optimization process allows the protein-binding site model
used for docking to be refined (if necessary) and provides a
check on the initial docking mode predictions. If the modeler
is confident that the original models can explain the majority
of the structure-activity relationships (SAR) observed via
biological testing, then solving these additional complex
structures may not be necessary. Furthermore, the
determination of protein-ligand structures can sometimes
prove difficult, even if the apo-structure is crystallized
readily.
A target structure that has been determined by 3-D NMR
techniques can be utilized for lead optimization; however,
this is less common than using an X-ray crystal structure,
because solving an NMR-determined protein structure can
be more time consuming and requires larger samples of
protein. NMR techniques are used for target determination
if the target is (i) sufficiently small (< 30 kDa), (ii) has
eluded structural determination via X-ray crystallographic
techniques, and (iii) is considered an important target.
However, it is more common that a homology model will be
generated based on a high resolution X-ray crystal structure
of a related protein, if one exists. For such homology models,
generally 20 to 30% sequence identity is required between
the desired target and the template protein sequence,
although there are no hard rules. For example, given low
overall sequence identity, higher identity in the active site
can be critical as can be the availability of other types of low
resolution structural data that provide constraints for the
target structure [5]. Gilson and co-workers carried out a
systematic study comparing the docking of a database of
'drug-like' molecules to an X-ray crystal structure and a set
of homology models for five drug targets [6•]. The study
demonstrated that docking to target homology models can
result in significant enrichment of known actives in a ranked
hit list; however, the researchers often found similar
enrichments when docking directly to the templates for the
homology models themselves.
Annotated databases of 3-D structures of druggable binding
sites suitable for docking studies (eg, http://bioinfo-pharma.
u-strasbg.fr/scPDB [7•]) and comparative models for protein
sequences that are homologous to at least one existing 3-D
protein structure (eg, http://salilab.org/modbase [8]) are
available on the Internet. Therefore, given the fact that the
number of 3-D macromolecular structures determined and
protein models generated continues to increase every year
[9], successful docking to surrogate protein structures and
homology models is also expected to increase in the future.
The success of a structure-based lead optimization effort also
depends, to a large extent, on close collaboration between
the modeler(s) and medicinal chemists developing the
ligand. Synthetic accessibility clearly needs to be considered
when designing modified leads, which is best accomplished
by the modeler and medicinal chemists together viewing
models of proposed compounds in 3-D. Tools such as
Benchware3D Explorer allow the modeler to prepare
annotated, labeled views of modeling results that can be
shared with medicinal chemists via email, and allows the
chemists to re-evaluate the models and structures at their
Desktop during the design of new synthetic targets. Existing
X-ray crystal structures of protein-ligand complexes are
viewed superimposed with docked poses for proposed
ligands.
The structure-based lead optimization process can involve
simple energy minimization of a few molecules in the
protein-binding site or the docking of relatively large
combinatorial libraries of hundreds or even thousands of
molecules. The same docking methods that are used for the
virtual screening of large combinatorial libraries of millions