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

By A. Currin, Neil Swainston, P. Day, Douglas B. Kell. 2014

317 citations recorded by the source corpus.

Improving enzymes by directed evolution requires the navigation of very large search spaces; we survey how to do this intelligently.

Subfields: Genetics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Debora Marks
2. Jian Peng
3. Christopher Sander
4. Huimin Zhao
5. Thomas A. Hopf
6. Yang Liu

In Molecular Biology

1. Debora Marks
2. Christopher Sander
3. Jian Peng
4. Thomas A. Hopf
5. John Ingraham
6. Yang Liu
7. Jinbo Xu
8. Charlotta P I Schärfe

Citations in the corpus

• Evolutionary context-integrated deep sequence modeling for protein engineering (2020, background): “However, the protein sequence space is tremendously large, and thus an exhaustive exploration or enumeration of the space is intractable in the laboratory and computationally when the length of the protein sequence is longer than 50 residues ​[1] ​.”
• Mutation effects predicted from sequence co-variation (2015, background): “25; (5) conserved/variable: column conservation of position in alignment > 0.”
• A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters (2018, unknown): “20–22), whereas QuasAr2 and Archer1 showed aggregation in cell bodies and processes (Supplementary Fig.”