Literature DB >> 18196461

Evaluating docking programs: keeping the playing field level.

John W Liebeschuetz1.   

Abstract

Over recent years many enrichment studies have been published which purport to rigorously compare the performance of two or more docking protocols. It has become clear however that such studies often have flaws within their methodologies, which cast doubt on the rigour of the conclusions. Setting up such comparisons is fraught with difficulties and no best mode of practice is available to guide the experimenter. Careful choice of structural models and ligands appropriate to those models is important. The protein structure should be representative for the target. In addition the set of active ligands selected should be appropriate to the structure in cases where different forms of the protein bind different classes of ligand. Binding site definition is also an area in which errors arise. Particular care is needed in deciding which crystallographic waters to retain and again this may be predicated by knowledge of the likely binding modes of the ligands making up the active ligand list. Geometric integrity of the ligand structures used is clearly important yet it is apparent that published sets of actives + decoys may contain sometimes high proportions of incorrect structures. Choice of protocol for docking and analysis needs careful consideration as many programs can be tweaked for optimum performance. Should studies be run using 'black box' protocols supplied by the software provider? Lastly, the correct method of analysis of enrichment studies is a much discussed topic at the moment. However currently promoted approaches do not consider a crucial aspect of a successful virtual screen, namely that a good structural diversity of hits be returned. Overall there is much to consider in the experimental design of enrichment studies. Hopefully this study will be of benefit in helping others plan such experiments.

Mesh:

Year:  2008        PMID: 18196461     DOI: 10.1007/s10822-008-9169-8

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  22 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Protocols for bridging the peptide to nonpeptide gap in topological similarity searches.

Authors:  R P Sheridan; S B Singh; E M Fluder; S K Kearsley
Journal:  J Chem Inf Comput Sci       Date:  2001 Sep-Oct

3.  Further development of reduced graphs for identifying bioactive compounds.

Authors:  Edward J Barker; Eleanor J Gardiner; Valerie J Gillet; Paula Kitts; Jeff Morris
Journal:  J Chem Inf Comput Sci       Date:  2003 Mar-Apr

4.  Comparative evaluation of eight docking tools for docking and virtual screening accuracy.

Authors:  Esther Kellenberger; Jordi Rodrigo; Pascal Muller; Didier Rognan
Journal:  Proteins       Date:  2004-11-01

Review 5.  Comparing protein-ligand docking programs is difficult.

Authors:  Jason C Cole; Christopher W Murray; J Willem M Nissink; Richard D Taylor; Robin Taylor
Journal:  Proteins       Date:  2005-08-15

6.  A critical assessment of docking programs and scoring functions.

Authors:  Gregory L Warren; C Webster Andrews; Anna-Maria Capelli; Brian Clarke; Judith LaLonde; Millard H Lambert; Mika Lindvall; Neysa Nevins; Simon F Semus; Stefan Senger; Giovanna Tedesco; Ian D Wall; James M Woolven; Catherine E Peishoff; Martha S Head
Journal:  J Med Chem       Date:  2006-10-05       Impact factor: 7.446

7.  Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes.

Authors:  Richard A Friesner; Robert B Murphy; Matthew P Repasky; Leah L Frye; Jeremy R Greenwood; Thomas A Halgren; Paul C Sanschagrin; Daniel T Mainz
Journal:  J Med Chem       Date:  2006-10-19       Impact factor: 7.446

8.  Evaluations of molecular docking programs for virtual screening.

Authors:  Kenji Onodera; Kazuhito Satou; Hiroshi Hirota
Journal:  J Chem Inf Model       Date:  2007-06-28       Impact factor: 4.956

9.  Design and synthesis of a series of potent and orally bioavailable noncovalent thrombin inhibitors that utilize nonbasic groups in the P1 position.

Authors:  T J Tucker; S F Brady; W C Lumma; S D Lewis; S J Gardell; A M Naylor-Olsen; Y Yan; J T Sisko; K J Stauffer; B J Lucas; J J Lynch; J J Cook; M T Stranieri; M A Holahan; E A Lyle; E P Baskin; I W Chen; K B Dancheck; J A Krueger; C M Cooper; J P Vacca
Journal:  J Med Chem       Date:  1998-08-13       Impact factor: 7.446

10.  Development and validation of a genetic algorithm for flexible docking.

Authors:  G Jones; P Willett; R C Glen; A R Leach; R Taylor
Journal:  J Mol Biol       Date:  1997-04-04       Impact factor: 5.469

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  5 in total

1.  Novel human mPGES-1 inhibitors identified through structure-based virtual screening.

Authors:  Adel Hamza; Xinyun Zhao; Min Tong; Hsin-Hsiung Tai; Chang-Guo Zhan
Journal:  Bioorg Med Chem       Date:  2011-08-25       Impact factor: 3.641

2.  Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation.

Authors:  Ajay N Jain
Journal:  J Comput Aided Mol Des       Date:  2009-04-02       Impact factor: 3.686

Review 3.  Recommendations for evaluation of computational methods.

Authors:  Ajay N Jain; Anthony Nicholls
Journal:  J Comput Aided Mol Des       Date:  2008-03-13       Impact factor: 3.686

4.  Electrostatic-field and surface-shape similarity for virtual screening and pose prediction.

Authors:  Ann E Cleves; Stephen R Johnson; Ajay N Jain
Journal:  J Comput Aided Mol Des       Date:  2019-10-24       Impact factor: 3.686

5.  Comparing neural-network scoring functions and the state of the art: applications to common library screening.

Authors:  Jacob D Durrant; Aaron J Friedman; Kathleen E Rogers; J Andrew McCammon
Journal:  J Chem Inf Model       Date:  2013-07-11       Impact factor: 4.956

  5 in total

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