coli but did not modify PTE’s enzymatic activities 24. These mutations improved the levels of soluble and functional enzyme in E. PTE-S5 carried three mutations (Lys185Arg, Asp208Gly and Arg319Ser) compared with the wild-type PTE. A PTE variant with higher expression in Escherichia coli, dubbed PTE-S5, was previously evolved via random mutagenesis and screening with 2-naphthyl acetate 24. The early steps of PTE’s evolution into an arylesterase have been described previously 24, 25, 26. By producing and characterizing this evolutionary trajectory, we provide a quantitative description of the magnitude of diminishing returns and tradeoffs throughout this trajectory, and also describe the molecular basis of diminishing returns and tradeoffs. We isolated and characterized the intermediates of this process, and could thus describe the entire trajectory. Iterations of random mutagenesis and selection for variants exhibiting higher arylesterase rates led to the gradual fixation of 18 substitutions, and a highly efficient arylesterase ( k cat/ K M=1.7 × 10 7 M −1 s −1). The laboratory evolution began from the weak, promiscuous, arylesterase activity of wild-type PTE ( k cat/ K M with 2NH=4.2 × 10 2 M −1 s −1). Although the original (PTE) and new (arylesterase) enzymes catalyse hydrolysis, the reactions differ in the bond that is hydrolysed (P–O versus C–O), the shape and binding orientation of the substrates, and the geometry of the substrates (tetrahedral versus planar) and transition states (TSs) (pentavalent versus tetrahedral Fig. Here, we describe a complete evolutionary trajectory comprising a functional transition from a naturally occurring phosphotriesterase (PTE) from Pseudomonas diminuta that catalyses the hydrolysis of the pesticide paraoxon with high efficiency ( k cat/ K M=2.2 × 10 7 M −1 s −1) 22, 23, to an arylesterase that catalyses the hydrolysis of 2-naphthyl hexanoate (2NH Fig. Although these experiments provided novel insight, they involved both random and rational (targeted) mutagenesis and/or varying substrates along the trajectory 21. Marked switches in enzymatic functions have been achieved via laboratory evolution, whereby the catalytic efficiency of the newly evolved enzyme matched that of the starting point 20. Such experiments have shown how new functional traits evolve, that is, by mutations increasing a latent, weak, promiscuous activity 11, 16, 17, 18, 19. Laboratory evolution (also dubbed experimental or directed evolution) can allow us to characterize every intermediate in a trajectory and thereby help us to understand the mechanisms and driving forces of evolution 13, 14, 15. In addition, there is no way to reproduce the series of evolutionary intermediates that underlie the divergence of natural enzymes. The starting points of natural enzymes remain mostly unknown. Overall, data and insights regarding complete functional transitions in enzymes are scarce. Finally, the potential implications of diminishing returns and tradeoffs on the existing repertoire of biomolecules, and of enzymes in particular, also remain unidentified. Our understanding of the molecular basis for diminishing returns and tradeoffs is also limited. However, exactly how diminishing returns and tradeoffs affect a complete evolutionary trajectory involving the emergence of a new catalytic activity remains unknown. Similarly, functional tradeoffs have been studied in the context of protein evolution 3, 10, 11, 12, yet previous works have focused on the early steps of optimization. For instance, it has been shown that the first mutations have the largest impact, and diminishing returns have been observed with respect to metabolic pathways 5, bacterial evolution 6, population sizes 7, mutation rates 8 and the evolution of antibiotic resistance 9. These aspects of evolutionary optimization have been addressed 3, 4, yet our understanding of the processes that define evolutionary optimization is by no means complete. Tradeoffs: the improvement of the trait under optimization comes at the expense of other traits. Diminishing returns: the first steps yield larger improvements than the later steps 1, 2, meaning that the closer the process is to its optimum, the slower and less cost-effective optimization becomes. Graduality: each step provides an incremental improvement. Optimization processes, be they industrial, economical, computational or biological (evolutionary), share several common features.
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