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

Abstract

Designed enzymes are of fundamental and technological interest. Experimental directed evolution still has significant limitations, and computational approaches are a complementary route. 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 have now extended the method to design an enzyme for specific transition state binding, i.e., for its catalytic power. We considered methionyl-tRNA synthetase (MetRS), which attaches methionine (Met) to its cognate tRNA, establishing codon identity. Previously, MetRS and other synthetases have been redesigned by experimental directed evolution to accept noncanonical amino acids as substrates, leading to genetic code expansion. Here, we have 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 by the design calculations, and 17 mutants predicted to bind MetAMP were characterized experimentally and all 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 suggest the present method should become the paradigm for computational enzyme design.

Computational optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2

“…9 are biased. However, if the same biasing potentials are used in the design simulations of the apo state and the complex (as done here), the biasing terms cancel out in the binding free energies (18). Thus, Eq.…”

“…We first performed a design simulation of the unbound SARS-CoV-2 RBD domain, in which we derived bias potentials that rendered all allowed chemical types at positions 455, 493, 494, and 501 equiprobable. The procedure is analogous to the Wang-Landau approach (20) and has been described in detail in (17)(18)(19). At a particular simulation time t, the bias potentials have the form E B ðs 1 ðtÞ; s 2 ðtÞ; .…”

“…The resulting biased potentials are approximately equal to the negative folding energies of the apo (A) SARS-CoV-2 RBD state (18). Using the same biasing potentials, a second design simulation is conducted for the CoV-2 complex (C).…”

“…We also perform exhaustive computational sequence design calculations at key positions 455, 493, and 494 and with a narrower set of chemical types at position 501 of the SARS-CoV-2 RBM. The computational protein design calculations employ a physics-based energy function and a recent methodology for the flattening of the sequence space landscape (17)(18)(19) that is closely analogous to the Wang-Landau approach (20). The top affinity sequences contain a glutamic or aspartic acid at position 493 in place of glutamine; nonpolar, aromatic, or polar (asparagine) residues at position 494 in place of serine; and valine or threonine in place of asparagine at position 501.…”

Computational optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2

“…We first performed a design simulation of the unbound SARS-CoV-2 RBD domain, in which we derived bias potentials that rendered all allowed chemical types at positions 455, 493, 494 and 501 equiprobable. The procedure is analogous to the Wang-Landau approach (20) and has been described in detail in refs (17)(18)(19). At a particular simulation time t, the bias potentials have the form…”