Adaptive landscape flattening allows the design of both enzyme:substrate binding and catalytic power

Abstract

AbstractDesigned enzymes are of fundamental and technological interest. Experimental directed evolution still has significant limitations, and computational approaches are complementary. A designed enzyme should satisfy multiple criteria: stability, substrate binding, transition state binding. Such multi-objective design is computationally challenging. Two recent studies used adaptive importance sampling Monte Carlo to redesign proteins for ligand binding. By first flattening the energy landscape of the apo protein, they obtained positive design for the bound state and negative design for the unbound. We extend the method to the design of an enzyme for specific transition state binding, i.e., for catalytic power. We consider methionyl-tRNA synthetase (MetRS), which attaches methionine (Met) to its cognate tRNA, establishing codon identity. MetRS and other synthetases have been extensively redesigned by experimental directed evolution to accept noncanonical amino acids as substrates, leading to genetic code expansion. We redesigned MetRS computationally to bind several ligands: the Met analog azidonorleucine, methionyl-adenylate (MetAMP), and the activated ligands that form the transition state for MetAMP production. Enzyme mutants known to have azidonorleucine activity were recovered, and mutants predicted to bind MetAMP were characterized experimentally and found to be active. Mutants predicted to have low activation free energies for MetAMP production were found to be active and the predicted reaction rates agreed well with the experimental values. We expect the present method will become the paradigm for computational enzyme design.