Predicting allosteric sites using fast conformational sampling as guided by coarse-grained normal modes.

To computationally identify cryptic binding sites for allosteric modulators, we have developed a fast and simple conformational sampling scheme guided by coarse-grained normal modes solved from the elastic network models followed by atomistic backbone and sidechain reconstruction. Despite the complexity of conformational changes associated with ligand binding, we previously showed that simply sampling along each of the lowest 30 modes can adequately restructure cryptic sites so they are detectable by pocket finding programs like Concavity. Here, we applied this method to study four classical examples of allosteric regulation (GluR2 receptor, GroEL chaperonin, GPCR, and myosin). Our method along with alternative methods has been utilized to locate known allosteric sites and predict new promising allosteric sites. Compared with other sampling methods based on extensive molecular dynamics simulation, our method is both faster (1-2 h for an average-size protein of ∼400 residues) and more flexible (it can be easily integrated with any structure-based pocket finding methods), so it is suitable for high-throughput screening of large datasets of protein structures at the genome scale.