Novel Approach to Structure-Based Pharmacophore Search Using Computational Geometry and Shape Matching Techniques

Abstract

Computationally efficient structure-based virtual screening methods have recently been reported that seek to find effective means to utilize experimental structure information without employing detailed molecular docking calculations. These tools can be coupled with efficient experimental screening technologies to improve the probability of identifying hits and leads for drug discovery research. Commercial software ROCS (rapid overlay of chemical structures) from Open Eye Scientific is such an example, which is a shape-based virtual screening method using the 3D structure of a ligand, typically from a bound X-ray costructure, as the query. We report here the development of a new structure-based pharmacophore search method (called Shape4) for virtual screening. This method adopts a variant of the ROCS shape technology and expands its use to work with an empty crystal structure. It employs a rigorous computational geometry method and a deterministic geometric casting algorithm to derive the negative image (i.e., pseudoligand) of a target binding site. Once the negative image (or pseudoligand) is generated, an efficient shape comparison algorithm in the commercial OE SHAPE Toolkit is adopted to compare and match small organic molecules with the shape of the pseudoligand. We report the detailed computational protocol and its computational validation using known biologically active compounds extracted from the WOMBAT database. Models derived for five selected targets were used to perform the virtual screening experiments to obtain the enrichment data for various virtual screening methods. It was found that our approach afforded similar or better enrichment ratios than other related methods, often with better diversity among the top ranking computational hits.

Structure-based three-dimensional pharmacophores as an alternative to traditional methodologies

“…This approach has been shown to help reduce the false positive rate. 52,53 Shape4 employs computational geometry algorithms (Delaunay tessellation/R-shape analysis) to detect the bind- …”

PLS and shape-based similarity analysis of maleimides – GSK-3 inhibitors

“…This fact emphasizes the importance of the biological activity of the query in terms of volume/shape and physico-chemical properties. The superior performance of Tversky scores over Tanimoto similarity score against the conformations of compound 81 is explainable since it evaluates the sub-shape matching between the database molecule and the query 54,78 . Since the molecule 81 display a smaller volume than the most active compound 34, this explains why the correlations provided by RefTversky similarity against the X-ray structure are shifted towards positive values.…”

Structure-based shape pharmacophore modeling for the discovery of novel anesthetic compounds

“…To allow for more detailed information to be used, pharmacophore group information is derived from the ferritin complex structures (with isoflurane and halothane), and added in the shape pharmacophore models. As reported in the SHAPE4 publication, 19 it implements an efficient, structure-based shape matching technology for virtual screening.…”

“…19 It uses the architecture and physicochemical texture of the binding pocket to perform virtual screening experiments. The binding pocket is modeled as an inverse shape with complementary functionalities to the binding pocket, and then used to screen in silico against large chemical libraries.…”