5.2 Analysis of Docking Tools
As discussed above, it is fruitful to analyze the ligands binding efficiency using
many methods like AutoDock, GOLD, Glide, LibDock, and HADDOCK; all these
tools are different in the method of docking as well as scoring.
There are several open-source commercial but free for academic use, and
complete commercial docking programs available from different software vendors.
In particular, fifty-one stand-alone and nineteen Web servers for docking employing
diverse set of novel features are listed at http://www.click2drug.org/index.
html#Docking (accessed on Dec 2017). To select suitable program(s) for docking
studies for receptor(s) of interest requires insight and expertise [117] in the method.
However, we shall discuss only a few selectively chosen methods based on popularity and diversity of strategies implemented in them as shown in Table 7.
Here we are discussing the in-house case study (unpublished work) of four docking
programs used to dock already experimentally known inhibitors of P. falciparum
protein kinase 5 (PfPK5) with IC 50 values ranging from 130 to 15000 nM. PfPK5 is a
ser/thr kinase and homolog of human CDK2 [185]. Chosen inhibitors are olomoucine
(OLM), indirubin-5-sulfonate (INR), staurosporine (STA), and purvalanol B (PVB),
respectively. Crystal structures of two of the inhibitors (INR and PVB) in complex
with PfPK5 are available [185]. We have chosen LibDock v2.3, Gold v5.2, Dock v6.7,
and Glide v7.0 for the comparison study. Different docking programs use different
scoring schemes, e.g., Glide score and Dock score assign high negative score
to high-affinity ligands, while LibDock and Gold assign high positive score to
high-affinity ligands. Pose reproduction and also scoring/ranking of docked poses of
these inhibitors is a good case to assess comparative performance of each of the
selected docking program and also with experimental values.
The best-scoring poses predicted by each of the programs were compared with
the crystallized poses for selected available complex of PfPK5 with PVB as in PDB
(1V0P). Predicted poses for PVB obtained from LibDock, Gold, Dock, and Glide
showed 0. 60, 1.01, 0.88, and 1.87 Å RMSDs with crystallized pose, respectively.
In present case, all the selected programs were able to reproduce observed binding
mode within RMSD of 2 Å.
Docking and scoring results obtained from the chosen programs show that none
of these could predict the correct ranking against the experimentally known activity
of chosen inhibitors (see Table 8). The best binder (PVB) among four inhibitors is
predicted to be best binder as rank 1, by Gold and Dock6, while LibDock and Glide
have ranked 2. LibDock is unable to discriminate between the OLM and INR and
predicts them as rank 3 and rank 4, while experimentally found ranks would be 4
and 3, respectively. Again, LibDock does not discriminate between STA and PVB
and predicted ranks are opposite to the experimental ranks. Gold predicts correct
ranks for best and worst inhibitors, while is unable to discriminate between
mid-ranged inhibitors INR and STA. Dock6 predicts correct ranks for better binders
STA and PVB, while does not discriminate between weak binders OLM and INR.
Predicted ranks from Glide did not match with experimental rank for any of the four
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As discussed above, it is fruitful to analyze the ligands binding efficiency using
many methods like AutoDock, GOLD, Glide, LibDock, and HADDOCK; all these
tools are different in the method of docking as well as scoring.
There are several open-source commercial but free for academic use, and
complete commercial docking programs available from different software vendors.
In particular, fifty-one stand-alone and nineteen Web servers for docking employing
diverse set of novel features are listed at http://www.click2drug.org/index.
html#Docking (accessed on Dec 2017). To select suitable program(s) for docking
studies for receptor(s) of interest requires insight and expertise [117] in the method.
However, we shall discuss only a few selectively chosen methods based on popularity and diversity of strategies implemented in them as shown in Table 7.
Here we are discussing the in-house case study (unpublished work) of four docking
programs used to dock already experimentally known inhibitors of P. falciparum
protein kinase 5 (PfPK5) with IC 50 values ranging from 130 to 15000 nM. PfPK5 is a
ser/thr kinase and homolog of human CDK2 [185]. Chosen inhibitors are olomoucine
(OLM), indirubin-5-sulfonate (INR), staurosporine (STA), and purvalanol B (PVB),
respectively. Crystal structures of two of the inhibitors (INR and PVB) in complex
with PfPK5 are available [185]. We have chosen LibDock v2.3, Gold v5.2, Dock v6.7,
and Glide v7.0 for the comparison study. Different docking programs use different
scoring schemes, e.g., Glide score and Dock score assign high negative score
to high-affinity ligands, while LibDock and Gold assign high positive score to
high-affinity ligands. Pose reproduction and also scoring/ranking of docked poses of
these inhibitors is a good case to assess comparative performance of each of the
selected docking program and also with experimental values.
The best-scoring poses predicted by each of the programs were compared with
the crystallized poses for selected available complex of PfPK5 with PVB as in PDB
(1V0P). Predicted poses for PVB obtained from LibDock, Gold, Dock, and Glide
showed 0. 60, 1.01, 0.88, and 1.87 Å RMSDs with crystallized pose, respectively.
In present case, all the selected programs were able to reproduce observed binding
mode within RMSD of 2 Å.
Docking and scoring results obtained from the chosen programs show that none
of these could predict the correct ranking against the experimentally known activity
of chosen inhibitors (see Table 8). The best binder (PVB) among four inhibitors is
predicted to be best binder as rank 1, by Gold and Dock6, while LibDock and Glide
have ranked 2. LibDock is unable to discriminate between the OLM and INR and
predicts them as rank 3 and rank 4, while experimentally found ranks would be 4
and 3, respectively. Again, LibDock does not discriminate between STA and PVB
and predicted ranks are opposite to the experimental ranks. Gold predicts correct
ranks for best and worst inhibitors, while is unable to discriminate between
mid-ranged inhibitors INR and STA. Dock6 predicts correct ranks for better binders
STA and PVB, while does not discriminate between weak binders OLM and INR.
Predicted ranks from Glide did not match with experimental rank for any of the four
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
143