Decoupling of catalysis and transition state analog binding from mutations throughout a phosphatase revealed by high-throughput enzymology

Abstract

Using high-throughput microfluidic enzyme kinetics (HT-MEK), we measured over 9,000 inhibition curves detailing impacts of 1,004 single-site mutations throughout the alkaline phosphatase PafA on binding affinity for two transition state analogs (TSAs), vanadate and tungstate. As predicted by catalytic models invoking transition state complementary, mutations to active site and active-site-contacting residues had highly similar impacts on catalysis and TSA binding. Unexpectedly, most mutations to more distal residues that reduced catalysis had little or no impact on TSA binding and many even increased tungstate affinity. These disparate effects can be accounted for by a model in which distal mutations alter the enzyme’s conformational landscape, increasing the occupancy of microstates that are catalytically less effective but better able to accommodate larger transition state analogs. In support of this ensemble model, glycine substitutions (rather than valine) were more likely to increase tungstate affinity (but not more likely to impact catalysis), presumably due to increased conformational flexibility that allows previously disfavored microstates to increase in occupancy. These results indicate that residues throughout an enzyme provide specificity for the transition state and discriminate against analogs that are larger only by tenths of an Ångström. Thus, engineering enzymes that rival the most powerful natural enzymes will likely require consideration of distal residues that shape the enzyme’s conformational landscape and fine-tune active-site residues. Biologically, the evolution of extensive communication between the active site and remote residues to aid catalysis may have provided the foundation for allostery to make it a highly evolvable trait.

Structural enzymology studies with the substrate 3S‐hydroxybutanoyl‐CoA: bifunctional MFE1 is a less efficient dehydrogenase than monofunctional HAD

“…As discussed above, for hlADH the conformational switch related to the dissociation of the NADH from the closed form of the ternary complex of hlADH is proposed to be the rate limiting step in the overall dehydrogenase reaction catalyzed by this dehydrogenase [ 36 ]. It is known that the dynamical properties of enzymes are important for their catalytic properties and that these dynamical properties are influenced by structural features far away from the catalytic site [ 41 , 42 , 43 , 44 ]. Therefore, it can be inferred that in MFE1 the dynamical properties of domain C with respect to domain D will be different from HsHAD, due to the presence of the linker helix, which will effect the catalytic properties of its HAD active site, being shaped by its C‐ and D‐domains.…”

Decreased Transition-State Analogue Affinity in Isotopically Heavy MTAN with Increased Catalysis

“…1,26,27 For example, a near-saturation mutagenesis of alkaline phosphatase (PafA) indicated that mutations in catalytic site residues had similar effects on catalysis and transition-state analogue binding, although remote mutations did not. 28 Mass-altered enzymes are probes of dynamic motion as the electron configuration is unaltered and the protein geometry is conserved. Catalytic perturbations are therefore interpreted as coming from the dynamic motions surrounding the transition state.…”

“…In thermodynamic descriptions of catalysis, the affinity of transition-state (and transition-state analogue) binding is interpreted as being proportional to the rate of chemistry. Thus, higher affinity for the transition state, and transition-state analogues, often corresponds to faster catalysis. ,, For example, a near-saturation mutagenesis of alkaline phosphatase (PafA) indicated that mutations in catalytic site residues had similar effects on catalysis and transition-state analogue binding, although remote mutations did not . Mass-altered enzymes are probes of dynamic motion as the electron configuration is unaltered and the protein geometry is conserved.…”

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

“…Ultimately, combinatorial mutagenesis data sets on additional protein families are necessary for understanding when MLDE is useful. In addition to developing high-throughput assays to map protein sequences to fitnesses, − it will be important to develop general and realistic mathematical models to describe protein fitness landscapes (Figure A). ,− …”