Applying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalization

Abstract

BackgroundStructure-based virtual screening techniques can help to identify new lead structures and complement other screening approaches in drug discovery. Prior to docking, the data (protein crystal structures and ligands) should be prepared with great attention to molecular and chemical details.ResultsUsing a subset of 18 diverse targets from the recently introduced DEKOIS 2.0 benchmark set library, we found differences in the virtual screening performance of two popular docking tools (GOLD and Glide) when employing two different commercial packages (e.g. MOE and Maestro) for preparing input data. We systematically investigated the possible factors that can be responsible for the found differences in selected sets. For the Angiotensin-I-converting enzyme dataset, preparation of the bioactive molecules clearly exerted the highest influence on VS performance compared to preparation of the decoys or the target structure. The major contributing factors were different protonation states, molecular flexibility, and differences in the input conformation (particularly for cyclic moieties) of bioactives. In addition, score normalization strategies eliminated the biased docking scores shown by GOLD (ChemPLP) for the larger bioactives and produced a better performance. Generalizing these normalization strategies on the 18 DEKOIS 2.0 sets, improved the performances for the majority of GOLD (ChemPLP) docking, while it showed detrimental performances for the majority of Glide (SP) docking.ConclusionsIn conclusion, we exemplify herein possible issues particularly during the preparation stage of molecular data and demonstrate to which extent these issues can cause perturbations in the virtual screening performance. We provide insights into what problems can occur and should be avoided, when generating benchmarks to characterize the virtual screening performance. Particularly, careful selection of an appropriate molecular preparation setup for the bioactive set and the use of score normalization for docking with GOLD (ChemPLP) appear to have a great importance for the screening performance. For virtual screening campaigns, we recommend to invest time and effort into including alternative preparation workflows into the generation of the master library, even at the cost of including multiple representations of each molecule.Graphical AbstractUsing DEKOIS 2.0 benchmark sets in structure-based virtual screening to probe the impact of molecular preparation and score normalization.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0074-6) contains supplementary material, which is available to authorized users.

A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

“…Although we do not claim that our collection is comprehensive, there are no commonly used, state-of-the-art evaluation data sets available. Standardized benchmark sets are accessible for broadly applied modeling approaches such as pharmacophore searches [61] and molecular docking [16,62]. In contrast, the high diversity of the applied benchmark sets for binding site comparison makes it difficult to draw definitive conclusions in comparing the different tools.…”

“…Usually, published binding site comparison algorithms have been benchmarked using specific data sets, which are highly correlated with distinct application domains. However, standardized benchmark data sets, as known for other in silico methodologies [14][15][16], have never been developed for cavity comparison tools. This often precludes the selection of a suitable tool.…”

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

“…New databases were designed with an increasing complexity in the decoys selection methodologies (see section Benchmarking Databases). Nowadays, benchmarking databases are widely used to evaluate various VS tools (Kellenberger et al, 2004 ; Warren et al, 2006 ; McGaughey et al, 2007 ; von Korff et al, 2009 ; Braga and Andrade, 2013 ; Ibrahim et al, 2015a ; Pei et al, 2015 ) and to support the identification of hit/lead compounds using LBVS and SBVS (Allen et al, 2015 ; Ruggeri et al, 2015 ).…”

“…On the contrary, the possible presence of active compounds in the decoy compounds set may introduce an artificial underestimation of the enrichment (Verdonk et al, 2004 ; Good and Oprea, 2008 ) since decoys are usually assumed to be inactive rather than proved to be true inactive compounds (i.e., confirmed inactive through experimental bioassays). New databases were designed to minimize those biases (Rohrer and Baumann, 2009 ; Vogel et al, 2011 ; Mysinger et al, 2012 ; Ibrahim et al, 2015a ). Finally, many studies pointed out that the VS performance depends on the target and its structural properties (structural flexibility, binding site physicochemical properties, etc.…”

“…; Cummings et al, 2005 ). Taking this into consideration, and despite the growing number of protein families represented in databases, decoy datasets generation tools were made publicly available in order to allow any scientist to fine-tune target-dependant and reliable benchmarking datasets (Mysinger et al, 2012 ; Ibrahim et al, 2015a ).…”