5606 Journal of Medicinal Chemistry, 2008, Vol. 51, No. 18
Chen et al.
using default protein parameters. For all other structures, hydrogen
bonds were optimized to the default value. A receptor grid large
enough to encompass all crystallographically observed binding sites
was then generated from the prepared target protein. Constraints
for the Mg2+ ions were created but not actually used subsequently.
Water and heteroatoms >5 Å from the active site region were
removed. Geometry optimized ligands were prepared using Lig-
Prep,37 specifying a target pH 7.0 with tautomer and stereoisomer
generation. For the docking calculations, standard-precision (SP)
was specified for preliminary calculations, and the extra-precision
(XP) mode specified for the final calculations. Crystallographically
determined ligand poses from each structure were then compared
with the top 5 poses obtained from Glide; the rms errors are reported
in the text and in Tables S2 and S3 of the Supporting Information.
Docking poses were exported to Sybyl38 for CoMSIA analysis and
also into Liason24 for scoring function parametrization.
Docking Scoring Function Parameterization. Predicted docked
poses of all ligands investigated (generated above) were imported
into Liaison24 for molecular-mechanics energy calculations. Default
options were specified including a minimization sampling method
using a truncated Newton algorithm. Ensemble averages of van
der Waals, electrostatic, and cavity (solvent exposed ligand surface
area) energies were computed for the ligand-bound and ligand-
free states using an implicit solvation model. The computed energies
for each inhibitor complex and SlogP25 (computed in MOE) were
then imported into Strike,39 where partial-least-squares (PLS) and
multiple linear regression (MLR) methods were applied, to construct
a linear equation representing binding affinity. The optimal number
of components was automatically selected, and outliers identified.
All molecules were used to construct an initial training set, then
five test sets were selected at random from the data set. Each test
set compound was removed from the subsequent training set, with
binding affinities being predicted by using the constructed linear
equation. Coefficients for each energy term, fitting statistics and
predictions were reported, and are shown in Tables S2 and S5 of
the Supporting Information.
should remain constant through the series of perturbations, with
the optimum value of 1, for stable models.
Acknowledgment. We thank H. Sagami (Institute of Mul-
tidisciplinary Research for Advanced Materials, Tohoku Uni-
versity, Sendai, Japan) for providing the human GGPPS
expression system. Portions of this research were carried out at
the National Synchrotron Radiation Research Center, a national
user facility supported by the National Science Council of
Taiwan, Republic of China. This work was supported by
Academia Sinica and the National Core Facility of High-
throughput Protein Crystallography grant NSC95-3112-B-001-
015-Y (to A.H.-H.W.) and by the U.S. Public Health Service
(NIH grants GM-65307 and GM-073216, to E.O.). Y.Z. was
supported by an American Heart Association, Midwest Affiliate,
Postdoctoral Fellowship. Y.S. was supported by a Leukemia
and Lymphoma Society Special Fellowship.
Supporting Information Available: Data deposition: The
atomic coordinates and structure factors have been deposited in
collection and refinement information, protein expression, crystal-
lization, inhibition and cell growth inhibition results. This material
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