Template-Based Method for Conformation Generation and Scoring for Congeneric Series of Ligands

Physics-based prediction
of protein–ligand binding affinities
for a congeneric series of ligands in lead optimization requires their
geometries as a first step. In this paper, we report a method that
uses the 3D conformation of a lead compound in complex with a protein
as a template to generate conformations of a series of related analog
compounds. The method uses the Maximal Common Substructure (MCS) computed
between lead and analog ligands to assign coordinates for the atoms
shared between the ligands. For the differing atoms, a conformation
generation procedure is implemented that results in a diversity of
conformations. The generated conformations are sorted using a score
based on the Molecular Mechanics and Generalized Born with Solvent
Accessible Surface Area contribution (MM-GBSA) method. The accuracy
of the generated conformations is tested retrospectively using a cross-validation
approach applied to four data sets obtained from the Drug Design Data
Resource (D3R) by measuring the RMSD of the top scored conformation
with respect to the crystallographic pose. The scoring ability of
the method is independently assessed using data for the same protein
targets to test the rank ordering ability and separating active and
inactive ligands. We tested the effect of protein flexibility during
structural optimization and scoring approaches with and without strain
energies. Retrospective validation on data sets comprising 4 targets
shows that the method outperforms random selection for all targets
and outperforms a molecular weight-based null model in 3 out of 4
targets in separating active and inactive compounds. Therefore, the
presented method is expected to be of utility in lead optimization
for rapidly screening analog ligands and generating initial conformations
for use in more detailed physics-based binding affinity prediction
methods.