Improving Docking Performance Using Negative Image-Based Rescoring.

Sami T Kurkinen,Pekka A Postila,Sakari Lätti,Mira Ahinko,Olli T Pentikäinen,Sanna Niinivehmas

doi:10.3389/fphar.2018.00260

Sami T Kurkinen, Pekka A Postila + Show 4 more

Open Access

https://doi.org/10.3389/fphar.2018.00260

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Abstract

Despite the large computational costs of molecular docking, the default scoring functions are often unable to recognize the active hits from the inactive molecules in large-scale virtual screening experiments. Thus, even though a correct binding pose might be sampled during the docking, the active compound or its biologically relevant pose is not necessarily given high enough score to arouse the attention. Various rescoring and post-processing approaches have emerged for improving the docking performance. Here, it is shown that the very early enrichment (number of actives scored higher than 1% of the highest ranked decoys) can be improved on average 2.5-fold or even 8.7-fold by comparing the docking-based ligand conformers directly against the target protein's cavity shape and electrostatics. The similarity comparison of the conformers is performed without geometry optimization against the negative image of the target protein's ligand-binding cavity using the negative image-based (NIB) screening protocol. The viability of the NIB rescoring or the R-NiB, pioneered in this study, was tested with 11 target proteins using benchmark libraries. By focusing on the shape/electrostatics complementarity of the ligand-receptor association, the R-NiB is able to improve the early enrichment of docking essentially without adding to the computing cost. By implementing consensus scoring, in which the R-NiB and the original docking scoring are weighted for optimal outcome, the early enrichment is improved to a level that facilitates effective drug discovery. Moreover, the use of equal weight from the original docking scoring and the R-NiB scoring improves the yield in most cases.

Highlights

Molecular docking is an in silico technique that samples potential binding poses of ligands flexibly against the ligand-binding cavities of receptor protein structures
The enrichment is achieved by comparing the shape/electrostatics similarity between the ligand conformers and the negative image of the target protein’s ligand-binding cavity
The starting point of the R-NiB workflow (Figure 1) is that the ligands are docked into the same target protein’s cavity using a standard docking algorithm and, preferably, multiple solutions that roughly fit into the cavity are outputted for the rescoring

Summary

Introduction

Molecular docking is an in silico technique that samples potential binding poses of ligands flexibly against the ligand-binding cavities of receptor protein structures. Anybody who has used docking on routine basis can confirm that these successes are case-specific and the methodology often fails to produce sufficient enrichment (Ferrara et al, 2004; Mohan et al, 2005; Sousa et al, 2006; McGaughey et al, 2007; Plewczynski et al, 2011) In part, this hit-or-miss nature of docking is caused by the lack of relevant 3D structure data on the target proteins (Schapira et al, 2003) or inadequacies of the ligand conformer sampling (Sastry et al, 2013), but the other fundamental problem is the failure in scoring the sampled docking solutions (Wang et al, 2003; Warren et al, 2006; Plewczynski et al, 2011; Pagadala et al, 2017)

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