Abstract
Virtual screening is emerging as a highly applied technique for the search of hits since it significantly reduces the time required for the establishment of novel, effective compounds compared to high-throughput screening. Implementing correlation coefficients to determine if a molecular docking study is robust and reliable has been established as common practice in recent years. The aim of this work was to determine if a relevant pairwise correlation between the scoring functions (ChemPLP, GoldScore, Chemscore and ASP) of the docking software GOLD 5.2 and previously determined experimental data of pyrrole derivatives with MAO-B inhibitory activity could be achieved. In order to optimize the correlation coefficient, we calculated the Pearson’s and Spearman’s coefficients after each docking simulation with all four GOLD 5.2 scoring functions. Thereafter, we varied three changeable parameters – the size of the grid space, the side-chain flexibility and the presence of water molecules in the active site, to perceive if we could obtain better correlation values. The highest R2=0.79 was attained with the following docking settings: scoring function ChemPLP, grid size 12Å and no rotatable side chain residues. This work provides an applicable GOLD 5.2 docking protocol for a future virtual screening of novel MAO-B inhibitors with pyrrole moiety.
Highlights
Computer-aided drug design (CADD) is a widely used term including all computational processes for the analysis and modeling of compounds (Song et al, 2009)
CADD is divided into two categories – structure-based drug design and ligand-based drug design
Ensemble docking is emerging as a prominent technique attempting to resolve the major problem of molecular docking, that is to say, the flexibility of the proteins
Summary
Computer-aided drug design (CADD) is a widely used term including all computational processes for the analysis and modeling of compounds (Song et al, 2009). The Structure-based drug design (SBDD) is applied when a 3D structure of the receptor is available. The most common technique utilized in SBDD is molecular docking. It analyzes the interactions between ligands and macrostructures using search algorithms and scoring functions (Kumar et al, 2016). The application of molecular docking is drastically increasing due to the rapid growth of resolved crystallographic receptors with cocrystallized ligands. Rising computing power is further accelerating the process of hit discovery and lead optimizations implementing molecular docking (Torres et al, 2019). In order to enrich the obtained top solutions, an ensemble docking simulation has been established (Ellingson et al, 2014)
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