Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes

Marshall, Liam R.; Bhattacharya, Sagar; Korendovych, Ivan V.

doi:10.1021/jacsau.3c00315

Cited by 4 publications

(2 citation statements)

References 83 publications

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“…15 The B-factor and free energy analysis using MD simulation allows rapid identification of residues that obstruct substrate entry into the protein pocket as well as investigation of the forces exerted by residues within the pocket on the substrate. 34,39,40 Introduction of mutations R47I and R47L in P450BM3 can increase hydrophobicity and expand the channel, thereby facilitating better access of m-alkylphenol to the heme center. 28 Consistent with the findings of this study, P25A-P329A-E435D of VD-BM3 and A328G-L437G-L439G of FA11a1-BM3 can create larger, more accommodating channel surfaces that facilitate the access of more steroids to the heme center.…”

Section: Discussionmentioning

confidence: 99%

Simplification of Corticosteroids Biosynthetic Pathway by Engineering P450BM3

Chen,

Chao,

Wang

et al. 2024

ACS Catal.

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Synthesis of corticosteroids, particularly hydrocortisone, is challenging owing to the complex network requiring pairing of cytochrome P450s with cytochrome P450 reductase (CPR) for achieving regionally selective hydroxylation modifications at multiple sites. Herein, we engineered a self-sufficient P450BM3 (CYP102A1 from Bacillus megaterium) for effectively reducing the traditionally complex, multienzyme cascade process (three steps and six enzymes) of hydrocortisone synthesis from progesterone (PG) to a simplified two-step process involving at least two enzymes. Driven by computational simulationguided substrate access channel and heme center pocket engineering, a series of P450BM3 variants were gradually designed with the ability to catalyze C16β, C17α, C21, and C17α/21 oxidation of PG and C11α oxidation of cortexolone (c). Subsequently, molecular dynamics simulations with an oxy-ferrous model of P450BM3 variants revealed that the glycine mutations of residues that are repulsive to the substrate allow for more stable exposure of the substrate above Fe�O. Finally, the developed P450 variants were employed to construct efficient Escherichia coli catalytic systems, which further achieved 11α/β-hydrocortisone (f/e) production in one pot from 1 g/L PG at a molar conversion rate of 81 and 84% (912 and 955 mg/L), respectively. Thus, this study provides feasible strategies for simplifying the biosynthetic steps and biocatalysts for steroidal pharmaceutical production.

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Section: Discussionmentioning

confidence: 99%

Simplification of Corticosteroids Biosynthetic Pathway by Engineering P450BM3

Chen,

Chao,

Wang

et al. 2024

ACS Catal.

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“…5 The inherent bottleneck of this approach is the cost associated with the production and evaluation of the of the huge mutant library. 6 Therefore, it is advantageous to carefully select the mutants to be tested. Machine learning has recently gained traction as an in silico tool to predict the expected effect of mutations based on known in vitro assessed mutants.…”

Section: Introductionmentioning

confidence: 99%

TransMEP: Transfer learning on large protein language models to predict mutation effects of proteins from a small known dataset

Hoffbauer,

Strodel

2024

Preprint

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Machine learning-guided optimization has become a driving force for recent improvements in protein engineering. In addition, new protein language models are learning the grammar of evolutionarily occurring sequences at large scales. This work combines both approaches to make predictions about mutational effects that support protein engineering. To this end, an easy-to-use software tool called TransMEP is developed using transfer learning by feature extraction with Gaussian process regression. A large collection of datasets is used to evaluate its quality, which scales with the size of the training set, and to show its improvements over previous fine-tuning approaches. Wet-lab studies are simulated to evaluate the use of mutation effect prediction models for protein engineering. This showed that TransMEP finds the best performing mutants with a limited study budget by considering the trade-off between exploration and exploitation.

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Lernen aus Protein‐Engineering durch Dekonvolution von Multi‐Mutationalen Enzymvarianten**

Hollmann,

Sanchis,

Reetz

2024

Angewandte Chemie

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This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. As part of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi‐mutational variants was established and refined during the past 15 years. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. Later, this phenomenon was shown to be general. Molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non‐additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi‐dimensional rugged fitness pathway landscapes, which provide more mechanistic insight than traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so‐called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non‐additive effects.

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Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes

Cited by 4 publications

References 83 publications

Simplification of Corticosteroids Biosynthetic Pathway by Engineering P450BM3

Simplification of Corticosteroids Biosynthetic Pathway by Engineering P450BM3

TransMEP: Transfer learning on large protein language models to predict mutation effects of proteins from a small known dataset

Lernen aus Protein‐Engineering durch Dekonvolution von Multi‐Mutationalen Enzymvarianten**

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