BINANA: A novel algorithm for ligand-binding characterization

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 http://www.nbcr.net/binana/. 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.

Graphical abstractFigure optionsDownload full-size imageDownload high-quality image (265 K)Download as PowerPoint slideResearch highlights► We here present a novel Python-implemented computer algorithm called BINANA (BINding ANAlyzer). ► BINANA can be used to automatically characterize ligand–receptor complexes for key binding characteristics like hydrogen-bond, hydrophobic, salt-bridge, van der Waals, and other interactions. ► As this characterization can be tedious when performed manually, especially when many complexes need be analyzed, we are hopeful that BINANA will be a useful tool for both computational chemists and structural biologists.