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Serine proteases have been proposed to dynamically sample inactive and active conformations, but direct evidence at atomic resolution has remained elusive. Using nuclear magnetic resonance (NMR), we identified a single residue, D164, in exfoliative toxin A (ETA) that acts as a molecular “switch” to regulate global dynamic sampling. Mutations at this site shift the balance between inactive and active states, correlating directly with catalytic activity. Beyond identifying this dynamic switch, we demonstrate how it works in concert with other allosterically coupled sites to rationally control enzyme movements and catalytic function. This study provides a framework for linking conformational dynamics to function and paves the way for engineering enzymes, in particular, proteases, with tailored activities for applications in medicine and biotechnology. 

Suboptimal path analysis in a protein structural or dynamical network becomes increasingly popular for identifying critical residues involved in allosteric communication and regulation. Several software packages have been developed for calculating suboptimal paths, including NetworkView, WISP, and CNAPATH (Bio3D). Although these packages work well for biological systems of moderate sizes, they either dramatically slow down or are subjected to accuracy issues when applied to large systems such as supramolecular complexes. In this work, we develop a new method called SOAN, which implements a modified version of Yen’s algorithm for finding loopless k-shortest paths. Instead of searching the entire protein network, SOAN builds up a subgraph for path calculations based on an initial evaluation of the optimal path and its neighbouring nodes. We test our method on four systems of increasing size and compare it to the NetworkView, WISP and CNAPATH methods. The result shows that SOAN is approximately five times faster than NetworkView and orders of magnitude faster than CNAPATH and WISP. In terms of accuracy, SOAN is comparable to CNAPATH and WISP and superior to NetworkView. We also discuss the influence of SOAN input parameters on performance and suggest optimal values.