Abstract

The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in structure-based drug discovery. To remedy this problem, elaborate rescoring and postprocessing schemes have been developed with a varying degree of success, specificity, and cost. The negative image-based rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets. The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein’s ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is directly used in the similarity comparison to rank the explicit docking poses. Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six popular docking softwares, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA, using five validated benchmark sets. Overall, the results indicate that R-NiB outperforms the default docking scoring consistently and inexpensively, demonstrating that the methodology is ready for wide-scale virtual screening usage.

Highlights

  • Structure-based drug discovery is increasingly turning toward in silico methods such as molecular docking for expediency and cost efficiency.[1−5] Docking aims to predict accurately both the bioactive binding pose and the affinity of a ligand forming the complex with its receptor

  • Target proteins (Figure 2; Table 1) were selected based on the dissimilarities in their ligand-binding cavities shape, size, and hydrophobicity or the level of difficulty observed in the prior docking or negative image-based rescoring (R-NiB; Figure 1) efforts.[21]

  • The mineralocorticoid receptor (MR; Figure 2A) has a welldefined and mostly hydrophobic ligand-binding site typical for steroid-binding nuclear receptors; i.e., high-affinity binding requires a tight fit between the ligand and the cavity

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Summary

Introduction

Structure-based drug discovery is increasingly turning toward in silico methods such as molecular docking for expediency and cost efficiency.[1−5] Docking aims to predict accurately both the bioactive binding pose and the affinity of a ligand forming the complex with its receptor. As the number of degrees of freedom increases, the computational costs of docking simulations increase Popular these days, it is debatable if either the flexible or induced-fit docking are suitable for high-throughput virtual screening as opposed to much lighter rigid docking simulations.[2,6] A robust metric for assessing sampling is to compare the predicted poses against the experimentally verified poses.[7−11] for example, X-ray crystal structures are only snapshots of the dynamic recognition process, and both the ligand and the receptor can have alternative reciprocal conformations.[12,13] Even so, the docking generally samples the “correct” poses excellently or at least reasonably well.[1,5,14,15]

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