Similarity Metrics for Ligands Reflecting the Similarity of the Target Proteins

In this study we evaluate how far the scope of similarity searching can be extended to identify not only
ligands binding to the same target as the reference ligand(s) but also ligands of other homologous targets
without initially known ligands. This “homology-based similarity searching” requires molecular representations
reflecting the ability of a molecule to interact with target proteins. The Similog keys, which are introduced
here as a new molecular representation, were designed to fulfill such requirements. They are based only on
the molecular constitution and are counts of atom triplets. Each triplet is characterized by the graph distances
and the types of its atoms. The atom-typing scheme classifies each atom by its function as H-bond donor
or acceptor and by its electronegativity and bulkiness. In this study the Similog keys are investigated in
retrospective in silico screening experiments and compared with other conformation independent molecular
representations. Studied were molecules of the MDDR database for which the activity data was augmented
by standardized target classification information from public protein classification databases. The MDDR
molecule set was split randomly into two halves. The first half formed the candidate set. Ligands of four
targets (dopamine D2 receptor, opioid δ-receptor, factor Xa serine protease, and progesterone receptor)
were taken from the second half to form the respective reference sets. Different similarity calculation methods
are used to rank the molecules of the candidate set by their similarity to each of the four reference sets. The
accumulated counts of molecules binding to the reference target and groups of targets with decreasing
homology to it were examined as a function of the similarity rank for each reference set and similarity
method. In summary, similarity searching based on Unity 2D-fingerprints or Similog keys are found to be
equally effective in the identification of molecules binding to the same target as the reference set. However,
the application of the Similog keys is more effective in comparison with the other investigated methods in
the identification of ligands binding to any target belonging to the same family as the reference target. We
attribute this superiority to the fact that the Similog keys provide a generalization of the chemical elements
and that the keys are counted instead of merely noting their presence or absence in a binary form. The
second most effective molecular representation are the occurrence counts of the public ISIS key fragments,
which like the Similog method, incorporates key counting as well as a generalization of the chemical elements.
The results obtained suggest that ligands for a new target can be identified by the following three-step
procedure:  1. Select at least one target with known ligands which is homologous to the new target. 2.
Combine the known ligands of the selected target(s) to a reference set. 3. Search candidate ligands for the
new targets by their similarity to the reference set using the Similog method. This clearly enlarges the
scope of similarity searching from the classical application for a single target to the identification of candidate
ligands for whole target families and is expected to be of key utility for further systematic chemogenomics
exploration of previously well explored target families.