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Front. Pharmacol. | doi: 10.3389/fphar.2018.00260

Improving Docking Performance Using Negative Image-Based Rescoring

 Sami Kurkinen1, Sanna Niinivehmas1,  Mira Ahinko1, Sakari Lätti1,  Olli Pentikäinen1, 2 and  Pekka Postila1*
  • 1Biological and Environmental Science & Nanoscience Center, University of Jyväskylä, Finland
  • 2Institute of Biomedicine, University of Turku, Finland

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.

Keywords: molecular docking, docking rescoring, negative image-based rescoring (R-NiB), Benchmarking, Consensus scoring

Received: 10 Nov 2017; Accepted: 08 Mar 2018.

Edited by:

Leonardo G. Ferreira, Universidade de São Paulo, Brazil

Reviewed by:

Craig Doupnik, Morsani College of Medicine, University of South Florida, United States
Alfonso T. Garcia-Sosa, University of Tartu, Estonia  

Copyright: © 2018 Kurkinen, Niinivehmas, Ahinko, Lätti, Pentikäinen and Postila. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Pekka Postila, University of Jyväskylä, Biological and Environmental Science & Nanoscience Center, Survontie 9, P.O. Box 35, Jyväskylä, 40014, Finland,