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Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database
Dariusz Plewczynski1, Michał Łaźniewski, Rafał Augustyniak
1Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Pawinskiego 5a Street, 02-106 Warsaw, Poland. darman@icm.edu.pl
Journal of Computational Chemistry
|September 3, 2010
Summary
This study evaluated seven popular docking programs for drug design, finding that most can accurately predict ligand binding poses. However, a universal scoring function for all molecule and protein types remains elusive.
Area of Science:
- Computational chemistry
- Molecular modeling
- Drug discovery
Background:
- Molecular docking is crucial in drug design for identifying ligand poses and estimating binding affinity.
- Efficient screening of millions of compounds necessitates computationally fast docking algorithms and scoring functions.
- Current docking programs utilize empirical algorithms and simplified scoring to expedite the drug discovery process.
Purpose of the Study:
- To comprehensively evaluate the performance of seven popular docking programs: Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock.
- To assess the accuracy of these programs in predicting ligand binding conformations and estimating binding affinity.
- To provide a large-scale, dual-aspect evaluation of docking software using extensive protein-ligand complex data.
Main Methods:
- Utilized an extensive dataset of 1300 protein-ligand complexes from the PDBbind 2007 database.
- Independently compared ligand posing accuracy using Root Mean Square Deviation (RMSD) against native conformations.
- Assessed scoring function performance by correlating docking scores with experimentally measured binding affinity values.
Main Results:
- The study confirmed that existing docking software can accurately identify ligand binding conformations in most cases.
- A significant lack of a universal scoring function applicable to all molecule types and protein families was observed.
- Performance varied across different docking programs and protein families, highlighting the need for careful software selection.
Conclusions:
- While current docking tools excel at predicting ligand conformations, their scoring functions are not universally applicable.
- Further development is needed to create robust scoring functions that accurately predict binding affinity across diverse molecular and protein targets.
- In vitro validation remains essential to confirm docking-derived predictions in drug discovery pipelines.
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