thymidine kinase (TK) with one PDB structure, ten active compounds, and 990
randomly selected decoys from pre-curated Advanced Chemical Directory
(ACD) which was considered for each of receptors to evaluate DOCK, FlexX, and
GOLD programs and seven scoring functions (Dock, FlexX, GOLD, PMF,
ChemScore, Fresno, and Score) [197].
5.4 Consensus Evaluation of Docking
Docking studies performed using different programs which do not necessarily agree
with each other as discussed earlier, mostly because each program carries different
subtasks of docking with potentially different approach [199]. Thus, when results
disagree among themselves, then selection of the final compounds to test becomes
indecisive. Matthew and co-workers [199] suggested selection of results based on
consensus followed by rationalization through physicochemical intuition. As discussed later, such strategies should be projected as standard to increase confidence
in docking results and decrease failure rate of docking studies.
Benchmarking of docking studies is very important for unbiased evaluation of
various docking methodologies and their implementations in docking programs. To
address this issue, Huang et al. [176] conducted a study along with creating a
directory of useful decoys (DUD) [176]. They choose total 40 different targets with
eight nuclear hormone receptors, nine kinases, three serine proteases, four metalloenzymes, two folate enzymes, and ten other enzymes. The crystal structures of all
targets except one kinase (PDGFrb) were available in PDB. They used 2950 ligands, creating 36 physically similar but topologically different decoys for each
ligand. Docking was done using DOCK 3.5.54, with flexible ligand and a
force-field-based scoring function accounting van der Waals and electrostatics interaction energies corrected for ligand desolvation. Authors reported that for most
of the targets, with MDDR (Elsevier MDL, San Leandro CA) databases, enrichment
were almost half log better than DUD, which supported their conclusion that
generally databases have bias.
Another protocol is known as checking with cross-docking which aims to
summarize the overall success of docking study [200], it captures ligands specificity
for its cognate receptor at diagonal of the matrix, and off-diagonal entries represent
enrichments against off-diagonal targets. The off-diagonal enrichments could also
be indicative of promiscuity of the ligand, or the similarity of the off-diagonal
targets [201]. The cross-docking performed in the process highlighted striking
results that ligands having very good enrichment for their cognate receptor had
good enrichments against a few other receptor sets, while ligands with poor
enrichment for their receptor had poor enrichment against others [202–204].
Overall, it has been found that the interaction-based classification and estimation
of accuracy of poses during docking are in better agreement with the experimental
results [205].
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
147
randomly selected decoys from pre-curated Advanced Chemical Directory
(ACD) which was considered for each of receptors to evaluate DOCK, FlexX, and
GOLD programs and seven scoring functions (Dock, FlexX, GOLD, PMF,
ChemScore, Fresno, and Score) [197].
5.4 Consensus Evaluation of Docking
Docking studies performed using different programs which do not necessarily agree
with each other as discussed earlier, mostly because each program carries different
subtasks of docking with potentially different approach [199]. Thus, when results
disagree among themselves, then selection of the final compounds to test becomes
indecisive. Matthew and co-workers [199] suggested selection of results based on
consensus followed by rationalization through physicochemical intuition. As discussed later, such strategies should be projected as standard to increase confidence
in docking results and decrease failure rate of docking studies.
Benchmarking of docking studies is very important for unbiased evaluation of
various docking methodologies and their implementations in docking programs. To
address this issue, Huang et al. [176] conducted a study along with creating a
directory of useful decoys (DUD) [176]. They choose total 40 different targets with
eight nuclear hormone receptors, nine kinases, three serine proteases, four metalloenzymes, two folate enzymes, and ten other enzymes. The crystal structures of all
targets except one kinase (PDGFrb) were available in PDB. They used 2950 ligands, creating 36 physically similar but topologically different decoys for each
ligand. Docking was done using DOCK 3.5.54, with flexible ligand and a
force-field-based scoring function accounting van der Waals and electrostatics interaction energies corrected for ligand desolvation. Authors reported that for most
of the targets, with MDDR (Elsevier MDL, San Leandro CA) databases, enrichment
were almost half log better than DUD, which supported their conclusion that
generally databases have bias.
Another protocol is known as checking with cross-docking which aims to
summarize the overall success of docking study [200], it captures ligands specificity
for its cognate receptor at diagonal of the matrix, and off-diagonal entries represent
enrichments against off-diagonal targets. The off-diagonal enrichments could also
be indicative of promiscuity of the ligand, or the similarity of the off-diagonal
targets [201]. The cross-docking performed in the process highlighted striking
results that ligands having very good enrichment for their cognate receptor had
good enrichments against a few other receptor sets, while ligands with poor
enrichment for their receptor had poor enrichment against others [202–204].
Overall, it has been found that the interaction-based classification and estimation
of accuracy of poses during docking are in better agreement with the experimental
results [205].
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
147