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Molecular docking screens using comparative models of proteins.

Hao Fan¹, John J Irwin, Benjamin M Webb

¹Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, San Francisco, California 94158, USA.

Journal of Chemical Information and Modeling

|October 23, 2009

Summary

Comparative models offer a vast resource for drug discovery, outnumbering experimentally determined protein structures. This study demonstrates their effectiveness in molecular screening, improving ligand discovery success rates.

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Area of Science:

Structural Biology
Computational Chemistry
Drug Discovery

Background:

Comparative modeling generates significantly more protein sequence data than experimental methods like X-ray crystallography and NMR spectroscopy.
Despite the abundance of comparative models, their use in ligand discovery has been limited due to concerns about model accuracy and potential errors.

Purpose of the Study:

To propose and validate a method for effectively utilizing comparative protein models in large-scale molecular screening for ligand discovery.
To address concerns regarding model errors by developing a consensus-based approach to enhance reliability.

Main Methods:

Developed a docking strategy using a diverse set of crystallographic structures and comparative models with known ligands.
Introduced a "consensus" enrichment method, ranking compounds by their best docking score across multiple models and templates.
Evaluated the enrichment performance of comparative models against established crystallographic structures (holo and apo).

Main Results:

Consensus enrichment using multiple comparative models generally outperformed or matched enrichment from experimental X-ray structures.
Even single comparative models showed significantly better enrichment than paralogous templates with >25% sequence identity.
The proposed method effectively leverages the vastness of comparative modeling for identifying potential drug candidates.

Conclusions:

Comparative models are a valuable and underutilized resource for accelerating ligand discovery in molecular screening.
The consensus enrichment strategy mitigates concerns about individual model inaccuracies, enhancing the reliability of virtual screening.
This approach broadens the scope of targets amenable to structure-based drug design, significantly expanding the potential for discovering novel therapeutics.