Computational tools for the analysis and visualization of multiple protein-ligand complexes

We illustrate how a novel binding-mode similarity metric is able to cluster 20 ligands complexed to HIV-1 reverse transcriptase into distinct groups. The properties of each cluster are then projected onto a group surface as a series of color gradients. Analysis of these surfaces reveals fundamental similarities and differences in the binding modes of these diverse compounds. In addition, the simplicity of the surface representations facilitates the transfer of information between the crystallographer, computational chemist and the chemist. We also show how two- and three-dimensional (2- and 3-D) similarities can be combined to provide enhanced understanding of 33 factor Xa inhibitor complexes. This methodology has enabled us to identify pharmaceutically relevant relationships between ligands and their binding modes that had previously been hidden in a wealth of data.