Using Multiple NMR solutions to Estimate Native Binding Orientation


#1

Hi,

Could we discriminate poor binding orientations from good orientations by docking the same ligand to a receptor, but performing the same dock multiple times using different NMR solutions of the ligand?

I was thinking about this since I docked two different NMR solutions to the same ligand and I got large differences in my HADDOCK score [Not within the SD].

Could we use it to refine the HADDOCK score?

Thanks,
M


#2

In theory, your approach is correct and, despite usually warning the users about the risk of comparing the HADDOCK scores between two different docking runs, this scenario allows you a fair comparison (provided your restraints are good enough to sample the same interface in both docking runs)

However, I see that the cluster size is rather small in both cases (which RMSD threshold did you use? How many models did you generate?) and that the RMSD of both clusters from the best HADDOCK model (lowest HADDOCK score) is huge (14-15 angstroms). You should maybe revise your parameters for the post-docking analysis, to obtain larger clusters and hopefully also cluster the lowest HADDOCK score model.
You can find here more information about how to perform manual clustering of the solutions:
http://www.bonvinlab.org/software/haddock2.2/analysis/#manual


#3

Considering you are docking the same ligand, but in different conformations, comparing the scores should be perfectly fine.

You can also instead give to HADDOCK an ensemble of ligand conformations. I would however limit those to say 10-20 max, otherwise you will have to increase the sampling.


#4

Hi there,

I have a follow-up question regarding the use of an ensemble of conformations as input.
Is there an easy way to find out then which of the models present in the ensemble was used to generate a given HADDOCK model?

Thanks!
Marta


#5

There is an easy way to keep track of which models have been used to generate a HADDOCK model at any stage of it. You can find in the tools/ directory of any successfully finished job a script called PDBtraceback.py.
To use it:

  • Change the name of the job directory to “run”.
  • Go to the new “run” directory.
  • Run the following command:
    $> python tools/PDBtraceback.py
  • A traceback.list file is created with the list of the HADDOCK models at each stage with the PDBs used as input.

I hope this will help you,

Regards,
Mikael


#6

Hello, could i make an ensemble in Haddock Guru of 15 pdbs by creating a multistate odb in pymol, and submit it in “second molecule”?


#7

You can indeed submit ensembles of models to HADDOCK. The format should still be a PDB formatted text file, but with MODEL / ENDMDL statements, as is done for example for NMR ensembles