Evaluation of DOCK 6 as a pose generation and database enrichment tool

In conjunction with the recent American Chemical Society symposium titled “Docking and Scoring: A Review of Docking Programs” the performance of the DOCK6 program was evaluated through (1) pose reproduction and (2) database enrichment calculations on a common set of organizer-specified systems and datasets (ASTEX, DUD, WOMBAT). Representative baseline grid score results averaged over five docking runs yield a relatively high pose identification success rate of 72.5 % (symmetry corrected rmsd) and sampling rate of 91.9 % for the multi site ASTEX set (N = 147) using organizer-supplied structures. Numerous additional docking experiments showed that ligand starting conditions, symmetry, multiple binding sites, clustering, and receptor preparation protocols all affect success. Encouragingly, in some cases, use of more sophisticated scoring and sampling methods yielded results which were comparable (Amber score ligand movable protocol) or exceeded (LMOD score) analogous baseline grid-score results. The analysis highlights the potential benefit and challenges associated with including receptor flexibility and indicates that different scoring functions have system dependent strengths and weaknesses. Enrichment studies with the DUD database prepared using the SB2010 preparation protocol and native ligand pairings yielded individual area under the curve (AUC) values derived from receiver operating characteristic curve analysis ranging from 0.29 (bad enrichment) to 0.96 (good enrichment) with an average value of 0.60 (27/38 have AUC ≥ 0.5). Strong early enrichment was also observed in the critically important 1.0–2.0 % region. Somewhat surprisingly, an alternative receptor preparation protocol yielded comparable results. As expected, semi-random pairings yielded poorer enrichments, in particular, for unrelated receptors. Overall, the breadth and number of experiments performed provide a useful snapshot of current capabilities of DOCK6 as well as starting points to guide future development efforts to further improve sampling and scoring.

[1]  D. Case,et al.  Exploring protein native states and large‐scale conformational changes with a modified generalized born model , 2004, Proteins.

[2]  Peter Willett,et al.  Maximum common subgraph isomorphism algorithms for the matching of chemical structures , 2002, J. Comput. Aided Mol. Des..

[3]  I. Kuntz,et al.  Inclusion of Solvation in Ligand Binding Free Energy Calculations Using the Generalized-Born Model , 1999 .

[4]  J. Irwin,et al.  ZINC ? A Free Database of Commercially Available Compounds for Virtual Screening. , 2005 .

[5]  Jörg Weiser,et al.  Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO) , 1999, J. Comput. Chem..

[6]  David A. Case,et al.  Modeling Unusual Nucleic Acid Structures , 1998 .

[7]  J. Irwin,et al.  Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.

[8]  Tingjun Hou,et al.  Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations , 2011, J. Chem. Inf. Model..

[9]  W. L. Jorgensen.  The Many Roles of Computation in Drug Discovery , 2004, Science.

[10]  D. Case,et al.  Rescoring docking hit lists for model cavity sites: predictions and experimental testing. , 2008, Journal of molecular biology.

[11]  Ricky Chachra,et al.  Origins of Resistance Conferred by the R292K Neuraminidase Mutation via Molecular Dynamics and Free Energy Calculations. , 2008, Journal of chemical theory and computation.

[12]  Christine Humblet,et al.  Investigation of MM-PBSA Rescoring of Docking Poses , 2008, J. Chem. Inf. Model..

[13]  Sudipto Mukherjee,et al.  Implementation and evaluation of a docking‐rescoring method using molecular footprint comparisons , 2011, J. Comput. Chem..

[14]  Pang-Ning Tan,et al.  Receiver Operating Characteristic , 2009, Encyclopedia of Database Systems.

[15]  Tudor I. Oprea,et al.  Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection? , 2008, J. Comput. Aided Mol. Des..

[16]  Christopher I. Bayly,et al.  Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem , 2007, J. Chem. Inf. Model..

[17]  J. Munkres.  ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

[18]  Sudipto Mukherjee,et al.  Docking Validation Resources: Protein Family and Ligand Flexibility Experiments , 2010, J. Chem. Inf. Model..

[19]  P. Kollman,et al.  Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. , 2000, Accounts of chemical research.

[20]  J. Pin,et al.  Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. , 2005, Journal of medicinal chemistry.

[21]  I. Kuntz,et al.  DOCK 6: combining techniques to model RNA-small molecule complexes. , 2009, RNA.

[22]  F. A. Neugebauer,et al.  Electrochemical oxidation and structural changes of 5,6-dihydrobenzo[c]cinnolines , 1996 .

[23]  Gregory D. Hawkins,et al.  Parametrized Models of Aqueous Free Energies of Solvation Based on Pairwise Descreening of Solute Atomic Charges from a Dielectric Medium , 1996 .

[24]  B. Kuhn,et al.  Validation and use of the MM-PBSA approach for drug discovery. , 2005, Journal of medicinal chemistry.

[25]  P. Kollman,et al.  Continuum Solvent Studies of the Stability of DNA, RNA, and Phosphoramidate−DNA Helices , 1998 .

[26]  J. Gasteiger,et al.  ITERATIVE PARTIAL EQUALIZATION OF ORBITAL ELECTRONEGATIVITY – A RAPID ACCESS TO ATOMIC CHARGES , 1980 .

[27]  Yongbo Hu,et al.  Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy , 2009, J. Chem. Inf. Model..

[28]  H. Kuhn.  The Hungarian method for the assignment problem , 1955 .

[29]  I. Kuntz,et al.  Pairwise GB/SA Scoring Function for Structure-based Drug Design , 2004 .

[30]  Brian K. Shoichet,et al.  Virtual screening of chemical libraries , 2004, Nature.

[31]  Ajay N. Jain,et al.  Recommendations for evaluation of computational methods , 2008, J. Comput. Aided Mol. Des..

[32]  G. Klebe.  Virtual ligand screening: strategies, perspectives and limitations , 2006, Drug Discovery Today.

[33]  I. Kuntz,et al.  Structure-based design of nonpeptide inhibitors specific for the human immunodeficiency virus 1 protease. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Paul N. Mortenson,et al.  Diverse, high-quality test set for the validation of protein-ligand docking performance. , 2007, Journal of medicinal chemistry.

[35]  Irwin D. Kuntz,et al.  Development and validation of a modular, extensible docking program: DOCK 5 , 2006, J. Comput. Aided Mol. Des..

[36]  J. Andrew Grant,et al.  A smooth permittivity function for Poisson–Boltzmann solvation methods , 2001, J. Comput. Chem..

[37]  H. Nar,et al.  Structural basis for inhibition promiscuity of dual specific thrombin and factor Xa blood coagulation inhibitors. , 2001, Structure.

[38]  J M Blaney,et al.  A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.

[39]  V. Hornak,et al.  Comparison of multiple Amber force fields and development of improved protein backbone parameters , 2006, Proteins.

[40]  Todd J. A. Ewing,et al.  DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases , 2001, J. Comput. Aided Mol. Des..

[41]  I. Kuntz,et al.  Automated docking with grid‐based energy evaluation , 1992 .

[42]  Todd J. A. Ewing,et al.  Critical evaluation of search algorithms for automated molecular docking and database screening , 1997, J. Comput. Chem..

[43]  I. Kuntz.  Structure-Based Strategies for Drug Design and Discovery , 1992, Science.

[44]  Robert P. Sheridan,et al.  Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results , 2008, J. Comput. Aided Mol. Des..

[45]  A. Spada,et al.  Crystal structures of human factor Xa complexed with potent inhibitors. , 2000, Journal of medicinal chemistry.

[46]  Giulio Rastelli,et al.  Fast and accurate predictions of binding free energies using MM‐PBSA and MM‐GBSA , 2009, J. Comput. Chem..

[47]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[48]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[49]  István Kolossváry,et al.  Low Mode Search . An Efficient , Automated Computational Method for Conformational Analysis : Application to Cyclic and Acyclic Alkanes and Cyclic Peptides , 1997 .