Virtual Target Screening: Validation Using Kinase Inhibitors

Abstract

Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term “Virtual Target Screening (VTS)”, a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.

Improvements, trends, and new ideas in molecular docking: 2012–2013 in review

“…However, the general outcome of this study is significant: an effective pathway to docking improvement may require the tailoring of a scoring function for a specific objective, herereverse docking. This conclusion is similar to that reached for the revised HYDE scoring function (Table 3) and its applications to binding affinity prediction versus VS. [72] Other inverse docking investigations included the following: the virtual target screening method calibrating a set of small molecules against a protein library [138] and prediction of activity of 656 marketed drugs on 73 unintended "side effect" targets. [139] Consideration of multiple docking solutions Consideration of multiple docking solutions can significantly increase the likelihood of determining the native binding pose, usually represented by an experimental pose.…”

Advances and Challenges in Computational Target Prediction

“…58 Similarly, in structure-based inverse screening, the interaction score of the ligand with each target is compared with the interaction score distribution from a set of reference ligands of the respective target complex structures, taken from X-ray structures or determined by docking. 81,83,84 Validation Strategies. The performance of ligand-based models should always be estimated using external test sets to minimize overfitting (besides cross-validation).…”

Synthesis, cytotoxicity and computational study of novel protoberberine derivatives

“…Despite inherent limitations of the available docking methodologies, this approach was demonstrated as useful in identifying a smaller number of putative targets for further evaluation. [41][42][43][44][45][46] Since the present aim was to rationalize the observed antiproliferative activities, targets were first prioritized based on the agreement between docking scores and the determined in vitro activity. This allowed the utilisation of all the available experimental data, but necessarily assumed that the in vitro cytotoxicity reflects on-target efficacy and was not dispro- portionately limited by other factors, such as permeability or active efflux.…”