Homology Model-Based Virtual Screening for GPCR Ligands Using Docking and Target-Biased Scoring

Abstract

The current study investigates the combination of two recently reported techniques for the improvement of homology model-based virtual screening for G-protein coupled receptor (GPCR) ligands. First, ligand-supported homology modeling was used to generate receptor models that were in agreement with mutagenesis data and structure-activity relationship information of the ligands. Second, interaction patterns from known ligands to the receptor were applied for scoring and rank ordering compounds from a virtual library using ligand-receptor interaction fingerprint-based similarity (IFS). Our approach was evaluated in retrospective virtual screening experiments for antagonists of the metabotropic glutamate receptor (mGluR) subtype 5. The results of our approach were compared to the results obtained by conventional scoring functions (Dock-Score, PMF-Score, Gold-Score, ChemScore, and FlexX-Score). The IFS lead to significantly higher enrichment rates, relative to the competing scoring functions. Though using a target-biased scoring approach, the results were not biased toward the chemical classes of the reference structures. Our results indicate that the presented approach has the potential to serve as a general setup for successful structure-based GPCR virtual screening.

FINDSITEX: A Structure-Based, Small Molecule Virtual Screening Approach with Application to All Identified Human GPCRs

“…The current approach utilizes remote as well as closely homologous proteins without solved receptor and holo structures but does require experimentally determined ligand binding data that are then employed in structure based virtual screening. FINDSITE X provides the necessary state-of-the-art multiple-template based receptor structures as well as structure-based virtual screening results based on these predicted receptor structures 14, 15, 19, 38 . FINDSITE X can also predict possible off-targets of a given target.…”

“…Ligand-based virtual screening is often the first step in structure based virtual screening 14, 15, 38 . The structures of target and library GPCRs (GPCR-lib) with experimentally identified binding ligands are built using TASSER VMT -lite.…”

“…Finally, the positions of small molecule ligands are placed and adjusted to optimize their interactions with the protein of modeled structure and to rank the predicted poses. These basic ideas have subsequently been applied by a number of groups including those of Zhang, Sternberg and others 17, 19, 37, 38 . The FINDSITE/Q-dock approach uses FINDSITE as the first step to identify functional relationships and binding ligand substructures from complexes in the PDB 39 .…”

“…Due to the scarcity of complexes in the PDB for GPCRs, FINDSITE is inapplicable to the large-scale virtual screening of GPCRs. Successful structure-based virtual screening using homology modeling for few individual GPCR family members can be found elsewhere 38, 40 .…”

Closing the side-chain gap in protein loop modeling

“…For example, homology models have been useful for developing kinase inhibitors as agents for cancer treatment [2,3]. Additionally, homology models have been used as successful starting points for virtual screening [4][5][6][7]. Accumulated protein modeling studies have shown that homology modeling is the most successful method for structure prediction [8][9][10].…”