Rational drug design efforts typically focus on identifying inhibitors that bind to protein active sites. Pockets that are not present in crystallographic structures yet can exert allosteric (i.e., long-range) control over distant active sites present an exciting alternative. However, identifying these hidden allosteric sites is extremely challenging because one usually has to simultaneously find a small molecule that binds to and stabilizes the open conformation of the pocket. In our new PNAS paper, the Bowman lab presents a means of combining advances in computer modeling—using Folding@home and Markov state models to capture long timescale dynamics—with biophysical experiments to identify hidden allosteric sites without requiring the simultaneous discovery of drug-like compounds that bind them. Using this technology, we discover multiple hidden allosteric sites in a single protein.
