Highly proficient, promiscuous enzymes can be springboards for functional evolution, able to avoid loss of function during adaptation by their capacity to promote multiple reactions. We employ systematic comparative study of structure, sequence and substrate specificity to track the evolution of specificity and reactivity between promiscuous members of clades of the alkaline phosphatase (AP) superfamily. Construction of a phylogenetic tree of protein sequences maps out the likely transition zone between arylsulfatases (ASs) and phosphonate monoester hydrolases (PMHs). Kinetic analysis shows that all enzymes characterized have four chemically distinct phospho-and sulfoesterase activities, with rate accelerations ranging from 10 11-10 17-fold for their primary and 10 9-10 12-fold for their promiscuous reactions, suggesting that catalytic promiscuity is widespread in the AP-superfamily. This functional characterization and crystallography reveal a novel class of ASs that is so similar in sequence to known PMHs that it had not been recognized as having diverged in function. Based on analysis of snapshots of catalytic promiscuity 'in transition' we develop possible models that would allow functional evolution and determine scenarios for trade-off between multiple activities. For the new ASs we observe largely invariant substrate specificity that would facilitate the transition from ASs to PMHs via trade-off-free molecular exaptation, i.e. evolution without initial loss of primary activity and specificity toward the original substrate. This ability to bypass low activity generalists provides a molecular solution to avoid adaptive conflict.

Catalytic promiscuity can facilitate evolution of enzyme functions—a multifunctional catalyst may act as a springboard for efficient functional adaptation. We test the effect of single mutations on multiple activities in two groups of promiscuous AP superfamily members to probe this hypothesis. We quantify the effect of site‐saturating mutagenesis of an analogous, nucleophile‐flanking residue in two superfamily members: an arylsulfatase (AS) and a phosphonate monoester hydrolase (PMH). Statistical analysis suggests that no one physicochemical characteristic alone explains the mutational effects. Instead, these effects appear to be dominated by their structural context. Likewise, the effect of changing the catalytic nucleophile itself is not reaction‐type‐specific. Mapping of “fitness landscapes” of four activities onto the possible variation of a chosen sequence position revealed tremendous potential for respecialization of AP superfamily members through single‐point mutations, highlighting catalytic promiscuity as a powerful predictor of adaptive potential.

We present a new microfluidic platform for the study of enzymtatic reactions using static droplets on demand. This allows us to monitor both fast and slow reactions with the same device and minute amounts of reagents. The droplets are produced and displaced using confinement gradients, which allows the experiments to be performed without having any mean flow of the external phase. Our device is used to produce six different pairs of drops, which are placed side by side in the same microfluidic chamber. A laser pulse is then used to trigger the fusion of each pair, thus initiating a chemcial reaction. Imaging is used to monitor the time evolution of enzymatic reactions. In the case of slow reactions, the reagents are completely mixed before any reaction is detected. This allows us to use standard Michaelis-Menten theory to analyze the time evolution. In the case of fast reactions, the time evolution takes place through a reaction-diffusion process, for which we develop a model that incorporates enzymatic reactions in the reaction terms. The theoretical predictions from this model are then compared to experiments in order to provide measurements of the chemical kinetics. The approach of producing droplets through confinement gradients and analyzing reactions within stationary drops provides an ultralow consumption platform. The physical principles are simple and robust, which suggests that the platform can be automated to reach large throughput analyses of enzymes.

No abstract