Over-docking with FlexPepDock

I’m not quite sure where the problem is – is it that fixbb CAN discriminate, but it can’t after docking? Or neither can discriminate? (I’m not sure if “the results are quite good” means “I like them” or “all results say good binding”). Or is it that you don’t like how the docking results drift from the pocket? If you can see that the hits are clashing or out of the pocket, then you know they’re not real binding orientations, so you know that sequence is bad…

My experience with Rosetta is that it *always* predicts things will bind if you look at absolute scores. The scorefunction was created via folded, compact, crystallizable structures, and so Rosetta tries to force everything to mimic that. It can’t predict that things won’t bind, it can only predict that the best binding orientation available is weak.

In a case I’ve studied, Rosetta predicts binding energies (via InterfaceAnalyzer) of -30 for mutants that do bind, and -24 or so for mutants that do NOT bind experimentally. Both are ‘good’ scores in absolute terms, but in relative terms -30 is clearly better and in practice, the -30 works and the -20 doesn’t. It may be useful to try to analyze your scores in that light.

I don’t know that it’s possible to tune down flexpepdock per se. If you want no-backbone-motion docking, why not try minimization-only docking? I don’t have access to it now, but Monday I’ll attach an options file and RosettaScripts file that does minimization-only at the interface. (You can build it yourself pretty quick – just put PackRotamersMover with all positions packable followed by MinMover with all positions chi, bb, and jump minimizable, followed by InterfaceAnalyzerMover for binding analysis).

NM Monday, here they are now. These are not quite what I described but they’re what I use for minimization-only protein-protein interface docking. They’ll need some tweaking of course, let me know if you don’t know what it’s doing…

The approach that some people have used to discriminate binders from non-binders (not necessarily with FlexPepDock) is to do random docking. You take the ligand you want to dock (assuming you’re doing a constant ligand against a set of possible receptors), and dock it against a set of proteins where you know it shouldn’t bind (or at least where there is a large majority of non-binders, so you don’t need to be limited by the availability of experimental data). You can then look at the distribution of scores you get from the putative non-binders, and then you would estimate that any binders should be several standard deviations from that average. (Note that different sizes and types of ligands will likely have different average random docking energies, so if you have several ligands, you may need to repeat the average docking multiple times.)

For example, in your docking to non-binders, you might see the binding energy is -24 +/- 2 REU. You would then expect a true binder would hopefully have a binding energy of less than something like -28 REU: -30 is probably good, -26 is likely questionable, -20 is probably very bad. This doesn’t always work, as you will often run into cases where a known non-binder will get a really good energy, or a known binder will somehow give a really bad energy, but it gives you a better feel of what the numbers “should” be.