The challenge of predicting distal active site mutations in computational enzyme design

Abstract

Many computational enzyme design approaches have been developed in recent years that focus on a reduced set of key enzymatic features. Initial protocols mostly focused on the chemical steps(s) through transition state stabilization, whereas most recent approaches exploit the enzyme conformational dynamics often crucial for substrate binding, product release, and allosteric regulation. The detailed evaluation of the conformational landscape of many laboratory-evolved enzymes has revealed dramatic changes on the relative stabilities of the conformational states after mutation, favoring those conformational states key for the novel functionality. Of note is that these mutations are often located all around the enzyme structure, which contrasts with most of the computational design strategies that reduce the problem into active site alterations. Recent computational strategies have been developed that consider enzyme design as a population shift problem, that is, redistribution of the relative stabilities of the conformational states induced by mutations. These strategies focus on reconstructing the conformational landscape of the enzyme, applying correlation-based tools to elucidate the underlying allosteric network of interactions and identify potential mutation hotspots located at the active site, but most importantly at distal positions for the first time