Computational protein–ligand docking and virtual drug screening with the AutoDock suite

Journal name:
Nature Protocols
Volume:
11,
Pages:
905–919
Year published:
DOI:
doi:10.1038/nprot.2016.051
Published online

Abstract

Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration. The entire protocol will require ~5 h.

At a glance

Figures

  1. AutoDockTools (ADT).
    Figure 1: AutoDockTools (ADT).

    ADT is built within the Python Molecule Viewer (PMV). ADT commands for coordinate preparation, docking and analysis are available through menus on the lower toolbar. PMV commands for higher-level visualization are available on the upper tool bar. The Dashboard controls representation, colors and labeling of molecular objects that are displayed in the Interactive Viewer panel. For more information on the capabilities of PMV and ADT, see http://mgltools.scripps.edu/documentation.

  2. Results of docking imatinib to its receptor in bound and apo conformations.
    Figure 2: Results of docking imatinib to its receptor in bound and apo conformations.

    (a) Re-docking of flexible imatinib to rigid Abl (PDB entry 1iep) using AutoDock (pink) and AutoDock Vina (green). The X-ray crystallographic ligand position is colored by atom type. (b) Cross-docking of flexible imatinib to Abl (PDB entry 1fpu) with a single flexible residue side chain using AutoDock Vina (green). Note that Vina does not retain hydrogen atom positions during docking, so the threonine hydroxyl hydrogen is placed in a random position in the docked coordinate set.

  3. Raccoon2.
    Figure 3: Raccoon2.

    Tabs at the top allow the user to choose each of the steps for setting up, running and analyzing a virtual screen.

  4. Raccoon result filtering.
    Figure 4: Raccoon result filtering.

    The 'Data Source' window (top) has several options for filtering the results of a virtual screen. Two interaction filters are applied here. The 'Visualization' window (bottom) allows visualization of the filtered results, and checkboxes can be used to select the desired compounds for export.

  5. AutoLigand results.
    Figure 5: AutoLigand results.

    c-Abl is analyzed with AutoLigand. (a) c-Abl with imatinib. (b) Carbon affinity map calculated around the protein. Notice the many disconnected areas of strong affinity. (c) 100-point envelope identifies the regions of the active site that provide the strongest affinity for the drug. (d) 400-point envelope includes the region of high affinity and also extends into adjacent solvent-accessible regions on the protein surface.

  6. Docking with explicit hydration.
    Figure 6: Docking with explicit hydration.

    (a) Crystallographic structure of acetylcholine-binding protein with nicotine. An ordered water molecule (red sphere) mediates interaction with the protein. (b) The default protocol in AutoDock finds two conformations of the pyridine ring with equal predicted energies. (c) Hydrated docking predicts the observed conformation. The second water molecule, marked with an X, is included during the docking but is removed because it clashes with the protein.

Accession codes

Referenced accessions

Protein Data Bank

References

  1. Trott, O. & Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455461 (2010).
  2. Goodsell, D.S. & Olson, A.J. Automated docking of substrates to proteins by simulated annealing. Proteins 8, 195202 (1990).
  3. Morris, G.M. et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19, 16391662 (1998).
  4. Forli, S. Raccoon for Processing Virtual Screening <http://autodock.scripps.edu/resources/raccoon> (2013).
  5. Morris, G.M. et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30, 27852791 (2009).
  6. Harris, R., Olson, A.J. & Goodsell, D.S. Automated prediction of ligand-binding sites in proteins. Proteins 70, 15061517 (2008).
  7. Sousa, S.F. et al. Protein-ligand docking in the new millennium—a retrospective of 10 years in the field. Curr. Med. Chem. 20, 22962314 (2013).
  8. Goodsell, D.S., Lauble, H., Stout, C.D. & Olson, A.J. Automated docking in crystallography: analysis of the substrates of aconitase. Proteins 17, 110 (1993).
  9. Goodsell, D.S., Morris, G.M. & Olson, A.J. Automated docking of flexible ligands: applications of AutoDock. J. Mol. Recognit. 9, 15 (1996).
  10. Soares, T.A., Goodsell, D.S., Briggs, J.M., Ferreira, R. & Olson, A.J. Docking of 4-oxalocrotonate tautomerase substrates: implications for the catalytic mechanism. Biolpolymers 50, 319328 (1999).
  11. Rosenfeld, R.J. et al. Automated docking of ligands to an artificial active site: augmenting crystallographic analysis with computer modeling. J. Comput. Aided Mol. Des. 17, 525536 (2003).
  12. Brik, A. et al. Rapid diversity-oriented synthesis in microtiter plates for in situ screening of HIV protease inhibitors. ChemBioChem 4, 12461248 (2003).
  13. Brik, A. et al. 1,2,3-Triazole as a peptide surrogate in the rapid synthesis of HIV-1 protease inhibitors. ChemBioChem 6, 11671169 (2005).
  14. Perryman, A.L. et al. Fragment-based screen against HIV protease. Chem. Biol. Drug Des. 75, 257268 (2010).
  15. Cosconati, S. et al. Structure-based virtual screening and biological evaluation of Mycobacterium tuberculosis adenosine 5′-phosphosulfate reductase inhibitors. J. Med. Chem. 51, 66276630 (2008).
  16. Perryman, A.L. et al. A virtual screen discovers novel, fragment-sized inhibitors of Mycobacterium tuberculosis InhA. J. Chem. Inf. Model. 55, 645659 (2015).
  17. Cosconati, S. et al. Identification of novel beta-secretase inhibitors through inclusion of protein flexibility in virtual screening calculations. FASEB J. 22, 791798 (2008).
  18. Cosconati, S. et al. Protein flexibility in virtual screening: the BACE-1 case study. J. Chem. Inf. Model. 52, 26972704 (2012).
  19. Jiang, H. et al. Stabilizers of the Max homodimer identified in virtual ligand screening inhibit Myc function. Mol. Pharmacol. 76, 491502 (2009).
  20. Leonard, S.E., Garcia, F.J., Goodsell, D.S. & Carroll, K.S. Redox-based probes for protein tyrosine phosphatases. Angew. Chem. Int. Ed. Engl. 50, 44234427 (2011).
  21. Huey, R., Goodsell, D.S., Morris, G.M. & Olson, A.J. Grid-based hydrogen bond potentials in improved directionality. Lett. Drug Des. Discov. 1, 178183 (2004).
  22. Huey, R., Morris, G.M., Olson, A.J. & Goodsell, D.S. A semiempirical free energy force field with charge-based desolvation. J. Comput. Chem. 28, 11451152 (2006).
  23. Forli, S. & Olson, A.J. A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking. J. Med. Chem. 55, 623638 (2012).
  24. Bianco, G., Forli, S., Goodsell, D.S. & Olson, A.J. Covalent docking using autodock: two-point attractor and flexible side chain methods. Protein Sci. 25, 295301 (2016).
  25. Cosconati, S. et al. Virtual screening with AutoDock: theory and practice. Expert Opin. Drug Discov. 5, 597607 (2010).
  26. Lin, J.H., Perryman, A.L., Schames, J.R. & McCammon, J.A. The relaxed complex method: accommodating receptor flexibility for drug design with an improved scoring scheme. Biopolymers 68, 4762 (2003).
  27. Nagar, B. et al. Crystal structures of the kinase domain of c-Abl in complex with the small molecule inhibitors PD173955 and imatinib (STI-571). Cancer Res. 62, 42364243 (2002).
  28. Schindler, T. et al. Structural mechanism for STI-571 inhibition of abelson tyrosine kinase. Science 289, 19381942 (2000).

Download references

Author information

Affiliations

  1. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.

    • Stefano Forli,
    • Ruth Huey,
    • Michael E Pique,
    • Michel F Sanner,
    • David S Goodsell &
    • Arthur J Olson

Contributions

All authors contributed equally to this work. D.S.G. and S.F. authored the protocol manuscript with extensive input from the other authors, based on tutorials developed by all authors. All authors have been instrumental in development of the AutoDock suite and training of users.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Supplementary information

Additional data