Literature DB >> 15937897

Comparing protein-ligand docking programs is difficult.

Jason C Cole1, Christopher W Murray, J Willem M Nissink, Richard D Taylor, Robin Taylor.   

Abstract

There is currently great interest in comparing protein-ligand docking programs. A review of recent comparisons shows that it is difficult to draw conclusions of general applicability. Statistical hypothesis testing is required to ensure that differences in pose-prediction success rates and enrichment rates are significant. Numerical measures such as root-mean-square deviation need careful interpretation and may profitably be supplemented by interaction-based measures and visual inspection of dockings. Test sets must be of appropriate diversity and of good experimental reliability. The effects of crystal-packing interactions may be important. The method used for generating starting ligand geometries and positions may have an appreciable effect on docking results. For fair comparison, programs must be given search problems of equal complexity (e.g. binding-site regions of the same size) and approximately equal time in which to solve them. Comparisons based on rescoring require local optimization of the ligand in the space of the new objective function. Re-implementations of published scoring functions may give significantly different results from the originals. Ostensibly minor details in methodology may have a profound influence on headline success rates. (c) 2005 Wiley-Liss, Inc.

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Year:  2005        PMID: 15937897     DOI: 10.1002/prot.20497

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  64 in total

1.  SimiCon: a web tool for protein-ligand model comparison through calculation of equivalent atomic contacts.

Authors:  Manuel Rueda; Vsevolod Katritch; Eugene Raush; Ruben Abagyan
Journal:  Bioinformatics       Date:  2010-09-24       Impact factor: 6.937

2.  Efficient molecular docking of NMR structures: application to HIV-1 protease.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  Protein Sci       Date:  2006-11-22       Impact factor: 6.725

3.  GALAHAD: 1. pharmacophore identification by hypermolecular alignment of ligands in 3D.

Authors:  Nicola J Richmond; Charlene A Abrams; Philippa R N Wolohan; Edmond Abrahamian; Peter Willett; Robert D Clark
Journal:  J Comput Aided Mol Des       Date:  2006-10-19       Impact factor: 3.686

4.  The structural determinants of macrolide-actin binding: in silico insights.

Authors:  James L Melville; Iain H Moal; Charles Baker-Glenn; Peter E Shaw; Gerald Pattenden; Jonathan D Hirst
Journal:  Biophys J       Date:  2007-03-09       Impact factor: 4.033

5.  Construction and test of ligand decoy sets using MDock: community structure-activity resource benchmarks for binding mode prediction.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2011-08-03       Impact factor: 4.956

Review 6.  Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go.

Authors:  N Moitessier; P Englebienne; D Lee; J Lawandi; C R Corbeil
Journal:  Br J Pharmacol       Date:  2007-11-26       Impact factor: 8.739

7.  Evaluating docking programs: keeping the playing field level.

Authors:  John W Liebeschuetz
Journal:  J Comput Aided Mol Des       Date:  2008-01-15       Impact factor: 3.686

Review 8.  Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection--what can we learn from earlier mistakes?

Authors:  Johannes Kirchmair; Patrick Markt; Simona Distinto; Gerhard Wolber; Thierry Langer
Journal:  J Comput Aided Mol Des       Date:  2008-01-15       Impact factor: 3.686

9.  Recipes for the selection of experimental protein conformations for virtual screening.

Authors:  Manuel Rueda; Giovanni Bottegoni; Ruben Abagyan
Journal:  J Chem Inf Model       Date:  2010-01       Impact factor: 4.956

10.  Carborane clusters in computational drug design: a comparative docking evaluation using AutoDock, FlexX, Glide, and Surflex.

Authors:  Rohit Tiwari; Kiran Mahasenan; Ryan Pavlovicz; Chenglong Li; Werner Tjarks
Journal:  J Chem Inf Model       Date:  2009-06       Impact factor: 4.956

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