Combining Markov state models and experiments to find new druggable sites

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.