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

Abstract

We have developed FINDSITEX, an extension of FINDSITE, a protein threading based algorithm for the inference of protein binding sites, biochemical function and virtual ligand screening, that removes the limitation that holo protein structures (those containing bound ligands) of a sufficiently large set of distant evolutionarily related proteins to the target be solved; rather, predicted protein structures and experimental ligand binding information are employed. To provide the predicted protein structures, a fast and accurate version of our recently developed TASSERVMT, TASSERVMT-lite, for template-based protein structural modeling applicable up to 1000 residues is developed and tested, with comparable performance to the top CASP9 servers. Then, a hybrid approach that combines structure alignments with an evolutionary similarity score for identifying functional relationships between target and proteins with binding data has been developed. By way of illustration, FINDSITEX is applied to 998 identified human G-protein coupled receptors (GPCRs). First, TASSERVMT-lite provides updates of all human GPCR structures previously modeled in our lab. We then use these structures and the new function similarity detection algorithm to screen all human GPCRs against the ZINC8 non-redundant (TC<0.7) ligand set combined with ligands from the GLIDA database (a total of 88,949 compounds). Testing (excluding GPCRs whose sequence identity > 30% to the target from the binding data library) on a 168 human GPCR set with known binding data, the average enrichment factor in the top 1% of the compound library (EF0.01) is 22.7, whereas EF0.01 by FINDSITE is 7.1. For virtual screening when just the target and its native ligands are excluded, then the average EF0.01 reaches 41.4. We also analyze off-target interactions for the 168 protein test set. All predicted structures, virtual screening data and off-target interactions for the 998 human GPCRs are available at http://cssb.biology.gatech.edu/skolnick/webservice/gpcr/index.html.

SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity

“…This binding-homology-search-based approach, pioneered by FINDsite [13][14][15] , shows significant improvements over two commonly employed docking techniques (DOCK6 [29] and AUTODOCK Vina [31] ) in enrichment factors and AUC values. The approach is effective because increasingly large number of complex structures between proteins and ligands are depositing in protein databank.…”

“…In this approach, whether a query ligand binds to a target receptor can be inferred from whether or not there exists a similar ligand (similar to the query compound) that binds to a similar receptor (structurally similar to the target receptor). [13][14][15][16] Such a "binding-homology" approach is computationally more efficient than docking because it does not require to search for optimal docking conformations. FINDsite [13][14][15] is the first structure-based virtual screening method based on binding homology search.…”

“…[13][14][15][16] Such a "binding-homology" approach is computationally more efficient than docking because it does not require to search for optimal docking conformations. FINDsite [13][14][15] is the first structure-based virtual screening method based on binding homology search. This method employed either known structure or threading-based model structure for the target protein.…”

“…This method employed either known structure or threading-based model structure for the target protein. The target structure is then globally aligned to template complex structures (either known [13] or model [14] complex structures or both [15] ). Template ligands in the dominant binding pockets are utilized for searching similar ligands against a compound library as the traditional ligand homology search approaches.…”

GPCR & Company: Databases and Servers for GPCRs and Interacting Partners

“…A repository of homology models for human GPCRs (Zhang et al 2006) was recently updated and now contains almost 1,000 human GPCR model structures combined with virtual screening of the ZINC (Irwin et al 2012) database (Zhou and Skolnick 2012). The I-TASSER server enables modeling of protein structures (including GPCRs) using multiple templates (Zhang 2008).…”