Journal of Computer-Aided Molecular Design

, Volume 30, Issue 8, pp 583–594 | Cite as

mRAISE: an alternative algorithmic approach to ligand-based virtual screening

  • Mathias M. von Behren
  • Stefan Bietz
  • Eva Nittinger
  • Matthias Rarey
Article

Abstract

Ligand-based virtual screening is a well established method to find new lead molecules in todays drug discovery process. In order to be applicable in day to day practice, such methods have to face multiple challenges. The most important part is the reliability of the results, which can be shown and compared in retrospective studies. Furthermore, in the case of 3D methods, they need to provide biologically relevant molecular alignments of the ligands, that can be further investigated by a medicinal chemist. Last but not least, they have to be able to screen large databases in reasonable time. Many algorithms for ligand-based virtual screening have been proposed in the past, most of them based on pairwise comparisons. Here, a new method is introduced called mRAISE. Based on structural alignments, it uses a descriptor-based bitmap search engine (RAISE) to achieve efficiency. Alignments created on the fly by the search engine get evaluated with an independent shape-based scoring function also used for ranking of compounds. The correct ranking as well as the alignment quality of the method are evaluated and compared to other state of the art methods. On the commonly used Directory of Useful Decoys dataset mRAISE achieves an average area under the ROC curve of 0.76, an average enrichment factor at 1 % of 20.2 and an average hit rate at 1 % of 55.5. With these results, mRAISE is always among the top performing methods with available data for comparison. To access the quality of the alignments calculated by ligand-based virtual screening methods, we introduce a new dataset containing 180 prealigned ligands for 11 diverse targets. Within the top ten ranked conformations, the alignment closest to X-ray structure calculated with mRAISE has a root-mean-square deviation of less than 2.0 Å for 80.8 % of alignment pairs and achieves a median of less than 2.0 Å for eight of the 11 cases. The dataset used to rate the quality of the calculated alignments is freely available at http://www.zbh.uni-hamburg.de/mraise-dataset.html. The table of all PDB codes contained in the ensembles can be found in the supplementary material. The software tool mRAISE is freely available for evaluation purposes and academic use (see http://www.zbh.uni-hamburg.de/raise).

Keywords

Ligand-based Virtual screening Molecular similarity Structural alignment 3D similarity searching Lead discovery 

Supplementary material

10822_2016_9940_MOESM1_ESM.pdf (125 kb)
Supplementary material 1 (pdf 126 KB)

References

  1. 1.
    Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754CrossRefGoogle Scholar
  2. 2.
    Finn PW, Morris GM (2013) Shape-based similarity searching in chemical databases. Wiley Interdiscip Rev Comput Mol Sci 3(3):226–241CrossRefGoogle Scholar
  3. 3.
    Giganti D, Guillemain H, Spadoni JL, Nilges M, Zagury JF, Montes M (2010) Comparative evaluation of 3D virtual ligand screening methods: impact of the molecular alignment on enrichment. J Chem Inf Model 50:992–1004CrossRefGoogle Scholar
  4. 4.
    Kirchmair J, Distinto S, Markt P, Schuster D, Spitzer GM, Liedl KR, Wolber G (2009) How to optimize shape-based virtual screening: choosing the right query and including chemical information. J Chem Inf Model 49:678–692CrossRefGoogle Scholar
  5. 5.
    Grant JA, Gallardo MA, Pickup BT (1996) A fast method of molecular shape comparison: a simple application of a gaussian description of molecular shape. J Comput Chem 17(14):1653–1666CrossRefGoogle Scholar
  6. 6.
    Vainio MJ, Puranen JS, Johnson MS (2009) ShaEP: molecular overlay based on shape and electrostatic potential. J Chem Inf Model 49:492–502CrossRefGoogle Scholar
  7. 7.
    de Lima LAV, Nascimento AS (2013) MolShaCS: a free and open source tool for ligand similarity identification based on Gaussian descriptors. Eur J Med Chem 59:296–303CrossRefGoogle Scholar
  8. 8.
    Roy A, Skolnick J (2015) LIGSIFT: an open-source tool for ligand structural alignment and virtual screening. Bioinformatics 31:539–544CrossRefGoogle Scholar
  9. 9.
    Taminau J, Thijs G, De Winter H (2008) Pharao: pharmacophore alignment and optimization. J Mol Graph Model 27:161–169CrossRefGoogle Scholar
  10. 10.
    Jain AN (2004) Ligand-based structural hypotheses for virtual screening. J Med Chem 47:947–961CrossRefGoogle Scholar
  11. 11.
    Lemmen C, Lengauer T, Klebe G (1998) FLEXS: a method for fast flexible ligand superposition. J Med Chem 41:4502–4520CrossRefGoogle Scholar
  12. 12.
    Abagyan R, Totrov M, Kuznetsov D (1994) Icma new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J Comput Chem 15(5):488–506CrossRefGoogle Scholar
  13. 13.
    Totrov M (2008) Atomic property fields: Generalized 3D pharmacophoric potential for automated ligand superposition, pharmacophore elucidation and 3D QSAR. Chem Biol Drug Des 71(1):15–27CrossRefGoogle Scholar
  14. 14.
    Huang N, Shoichet K, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49(23):6789–6801CrossRefGoogle Scholar
  15. 15.
    Henzler AM, Urbaczek S, Hilbig M, Rarey M (2014) An integrated approach to knowledge-driven structure-based virtual screening. J Comput Aided Mol Des 28:927–939CrossRefGoogle Scholar
  16. 16.
    Schomburg KT, Bietz S, Briem H, Henzler AM, Urbaczek S, Rarey M (2014) Facing the challenges of structure-based target prediction by inverse virtual screening. J Chem Inf Model 54:1676–1686CrossRefGoogle Scholar
  17. 17.
    von Behren MM, Volkamer A, Henzler AM, Schomburg KT, Urbaczek S, Rarey M (2013) Fast protein binding site comparison via an index-based screening technology. J Chem Inf Model 53(2):411–422CrossRefGoogle Scholar
  18. 18.
    Schellhammer I, Rarey M (2007) TrixX: structure-based molecule indexing for large-scale virtual screening in sublinear time. J Comput Aided Mol Des 21:223–238CrossRefGoogle Scholar
  19. 19.
    Scharfer C, Schulz-Gasch T, Hert J, Heinzerling L, Schulz B, Inhester T, Stahl M, Rarey M (2013) CONFECT: conformations from an expert collection of torsion patterns. ChemMedChem 8:1690–1700CrossRefGoogle Scholar
  20. 20.
    Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55:6582–6594CrossRefGoogle Scholar
  21. 21.
    Nittinger E, Schneider N, Lange G, Rarey M (2015) Evidence of water molecules-a statistical evaluation of water molecules based on electron density. J Chem Inf Model 55:771–783CrossRefGoogle Scholar
  22. 22.
    Bietz S, Rarey M (2016) SIENA: efficient compilation of selective protein binding site ensembles. J Chem Inf Model 56:248–259CrossRefGoogle Scholar
  23. 23.
    Hilbig M, Rarey M (2015) MONA 2: a light cheminformatics platform for interactive compound library processing. J Chem Inf Model 55:2071–2078CrossRefGoogle Scholar
  24. 24.
    Stierand K, Maass PC, Rarey M (2006) Molecular complexes at a glance: automated generation of two-dimensional complex diagrams. Bioinformatics 22:1710–1716CrossRefGoogle Scholar
  25. 25.
    OpenEye Scientific Software, Inc. (2015) ROCS method introduction. http://docs.eyesopen.com/rocs/introduction.html. Accessed Feb 2016

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mathias M. von Behren
    • 1
  • Stefan Bietz
    • 1
  • Eva Nittinger
    • 1
  • Matthias Rarey
    • 1
  1. 1.ZBH - Center for BioinformaticsUniversity of HamburgHamburgGermany

Personalised recommendations