Protein Preparation Automatic Protocol for High-Throughput Inverse Virtual Screening: Accelerating the Target Identification by Computational Methods

Structure-based virtual screening
is highly used in the early stages
of drug discovery to identify new putative lead compounds for a given
target. However, when a small molecule elicits a biological effect,
but its target is unknown, or the side effects it causes arise from
its undesired interaction with unknown counterparts, the identification
of its interacting targets represents an indispensable task. The computational
procedure named inverse virtual screening, which relies on docking
a molecule (or a small set of compounds) against panels of target
proteins to select the most promising complexes, could be useful to
overcome these issues. Panels can contain thousands of proteins, and
they must be correctly prepared to assure the best docking performance.
Therefore, the preparation of panels of proteins collected in the
Protein Data Bank (www.rcsb.org), if manually performed, may be costly in terms of time and efforts,
and this can limit the applicability of this approach in high-throughput
virtual screening workflows. We here show an automated workflow to
speed up panel preparation and development, and to test its performance,
this protocol was initially applied to a panel of 628 viral proteins
and, afterward, to a panel of transferase proteins (2789 entries)
to perform a large inverse virtual screening study, testing a small
set of compounds synthesized in our laboratory. Tankyrase 2 (PARP
5b) was selected as their preferred target of interaction, and the
predicted binding was validated by means of surface plasmon resonance
experiments. This protocol is useful for the rapid identification
of the interacting target for a bioactive compound; accordingly, it
facilitates the re-evaluation of the pharmacological activity of known
active compounds, addressing the repurposing and the polypharmacology
concepts.