Design of efficient artificial enzymes using crystallographically-enhanced conformational sampling

The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing catalytic efficiency of de novo enzymes to a level comparable to their natural counterparts without relying on directed evolution. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (k(cat)/K(M) 125 M(−1) s(−1)) and KE70 (k(cat)/K(M) 57 M(−1) s(−1)), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display k(cat)/K(M) values improved by 100–250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models. Our work shows how computational design can generate efficient artificial enzymes by exploiting the true conformational ensemble to more effectively stabilize the transition state.