I took a pdb from protein data bank and extract the ligand and docked it, on the same pdb. My RMSD value was found to be 0.569 which is under the permissible limit. As such this is used to rationalize the docking software. But when I docked my inhibitors then I found they went and used some other sites and obviously have different interactions.
So I want to know how will I justify the molecular docking now? or is there any method which could show rationality in my results?
Hi Arvind
May I know which software you are using for the docking?
Dear Arvind
I think you must limit searching area to the protein active site.Maybe your docking tools cant search whole surface of your protein.
HAH
Sometimes size and shape of the docked ligand may not be suitable to dock ligand in the active site pocet of the receptor protein if the receptor residues are kept rigid. Manually fitting the ligand in the active site pocket based on the pharmacophoric reqirement for binding may give some clue if there are some very obvious reason (say steric clashes) for the ligand not to dock in the active site. Perhaps you may want to dock it using flexible docking protocol where the receptor residues are made flexible. Alternately if you have access to GOLD docking program, trying docking with soft potentials for the receptor atoms may also be useful. Bye the way which program(s) are you usin for this docking?
@ hamed. If I will limit my search area/ docking area then i am restricting the docking area and may be my results will be called as biased result for a particular area because i am forcing the inhibitors to docked only to one place.
@ uttam.. Ya consensus (output from different programs) will be better idea!
However will you little describe about the statistical (cluster analysis) validation.
For statistical validation, you need to run blind docking (without any restrictions) for a statistically significant number of times. Docking is a stochastic method so every time it will generate a different output. Now if you cluster the output with energy and RMSD as cut offs and then distribute it, you might observe some pattern. If you get a normal distribution pattern, binding is most likely to be non specific (no good). If you get a skewed (toward more negative) pattern, you can say the binding is non random and biased by some specific interactions. For more information please see the attached JBC article.
Article Novel anti-inflammatory activity of epoxyazadiradione agains...
Hi @uttam, I have seen the paper and wants to say thanks.
Moreover I found your paper interesting and informative. on the other side, I have some questions that wasn't provided in the paper.
(1) How you did clustering, which software and the way to did that?
(2) will it possible to arrange more parameters on that 3D graph?
- Badges
- Science topic
- Similar topics
- Bioinformatics
- Bioinformatics and Computational Biology