A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor

  • Future Med Chem. 2018 Oct;10(20):2411-2430. doi: 10.4155/fmc-2018-0198.
Maria E Mavrogeni  1 ,  Filippos Pronios  1 ,  Danae Zareifi  2 ,  Sofia Vasilakaki  1 ,  Olivier Lozach  3 ,  Leonidas Alexopoulos  4 ,  Laurent Meijer  5 ,  Vassilios Myrianthopoulos  1  6 ,  Emmanuel Mikros  1  6
Affiliations
  • 1. Department of Pharmaceutical Chemistry, School of Pharmacy, University of Athens, Panepistimiopolis Zografou, 157 71 Athens, Greece.
  • 2. ProtATonce Ltd, Dimokritos Science Park, Agia Paraskevi, 153 43 Athens, Greece.
  • 3. Laboratoire Chimie Electrochimie Moléculaires et Chimie Analytique, University of Brest, 29238 Brest, France.
  • 4. School of Mechanical Engineering, National Technical University of Athens, 157 80 Athens, Greece.
  • 5. ManRos Therapeutics, Perharidy Research Center, 29680 Roscoff, Bretagne, France.
  • 6. 'Athena' Research & Innovation Center, 151 25 Athens, Greece.
PMID: 30325204 DOI: 10.4155/fmc-2018-0198
Abstract

Background: Virtual screening is vital for contemporary drug discovery but striking performance fluctuations are commonly encountered, thus hampering error-free use. Results and Methodology: A conceptual framework is suggested for combining screening algorithms characterized by orthogonality (docking-scoring calculations, 3D shape similarity, 2D fingerprint similarity) into a simple, efficient and expansible python-based consensus ranking scheme. An original experimental dataset is created for comparing individual screening methods versus the novel approach. Its utilization leads to identification and phosphoproteomic evaluation of a cell-active DYRK1α inhibitor.

Conclusion: Consensus ranking considerably stabilizes screening performance at reasonable computational cost, whereas individual screens are heavily dependent on calculation settings. Results indicate that the novel approach, currently available as a free online tool, is highly suitable for prospective screening by nonexperts.

Keywords
CREB1; NCI diversity set-II; NSC379099; analysis of residuals; docking-scoring calculations; fingerprint similarity; p53; phosphoproteomics; screening enrichment; shape-based similarity.
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