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I have perform a ligand-protein docking using Autodock Vina. The result of the docking looks like this:

WARNING: The search space volume > 27000 Angstrom^3 (See FAQ)
Detected 8 CPUs
Reading input ... done.
Setting up the scoring function ... done.
Analyzing the binding site ... done.
Using random seed: -1553787135
Performing search ... done.
Refining results ... done.

mode |   affinity | dist from best mode
     | (kcal/mol) | rmsd l.b.| rmsd u.b.
-----+------------+----------+----------
   1         -5.9      0.000      0.000.  Pose 1
   2         -5.7     22.945     25.492.  Pose 2
   3         -5.5      1.426      2.046.  Pose 3
   4         -5.5     23.669     25.616
   5         -5.4     25.783     29.152.  .....
   6         -5.3     21.146     23.357
   7         -5.2     20.323     22.545
   8         -5.2     23.864     26.064
   9         -5.1     23.422     26.585.  Pose 9

As far as I understand from these statistics Mode 1(Pose 1) is the best. However when I actually visualize them in Pymol, Pose 1 has no hydrogen bonding at all but Pose 2 has.

My question is how can we judge if which of those two Pose is the best to use?

Note in figure below Pose 2 has dashed line (Hydrogen bond).

enter image description here

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It is best to contextualise the numbers. -1 kcal/mol is about the potential energy gained from a hydrogen bond —technically described in the r^6 part of the Lenard–Jones term, it is also the average collision energy of a water molecule at 37°C as that is RT ($\frac{k_b\cdot T}{N_A}$, wiki)under a Maxwell–Boltzmann distribution. A salt bridge –2 kcal/mol (Columbic force term). So your scores are not very low, hence why you are counting two hydrogen bonds. Although you can also see a lovely sulfur-pi interaction which is –1 to –2 kcal/mol, so those bonds alone are probably making a –4 kcal/mol contribution, so I am guessing that some terms may be horrendous, such as repulsion forces etc. A nice metric is doing a conversion to ligand efficiency, which weeds out affinities driven by size of the molecule... in this case most of the molecule is doing nothing. Also, most programs have an accuracy of 1 kcal/mol or higher.

So one cannot say what ∆∆G is the best with the data at hand due to noise, but one can say that the ∆∆G is not low enough... Sorry.

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  • $\begingroup$ Thanks. By ` ∆∆G` Do you mean ` affinity (kcal/mol)`? Then what do you consider ideal score range for ∆∆G. Since you said that my score above is not low enough? $\endgroup$
    – littleworth
    Jun 14, 2021 at 0:08
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    $\begingroup$ I did and it generally is fine, but variations of definitions and calculation come into play. Gibbs free energy is ∆G (so one delta to start with), although some tools refer to ∆G_bind as ∆∆G, and generally it's intended as ∆∆G = ∆G_holo - (∆G_apo + ∆G_aq), where ∆G_aq is the free energy in aqueous solvent, but this is not always true as there may be fancy stuff correcting for ∆G_aq (which makes little sense for a high partition coefficient compound), Vina is one of them. $\endgroup$
    – Matteo Ferla
    Jun 14, 2021 at 16:13
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    $\begingroup$ Higher than 10 kcal/mol... If you calculate the ligand efficiency it will be something between –0.1 and –0.2 kcal/mol/heavy. If your ligand is meant to dock with a high affinity, then I would revisit the template —a holo enzyme structure with the substrate removed is best, check the protonation of that aspartate etc. $\endgroup$
    – Matteo Ferla
    Jun 14, 2021 at 16:17
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    $\begingroup$ Ah. I just noticed your box is giving a warning of being over 30 Å x 30 Å x 30 Å. Which is huge. $\endgroup$
    – Matteo Ferla
    Jun 14, 2021 at 16:19
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    $\begingroup$ It's a ballpark figure. Playing devil's advocate, if you take the example of aspirin (an old small drug that's totally non-specific, that is taken at a 1,000 mg dose, that is charged, that is quickly broken by a liver cytochrome etc), it's binding to it's target is only -7 kcal/mol, which for 13 heavy atoms makes it's LE -0.5 kcal/mol, meaning that threshold doesn't always hold true (with massive caveats) $\endgroup$
    – Matteo Ferla
    Jun 20, 2021 at 22:08

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