We use cookies to improve your experience. By continuing to browse this site, you accept our cookie policy.×
Published Online:https://doi.org/10.4155/fmc-2016-0115

Aim: Virtual screening selects compounds that resemble a known modulator or compounds that fit into the binding site of a target protein. Computational solvent mapping defines important chemical features for binding to a target protein. Results/methodology: We have tested the ability to use solvent mapping for generating a ‘fake’ ligand that is a negative image of the binding site. We used this fake ligand as a query for the program ROCS and to define the search space of the docking programs FRED and HYBRID. Conclusion: The fake ligands perform comparably to or better than the ligands from crystal structures across a set of ten targets. Thus, the approach is suitable for guiding virtual screening and hit-to-lead optimization.

References

  • 1 Glick M, Jacoby E. The role of computational methods in the identification of bioactive compounds. Curr. Opin. Chem. Biol. 15(4), 540–546 (2011).
  • 2 Nussinov R, Tsai C-J. Allostery in disease and in drug discovery. Cell 153(2), 293–305 (2013).
  • 3 Dennis S, Kortvelyesi T, Vajda S. Computational mapping identifies the binding sites of organic solvents on proteins. Proc. Natl Acad. Sci. USA 99(7), 4290–4295 (2002).
  • 4 Ngan CH, Hall DR, Zerbe B, Grove LE, Kozakov D, Vajda S. FTSite: high accuracy detection of ligand binding sites on unbound protein structures. Bioinformatics 28(2), 286–287 (2012).
  • 5 Hall DR, Kozakov D, Vajda S. Analysis of protein binding sites by computational solvent mapping. Methods Mol. Biol. 819(Chapter 2), 13–27 (2012).
  • 6 Hall DR, Enyedy IJ. Computational solvent mapping in structure-based drug design. Future Med. Chem. 7(3), 337–353 (2015).
  • 7 Rush TS, Grant JA, Mosyak L, Nicholls A. A shape-based 3-D scaffold hopping method and its application to a bacterial protein−protein interaction. J. Med. Chem. 48(5), 1489–1495 (2005).
  • 8 Nicholls A, McGaughey GB, Sheridan RP et al. Molecular shape and medicinal chemistry: a perspective. J. Med. Chem. 53(10), 3862–3886 (2010).
  • 9 Brenke R, Kozakov D, Chuang G-Y et al. Fragment-based identification of druggable “hot spots” of proteins using Fourier domain correlation techniques. Bioinformatics 25(5), 621–627 (2009).
  • 10 Kortemme T, Morozov AV, Baker D. An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein–protein complexes. J. Mol. Biol. 326(4), 1239–1259 (2003).
  • 11 Hawkins PCD, Skillman AG, Nicholls A. Comparison of shape-matching and docking as virtual screening tools. J. Med. Chem. 50(1), 74–82 (2007).
  • 12 McGann M. FRED pose prediction and virtual screening accuracy. J. Chem. Inf. Model. 51(3), 578–596 (2011).
  • 13 McGann M. FRED and HYBRID docking performance on standardized datasets. J. Comput. Aided Mol. Des. 26(8), 897–906 (2012).
  • 14 McGann M, Nicholls A, Enyedy I. The statistics of virtual screening and lead optimization. J. Comput. Aided Mol. Des. 29(10), 923–936 (2015).
  • 15 Receptors – OEDocking. https://docs.eyesopen.com/oedocking/receptor.html.
  • 16 FastRocs. www.eyesopen.com/fastrocs.