Back

Pairing two growth-based, high-throughput selections to fine tune conformational dynamics in oxygenase engineering

Maxel, S.; Zhang, L.; King, E.; Aspacio, D.; Acosta, A. P.; Luo, R.; Li, H.

2020-05-26 bioengineering
10.1101/2020.05.22.111575 bioRxiv
Show abstract

Cyclohexanone monooxygenases (CHMO) consume molecular oxygen and NADPH to catalyze the valuable oxidation of cyclic ketones. However, CHMO usage is restricted by poor thermostability and stringent specificity for NADPH. Efforts to engineer CHMO have been limited by the sensitivity of the enzyme to perturbations in conformational dynamics and long-range interactions that cannot be predicted. We demonstrate a pair of aerobic, high-throughput growth selection platforms in Escherichia coli for oxygenase evolution, based on NADPH or NADH redox balance. We utilize the NADPH-dependent selection in the directed evolution of thermostable CHMO and discover the variant CHMO GV (A245G-A288V) with a 2.7-fold improvement in residual activity compared to the wild type after 40 {degrees}C incubation. Addition of a previously reported mutation resulted in A245G-A288V-T415C which has further improved thermostability at 45 {degrees}C. We apply the NADH-dependent selection to alter the cofactor specificity of CHMO to accept NADH, a less expensive cofactor than NADPH. We identified the variant CHMO DTNP (S208D-K326T-K349N-L143P) with a 21-fold cofactor specificity switch from NADPH to NADH compared to the wild type. Molecular modeling indicates that CHMO GV experiences more favorable residue packing and backbone torsions, and CHMO DTNP activity is driven by cooperative fine-tuning of cofactor contacts. Our introduced tools for oxygenase evolution enable the rapid engineering of properties critical to industrial scalability.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Angewandte Chemie International Edition
81 papers in training set
Top 0.1%
18.3%
2
ACS Catalysis
16 papers in training set
Top 0.1%
14.5%
3
Nature Communications
4913 papers in training set
Top 11%
14.1%
4
Cell Systems
167 papers in training set
Top 2%
6.2%
50% of probability mass above
5
Journal of the American Chemical Society
199 papers in training set
Top 2%
3.8%
6
Cell Chemical Biology
81 papers in training set
Top 0.9%
3.0%
7
Advanced Science
249 papers in training set
Top 7%
2.8%
8
Cell Reports
1338 papers in training set
Top 19%
2.4%
9
eLife
5422 papers in training set
Top 36%
2.0%
10
Nucleic Acids Research
1128 papers in training set
Top 10%
1.8%
11
Nature Chemical Biology
104 papers in training set
Top 2%
1.8%
12
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 33%
1.7%
13
ACS Synthetic Biology
256 papers in training set
Top 2%
1.7%
14
Metabolic Engineering
68 papers in training set
Top 0.4%
1.7%
15
Science Advances
1098 papers in training set
Top 20%
1.5%
16
Cell Genomics
162 papers in training set
Top 4%
1.5%
17
Science
429 papers in training set
Top 16%
1.3%
18
Computational and Structural Biotechnology Journal
216 papers in training set
Top 7%
0.9%
19
Proteins: Structure, Function, and Bioinformatics
82 papers in training set
Top 0.8%
0.9%
20
Biochemistry
130 papers in training set
Top 2%
0.9%
21
iScience
1063 papers in training set
Top 30%
0.8%
22
Nature Machine Intelligence
61 papers in training set
Top 4%
0.7%
23
PLOS Computational Biology
1633 papers in training set
Top 25%
0.7%
24
Chemical Science
71 papers in training set
Top 2%
0.7%
25
Communications Biology
886 papers in training set
Top 30%
0.6%
26
Protein & Cell
25 papers in training set
Top 3%
0.6%
27
Nature Chemistry
34 papers in training set
Top 1%
0.6%
28
Scientific Reports
3102 papers in training set
Top 79%
0.6%
29
Cell Reports Physical Science
18 papers in training set
Top 1%
0.6%