DOCK 6: Combining techniques to model RNA-small molecule complexes

P. Therese Lang, Scott R. Brozell, Sudipto Mukherjee, Eric F. Pettersen, Elaine C. Meng, Veena Thomas, Robert C. Rizzo, David A. Case, Thomas L. James, Irwin D. Kuntz

Research output: Contribution to journalArticlepeer-review

515 Scopus citations


With an increasing interest in RNA therapeutics and for targeting RNA to treat disease, there is a need for the tools used in protein-based drug design, particularly DOCKing algorithms, to be extended or adapted for nucleic acids. Here, we have compiled a test set of RNA-ligand complexes to validate the ability of the DOCK suite of programs to successfully recreate experimentally determined binding poses. With the optimized parameters and a minimal scoring function, 70% of the test set with less than seven rotatable ligand bonds and 26% of the test set with less than 13 rotatable bonds can be successfully recreated within 2 Å heavy-atom RMSD. When DOCKed conformations are rescored with the implicit solvent models AMBER generalized Born with solvent-accessible surface area (GB/SA) and Poisson-Boltzmann with solvent-accessible surface area (PB/SA) in combination with explicit water molecules and sodium counterions, the success rate increases to 80% with PB/SA for less than seven rotatable bonds and 58% with AMBER GB/SA and 47% with PB/SA for less than 13 rotatable bonds. These results indicate that DOCK can indeed be useful for structure-based drug design aimed at RNA. Our studies also suggest that RNA-directed ligands often differ from typical protein-ligand complexes in their electrostatic properties, but these differences can be accommodated through the choice of potential function. In addition, in the course of the study, we explore a variety of newly added DOCK functions, demonstrating the ease with which new functions can be added to address new scientific questions.

Original languageEnglish (US)
Pages (from-to)1219-1230
Number of pages12
Issue number6
StatePublished - Jun 2009

All Science Journal Classification (ASJC) codes

  • Molecular Biology


  • Binding mode prediction
  • RNA docking
  • Scoring functions
  • Structure-based drug design
  • Validation


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