The Crucial Role of Methodology Development in Directed Evolution of Selective Enzymes.

By G. Qu, Aitao Li, C. Acevedo‐Rocha, Zhoutong Sun, M. Reetz. Angewandte Chemie, 2020

360 citations recorded by the source corpus.

Directed evolution of stereo-, regio- and chemoselective enzymes provides a means to generate biocatalysts for synthetically interesting transformations. In order for this protein engineering technique to be efficient, fast and reliable, and of relevance to organic chemistry, methodology development was and still is necessary. Following a description of early contributions, this review focuses on recent developments originating from various groups. It includes optimization of molecular biological methods for gene mutagenesis and the design of efficient strategies for their application, resulting in notable reduction of the screening effort (bottleneck of directed evolution). When aiming for laboratory evolution of selectivity and activity, the newest versions of Combinatorial Active-Site Saturation Test (CAST) and Iterative Saturation Mutagenesis (ISM) have emerged as preferred approaches, flanked by in silico methods such as machine learning. On-chip solid-phase chemical gene synthesis for rapid library construction enhances library quality notably, the future of directed evolution?

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f5e00d26df4cae189920d9927df66308ec872ce4

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In Molecular Biology

1. Jirí Damborský
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3. Stanislav Mazurenko

Citations in the corpus

• Machine Learning in Enzyme Engineering (2019, background): “chemical and pharmaceutical biosynthesis, regenerative medicine, food production, waste biodegradation and biosensing (1-4).”