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Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently.

ABSTRACT: The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

SUBMITTER: Currin A

PROVIDER: S-EPMC4349129 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently.

Currin Andrew A Swainston Neil N Day Philip J PJ Kell Douglas B DB

Chemical Society reviews 20150301 5

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. Ho ...[more]

PMID: 25503938

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Project description:Despite the astonishing diversity of naturally occurring biocatalytic processes, enzymes do not catalyze many of the transformations favored by synthetic chemists. Either nature does not care about the specific products, or if she does, she has adopted a different synthetic strategy. In many cases, the appropriate reagents used by synthetic chemists are not readily accessible to biological systems. Here, we discuss our efforts to expand the catalytic repertoire of enzymes to encompass powerful reactions previously known only in small-molecule catalysis: formation and transfer of reactive carbene and nitrene intermediates leading to a broad range of products, including products with bonds not known in biology. In light of the structural similarity of iron carbene (Fe═C(R1)(R2)) and iron nitrene (Fe═NR) to the iron oxo (Fe═O) intermediate involved in cytochrome P450-catalyzed oxidation, we have used synthetic carbene and nitrene precursors that biological systems have not encountered and repurposed P450s to catalyze reactions that are not known in the natural world. The resulting protein catalysts are fully genetically encoded and function in intact microbial cells or cell-free lysates, where their performance can be improved and optimized by directed evolution. By leveraging the catalytic promiscuity of P450 enzymes, we evolved a range of carbene and nitrene transferases exhibiting excellent activity toward these new-to-nature reactions. Since our initial report in 2012, a number of other heme proteins including myoglobins, protoglobins, and cytochromes c have also been found and engineered to promote unnatural carbene and nitrene transfer. Due to the altered active-site environments, these heme proteins often displayed complementary activities and selectivities to P450s.Using wild-type and engineered heme proteins, we and others have described a range of selective carbene transfer reactions, including cyclopropanation, cyclopropenation, Si-H insertion, B-H insertion, and C-H insertion. Similarly, a variety of asymmetric nitrene transfer processes including aziridination, sulfide imidation, C-H amidation, and, most recently, C-H amination have been demonstrated. The scopes of these biocatalytic carbene and nitrene transfer reactions are often complementary to the state-of-the-art processes based on small-molecule transition-metal catalysts, making engineered biocatalysts a valuable addition to the synthetic chemist's toolbox. Moreover, enabled by the exquisite regio- and stereocontrol imposed by the enzyme catalyst, this biocatalytic platform provides an exciting opportunity to address challenging problems in modern synthetic chemistry and selective catalysis, including ones that have eluded synthetic chemists for decades.

Project description:MotivationShedding light on the relationships between protein sequences and functions is a challenging task with many implications in protein evolution, diseases understanding, and protein design. The protein sequence space mapping to specific functions is however hard to comprehend due to its complexity. Generative models help to decipher complex systems thanks to their abilities to learn and recreate data specificity. Applied to proteins, they can capture the sequence patterns associated with functions and point out important relationships between sequence positions. By learning these dependencies between sequences and functions, they can ultimately be used to generate new sequences and navigate through uncharted area of molecular evolution.ResultsThis study presents an Adversarial Auto-Encoder (AAE) approached, an unsupervised generative model, to generate new protein sequences. AAEs are tested on three protein families known for their multiple functions the sulfatase, the HUP and the TPP families. Clustering results on the encoded sequences from the latent space computed by AAEs display high level of homogeneity regarding the protein sequence functions. The study also reports and analyzes for the first time two sampling strategies based on latent space interpolation and latent space arithmetic to generate intermediate protein sequences sharing sequential properties of original sequences linked to known functional properties issued from different families and functions. Generated sequences by interpolation between latent space data points demonstrate the ability of the AAE to generalize and produce meaningful biological sequences from an evolutionary uncharted area of the biological sequence space. Finally, 3D structure models computed by comparative modelling using generated sequences and templates of different sub-families point out to the ability of the latent space arithmetic to successfully transfer protein sequence properties linked to function between different sub-families. All in all this study confirms the ability of deep learning frameworks to model biological complexity and bring new tools to explore amino acid sequence and functional spaces.

Dataset Information

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently.

Publications

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently.

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