Title
Maximum Common Binding Modes (MCBM): Consensus Docking Scoring Using Multiple Ligand Information and Interaction Fingerprints.
Abstract
Improving the scoring functions for small molecule-protein docking is a highly challenging task in current computational drug design. Here we present a novel consensus scoring concept for the prediction of binding modes for multiple known active ligands. Similar ligands are generally believed to bind to their receptor in a similar fashion. The presumption of our approach was that the true binding modes of similar ligands should be more similar to each other compared to false positive binding modes. The number of conserved (consensus) interactions between similar ligands was used as a docking score. Patterns of interactions were modeled using ligand receptor interaction fingerprints. Our approach was evaluated for four different data sets of known cocrystal structures (CDK-2, dihydrofolate reductase, HIV-1 protease, and thrombin). Docking poses were generated with FlexX and rescored by our approach. For comparison the CScore scoring functions from Sybyl were used, and consensus scores were calculated thereof. Our approach performed better than individual scoring functions and was comparable to consensus scoring. Analysis of the distribution of docking poses by self-organizing maps (SOM) and interaction fingerprints confirmed that clusters of docking poses composed of multiple ligands were preferentially observed near the native binding mode. Being conceptually unrelated to commonly used docking scoring functions our approach provides a powerful method to complement and improve computational docking experiments.
Year
DOI
Venue
2008
10.1021/ci7003626
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Field
DocType
Volume
Dihydrofolate reductase,Plasma protein binding,Binding site,Tetrahydrofolate dehydrogenase,Docking (dog),Ligand,Chemistry,Bioinformatics,Scoring functions for docking
Journal
48
Issue
ISSN
Citations 
2
1549-9596
7
PageRank 
References 
Authors
0.58
0
4
Name
Order
Citations
PageRank
Steffen Renner1645.18
Swetlana Derksen23215.38
Sebastian Radestock3182.40
Fabian Mörchen437217.94