Abstract

Background

Protein-based pharmacophore models are enriched with the information of potential interactions between ligands and the protein target. We have shown in a previous study that protein-based pharmacophore models can be applied for ligand pose prediction and pose ranking. In this publication, we present a new pharmacophore-based docking program PharmDock that combines pose sampling and ranking based on optimized protein-based pharmacophore models with local optimization using an empirical scoring function.

Results

Tests of PharmDock on ligand pose prediction, binding affinity estimation, compound ranking and virtual screening yielded comparable or better performance to existing and widely used docking programs. The docking program comes with an easy-to-use GUI within PyMOL. Two features have been incorporated in the program suite that allow for user-defined guidance of the docking process based on previous experimental data. Docking with those features demonstrated superior performance compared to unbiased docking.

Conclusion

A protein pharmacophore-based docking program, PharmDock, has been made available with a PyMOL plugin. PharmDock and the PyMOL plugin are freely available fromhttp://people.pharmacy.purdue.edu/~mlill/software/pharmdock webcite.

Comments

This is the publisher pdf of Bingjie Hu and Markus A Lill. PharmDock: A Pharmacophore-Based Docking Program. Journal of Cheminformatics 2014, 6:14 and is available at: 10.1186/1758-2946-6-14.

Keywords

Protein pharmacophores; Docking; Scoring; Biased docking; Constraint docking; Confined docking; GUI; PyMOL

Date of this Version

4-16-2014

DOI

10.1186/1758-2946-6-14

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