CB-Dock: a web server for cavity detection-guided protein–ligand blind docking

Abstract

As the number of elucidated protein structures is rapidly increasing, the growing data call for methods to efficiently exploit the structural information for biological and pharmaceutical purposes. Given the three-dimensional (3D) structure of a protein and a ligand, predicting their binding sites and affinity are a key task for computer-aided drug discovery. To address this task, a variety of docking tools have been developed. Most of them focus on docking in the preset binding sites given by users. To automatically predict binding modes without information about binding sites, we developed a user-friendly blind docking web server, named CB-Dock, which predicts binding sites of a given protein and calculates the centers and sizes with a novel curvature-based cavity detection approach, and performs docking with a popular docking program, Autodock Vina. This method was carefully optimized and achieved ~70% success rate for the top-ranking poses whose root mean square deviation (RMSD) were within 2 Å from the X-ray pose, which outperformed the state-of-the-art blind docking tools in our benchmark tests. CB-Dock offers an interactive 3D visualization of results, and is freely available at http://cao.labshare.cn/cb-dock/.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. 1.

    Pagadala NS, Syed K, Tuszynski J. Software for molecular docking: a review. Biophys Rev. 2017;9:91–102.

  2. 2.

    Yuriev E, Holien J, Ramsland PA. Improvements, trends, and new ideas in molecular docking: 2012–2013 in review. J Mol Recognit. 2015;28:581–604.

  3. 3.

    Meiler J, Baker D. ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility. Proteins. 2006;65:538–48.

  4. 4.

    Marialke J, Tietze S, Apostolakis J. Similarity based docking. J Chem Inf Model. 2008;48:186–96.

  5. 5.

    Morris G, Huey R. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2010;30:2785–91.

  6. 6.

    Bolia A, Ozkan SB. Adaptive BP-Dock: an induced fit docking approach for full receptor flexibility. J Chem Inf Model. 2016;56:734–46.

  7. 7.

    Allen WJ, Balius TE, Mukherjee S, Brozell SR, Moustakas DT, Lang PT, et al. DOCK 6: impact of new features and current docking performance. J Comput Chem. 2015;36:1132–56.

  8. 8.

    Liu Z, Su M, Han L, Liu J, Yang Q, Li Y, et al. Forging the basis for developing protein-ligand interaction scoring functions. Acc Chem Res. 2017;50:302–9.

  9. 9.

    Lam PCH, Abagyan R, Totrov M. Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach. J Comput Aided Mol Des. 2018;32:187–98.

  10. 10.

    Padhorny D, Hall DR, Mirzaei H, Mamonov AB, Moghadasi M, Alekseenko A, et al. Protein–ligand docking using FFT based sampling: D3R case study. J Comput Aided Mol Des. 2018;32:225–30.

  11. 11.

    Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol. 1997;267:0–748.

  12. 12.

    Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD. Improved protein-ligand docking using GOLD. Proteins. 2003;52:609–23.

  13. 13.

    Hetényi C, Van Der Spoel D. Blind docking of drug-sized compounds to proteins with up to a thousand residues. FEBS Lett. 2006;580:0–1450.

  14. 14.

    Hetényi C, van der Spoel D. Efficient docking of peptides to proteins without prior knowledge of the binding site. Protein Sci. 2002;11:1729–37.

  15. 15.

    Hassan NM, Alhossary AA, Mu Y, Kwoh CK. Protein-ligand blind docking using QuickVina-W with inter-process spatio-temporal integration. Sci Rep 2017;7:15451.

  16. 16.

    Sánchez-Linares I, Pérez-Sánchez H, Cecilia JM, García JM. High-throughput parallel blind virtual screening using BINDSURF. BMC Bioinformatics 2012;13(Suppl 14):S13.

  17. 17.

    Iorga B, Herlem D, Barré E, Guillou C. Acetylcholine nicotinic receptors: finding the putative binding site of allosteric modulators using the “blind docking” approach. J Mol Model. 2006;12:366–72.

  18. 18.

    Ghersi D, Sanchez R. Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites. Proteins. 2009;74:417–24.

  19. 19.

    Dai W, Wu A, Ma L, Li YX, Jiang T, Li YY. A novel index of protein-protein interface propensity improves interface residue recognition. BMC Syst Biol. 2016;10:381–92.

  20. 20.

    Shin WH, Seok C. GalaxyDock: Protein-ligand docking with flexible protein side-chains. J Chem Inf Model. 2012;52:3225–32.

  21. 21.

    Capra JA, Laskowski RA, Thornton JM, Singh M, Funkhouser TA. Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. PLoS Comput Biol. 2009. https://doi.org/10.1371/journal.pcbi.1000585.

  22. 22.

    Xu Y, Wang S, Hu Q, Gao S, Ma X, Zhang W, et al. CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction. Nucleic Acids Res. 2018;46:W374–W379.

  23. 23.

    Yang J, Roy A, Zhang Y. Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics. 2013;29:2588–95.

  24. 24.

    Levitt DG, Banaszak LJ. POCKET: A computer graphies method for identifying and displaying protein cavities and their surrounding amino acids. J Mol Graph. 1992;10:229.

  25. 25.

    Laskowski RA. SURFNET: A program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph. 1995;13:323–30.

  26. 26.

    Brylinski M, Skolnick J. A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation. Proc Natl Acad Sci USA. 2008;105:129–34.

  27. 27.

    Venkatachalam CM, Jiang X, Oldfield T, Waldman M. LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J Mol Graph Model. 2003;21:289–307.

  28. 28.

    Brylinski M, Feinstein WP. EFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des. 2013;27:551–67.

  29. 29.

    Wu Qi, Peng Zhenling, Yang Zhang JY. COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking. Nucleic Acids Res. 2018;46:313–38.

  30. 30.

    Grosdidier A, Zoete V, Michielin O. Blind docking of 260 protein-ligand complexes with eadock 2.0. J Comput Chem. 2010;30:2021–30.

  31. 31.

    Grosdidier A, Zoete V, Michielin O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res. 2011;39:270–7.

  32. 32.

    Lee HS, Zhang Y. BSP-SLIM: a blind low-resolution ligand-protein docking approach using predicted protein structures. Proteins. 2012;80:93–110.

  33. 33.

    Trott O, Olson AJ. Software news and update AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2009;31:455–61.

  34. 34.

    Liu Z, Li Y, Han L, Li J, Liu J, Zhao Z, et al. PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics. 2015;31:405–12.

  35. 35.

    Hartshorn MJ, Verdonk ML, Chessari G, Brewerton SC, Mooij WTM, Mortenson PN, et al. Diverse, high-quality test set for the validation of protein-ligand docking performance. J Med Chem. 2007;50:726–41.

  36. 36.

    Burley SK, Berman HM, Christie C, Duarte JM, Feng Z, Westbrook J, et al. RCSB Protein Data Bank: Sustaining a living digital data resource that enables breakthroughs in scientific research and biomedical education. Protein Sci. 2018;27:316–30.

  37. 37.

    Labbé CM, Rey J, Lagorce D, Vavruša M, Becot J, Sperandio O, et al. MTiOpenScreen: A web server for structure-based virtual screening. Nucleic Acids Res. 2015;43:448–54.

  38. 38.

    Di Muzio E, Toti D, Polticelli F. DockingApp: a user friendly interface for facilitated docking simulations with AutoDock Vina. J Comput Aided Mol Des. 2017;31:213–8.

  39. 39.

    Feinstein WP, Brylinski M. Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform. 2015;7:1–10.

  40. 40.

    Sotriffer C, Klebe G. Identification and mapping of small-molecule binding sites in proteins: Computational tools for structure-based drug design. Farmaco. 2002;3:243–51.

  41. 41.

    Cao Y, Li L. Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model. Bioinformatics. 2014;30:1674–80.

  42. 42.

    Cao Yang, Wentao Dai ZM. Evaluation of protein–ligand docking by cyscore. Comput Drug Discov Des. 2018;1762:223–32.

  43. 43.

    Rodriguez A, Laio A, Xu R, Wunsch D, Frey BJ, Dueck D. et al.Machine learning. Clustering by fast search and find of density peaks. Science. 2014;344:1492–6.

  44. 44.

    Schmidt T, Haas J, Gallo Cassarino T, Schwede T. Assessment of ligand-binding residue predictions in CASP9. Proteins. 2011;79:126–36.

  45. 45.

    Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, et al. rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Comput Biol. 2014;10:e1003571 https://doi.org/10.1371/journal.pcbi.1003571.

  46. 46.

    Hendlich M, Rippmann F, Barnickel G. LIGSITE: Automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Model. 1997;15:359–63.

  47. 47.

    O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An Open chemical toolbox. J Cheminform. 2011;3:33.

  48. 48.

    Rose AS, Bradley AR, Valasatava Y, Jose M, Prli A, Rose PW. NGL Viewer : Web-based molecular graphics for large complexes. Bioinformatics. 2018;34:3755–8.

  49. 49.

    Schüttelkopf AW, Van Aalten DMF. PRODRG: A tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr Sect D Biol Crystallogr. 2004;60:1355–63.

  50. 50.

    Sánchez-Linares I, Pérez-Sánchez H, Cecilia JM, García JM. High-Throughput parallel blind Virtual Screening using BINDSURF. BMC Bioinformatics. 2012;13:S13 https://doi.org/10.1186/1471-2105-13-S14-S13.

  51. 51.

    Pérot S, Sperandio O, Miteva MA, Camproux AC, Villoutreix BO. Druggable pockets and binding site centric chemical space: A paradigm shift in drug discovery. Drug Discov Today. 2010;15:656–67.

  52. 52.

    Schwardt O, Cutting B, Kolb H, Ernst B. Drug discovery today. Front Med Chem. 2005;3:1–9.

  53. 53.

    Kharkar PS, Warrier S, Gaud RS. Reverse docking: A powerful tool for drug repositioning and drug rescue. Future Med Chem. 2014;6:333–42.

Download references

Acknowledgements

The authors thank Professor Jian-yi Yang of Nankai University for helping in running COACH-D and Dr. Holger Stitz of Johannes Kepler University Linz for his invaluable editing of the manuscript. We also thank Professor Yang Zhang and Cheng-xin Zhang of the University of Michigan, Professor Xiang-jun Du of Sun Yat-sen University and Dr. Zhi-chao Miao of Cambridge University for invaluable discussions. This work was supported by the National Natural Science Foundation of China (Grant numbers 31401130, 81830108, and 81672736), the National Key R&D Program of China (2018YFC0910500), the Shanghai Sailing Program (16YF1408600), the funding for prevention and control technology of African swine fever (2018NZ0151) and the Shanghai Industrial Technology Institute (17CXXF008).

Author information

YL designed and optimized the CB-Dock tool and wrote the manuscript. MG built the CB-Dock web server. WTD benchmarked the program. MCH tested the server. ZXX guided the experiments. YC designed the project and wrote the manuscript.

Correspondence to Yang Cao.

Ethics declarations

Competing interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary figure 1

Supplementary figure 2

Supplementary figure 3

Supplementary table

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Keywords

  • bioinformatics
  • computer-aided design
  • computer-aided drug discovery