively.
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