Small molecule drugs may interact with many proteins. Some of these interactions may cause unexpected effects, including side effects or potentially useful therapeutic effects. One way to predict these effects is to analyze the three-dimensional structure of target proteins, and identify new binding sites for small molecule drugs. Several methods have been proposed for predicting new binding sites, relying on geometric and functional complementarity of the sites and the small molecules.
Stanford researchers have come up with an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared micro environments. PocketFEATURE is a new method for identifying novel protein-drug interactions. It seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). It has proved that geometric flexibility is useful for effectively comparing sites.