Extensive
usage of molecular docking for computer-aided drug discovery
resulted in development of numerous programs with versatile scoring
and posing algorithms. Selection of the docking program among these
vast number of options is central to the outcome of drug discovery.
To this end, comparative assessment studies of docking offer valuable
insights into the selection of the optimal tool. Despite the availability
of various docking assessment studies, the performance difference
of docking programs has not been well addressed on metalloproteins
which comprise a substantial portion of the human proteome and have
been increasingly targeted for treatment of a wide variety of diseases.
This study reports comparative assessment of seven docking programs
on a diverse metalloprotein set which was compiled for this study.
The refined set of the PDBbind (2017) was screened to gather 710 complexes
with metal ion(s) closely located to the ligands (<4 Å). The
redundancy was eliminated by clustering and overall 213 complexes
were compiled as the nonredundant metalloprotein subset of the PDBbind
refined. The scoring, ranking, and posing powers of seven noncommercial
docking programs, namely, AutoDock4, AutoDock4Zn, AutoDock
Vina, Quick Vina 2, LeDock, PLANTS, and UCSF DOCK6, were comprehensively
evaluated on this nonredundant set. Results indicated that PLANTS
(80%) followed by LeDock (77%), QVina (76%), and Vina (73%) had the
most accurate posing algorithms while AutoDock4 (48%) and DOCK6 (56%)
were the least successful in posing. Contrary to their moderate-to-high
level of posing success, none of the programs was successful in scoring
or ranking of the binding affinities (r
2 ≈ 0). Screening power was further evaluated by using active-decoy
ligand sets for a large compilation of metalloprotein targets. PLANTS
stood out among other programs to be able to enrich the active ligand
for every target, underscoring its robustness for screening of metalloprotein
inhibitors. This study provides useful information for drug discovery
studies targeting metalloproteins.