Optimization of the Turnover in Artificial Enzymes via Directed Evolution Results in the Coupling of Protein Dynamics to Chemistry

Schafer, Joseph W.; Zoi, Ioanna; Antoniou, Dimitri; Schwartz, Steven D.

doi:10.1021/jacs.9b04515

Cited by 38 publications

(35 citation statements)

References 60 publications

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“…The molecular mechanism of the non-natural multistep retro-aldolase reaction catalyzed by a de novo enzyme, the RA95.5-5 designed by Hilvert and co-workers, 46 has been investigated by means of multiscale QM/MM methods using methodol as substrate. The results have been compared to our previous computational study on the more evolved RA95.5-8F retro-aldolase, 52 designed also by Hilvert and co-workers. 43 The obtained free energy landscape has allowed to confirm how the RA95.5-5 presents a higher overall activation free energy barrier than the RA95.5-8F (29.1 vs. 20.1 kcal mol -1 ), as well as to define the details of every of the 7 steps of the full reaction mechanism.…”

Section: Discussionmentioning

confidence: 99%

“…Since the catalytic activity of artificial enzymes are usually much lower than that of natural enzymes, 9 several studies have been recently conducted to understand the behavior of the designed RAs, thus supporting the timeliness of the present study. 52,5354 In particular, we perform a multiscale QM/MM computational study on the reaction mechanism of the RA95.5 designed by Hilvert and co-6 workers, 48,51 and compare the results with the more evolved RA95.5-8F design, 48 previously studied in our laboratory. 54 We chose a variant showing an activity clearly lower than RA95.5-8F (see Table S1 deposited in the Supporting Information), in order to confirm the mechanism but, more importantly, to compare a less evolved RA with the most efficient one.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Directed Evolution of De Novo Retro-Aldolases from QM/MM Studies

2020

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In an era in which climatic change puts at risk the planet, the study and develop of alternative green chemistry which can help and improve our life can play an essential role. In this context, the use of artificial enzymes capable of substitute traditional industrial processes by environmental friendly routes is a challenge. Unfortunately, the complete understanding of the catalytic activity and selectivity of enzymes remains still elusive, thus hampering creation and development enzymatic proteins. In this paper, the molecular mechanism of the non-natural multistep retro-aldolase reaction catalysed by a de novo biocatalyst, the RA95.5-5, has been investigated by means of multiscale QM/MM methods. The design of a retro-aldolase presents the difficulty to create an enzyme being able to stabilize several transition states, maintaining low energy barriers along the overall reaction. The obtained QM/MM free energy landscape has allowed defining the rate determining step corresponding to the carbon-carbon bond scission of the substrate, which is in accordance with the experimental data. A deep analysis of the electrostatic interactions between the substrate and the different amino acid residues of the protein, as well as the estimation of the electrostatic potential generated on key atoms of the substrate, has been carried out for the key steps of the reaction. The results, compared with previous computational studies on the most efficient de novo retro-aldolase, the RA95.5-8F, explains the different activities achieved during the directed evolution process and provides insights for future developments of more efficient enzymes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding the Directed Evolution of De Novo Retro-Aldolases from QM/MM Studies

2020

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“…Indeed Y180 has been identified as possibly serving as the catalytic base in the highly evolved RA95.5–8F. 32 , 33 A previous site‐directed mutagenesis study 9 (Table S1 ) showed that mutation of either of these two tyrosine residues individually in RA95.5–8F had a relatively modest effect on k cat / K M , while simultaneous mutation of both tyrosine residues resulted in substantially reduced activity (200‐fold loss in catalytic efficiency for the double mutant Y51F/Y180F). Note that Y51, K83, and Y180 are the three top residues in the POOL rankings for RA95.5–8F.…”

Section: Discussionmentioning

confidence: 99%

Analysis of electrostatic coupling throughout the laboratory evolution of a designed retroaldolase

et al. 2021

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The roles of local interactions in the laboratory evolution of a highly active, computationally designed retroaldolase (RA) are examined. Partial Order Optimum Likelihood (POOL) is used to identify catalytically important amino acid interactions in several RA95 enzyme variants. The series RA95.5, RA95.5-5, RA95.5-8, and RA95.5-8F, representing progress along an evolutionary trajectory with increasing activity, is examined. Computed measures of coupling between charged states of residues show that, as evolution proceeds and higher activities are achieved, electrostatic coupling between the biochemically active amino acids and other residues is increased. In silico residue scanning suggests multiple coupling partners for the catalytic lysine K83. The effects of two predicted partners, Y51 and E85, are tested using site-directed mutagenesis and kinetic analysis of the variants Y51F and E85Q. The Y51F variants show decreases in k cat relative to wild type, with the greatest losses observed for the more evolved constructs; they also exhibit significant decreases in k cat /K M across the series. Only modest decreases in k cat /K M are observed for the E85Q variants with little effect on k cat . Computed metrics of the degree of coupling between protonation states rise significantly as evolution proceeds and catalytic turnover rate increases. Specifically, the charge state of the catalytic lysine K83 becomes more strongly coupled to those of other amino acids as the enzyme evolves to a better catalyst.

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“… 23 Finally, further computational modeling identified fast time scale motions that were present only in the most catalytically efficient evolved variant of the de novo RA. 168 The change in conformational ensemble during directed evolution thus occurs as an unintentional but essential consequence of the mutations introduced during laboratory evolution.…”

Section: Enzyme Engineering By Fine-tuning Protein Conformational Dynmentioning

confidence: 99%

Harnessing Conformational Plasticity to Generate Designer Enzymes

2020

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Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of designer biocatalysts.

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Optimization of the Turnover in Artificial Enzymes via Directed Evolution Results in the Coupling of Protein Dynamics to Chemistry

Cited by 38 publications

References 60 publications

Understanding the Directed Evolution of De Novo Retro-Aldolases from QM/MM Studies

Understanding the Directed Evolution of De Novo Retro-Aldolases from QM/MM Studies

Analysis of electrostatic coupling throughout the laboratory evolution of a designed retroaldolase

Harnessing Conformational Plasticity to Generate Designer Enzymes

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