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J Mol Graph Model. 2011 Apr;29(6):888-93. doi: 10.1016/j.jmgm.2011.01.004. Epub 2011 Jan 19.

BINANA: a novel algorithm for ligand-binding characterization.

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Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, CA 92093-0365, United States.


Computational chemists and structural biologists are often interested in characterizing ligand-receptor complexes for hydrogen-bond, hydrophobic, salt-bridge, van der Waals, and other interactions in order to assess ligand binding. When done by hand, this characterization can become tedious, especially when many complexes need be analyzed. In order to facilitate the characterization of ligand binding, we here present a novel Python-implemented computer algorithm called BINANA (BINding ANAlyzer), which is freely available for download at To demonstrate the utility of the new algorithm, we use BINANA to confirm that the number of hydrophobic contacts between a ligand and its protein receptor is positively correlated with ligand potency. Additionally, we show how BINANA can be used to search through a large ligand-receptor database to identify those complexes that are remarkable for selected binding features, and to identify lead candidates from a virtual screen with specific, desirable binding characteristics. We are hopeful that BINANA will be useful to computational chemists and structural biologists who wish to automatically characterize many ligand-receptor complexes for key binding characteristics.

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