Computation-guided engineering of distal mutations in an artificial enzyme

Artificial enzymes are valuable biocatalysts able to perform new-to-nature transformations with the precision and (enantio-)selectivity of natural enzymes. Although being highly engineered biocatalysts, they often cannot reach catalytic rates akin those of their natural counterparts, slowing down their application in real-world industrial processes. Typically, their designs only optimise the chemistry inside the active site, while overlooking the role of protein dynamics on catalysis. In this work, we show how the catalytic performance of an already engineered artificial enzyme can be further improved by modulating its long-range network of interactions. To this aim, we subjected a specialised artificial enzyme based on the Lactococcal multidrug resistance regulator (LmrR) to an innovative algorithm that quickly inspects the whole protein sequence space for protein dynamics hotspots. From an initial predicted selection of 73 variants, two variants with mutations distant more than 11 Å showed increased catalytic activity towards the new-to-nature hydrazone formation reaction. Their recombination displayed a 60% higher turnover number and 14 ℃ higher thermostability. Microsecond time scale molecular dynamics simulations evidenced a shift in the distribution of productive enzyme conformations, which are the result of a cascade of interactions initiated by the introduced mutations.