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A curated list of targeted optimized promiscuous ketoreductases (TOP-K)
Open accessPublished version of a preprint
Abstract
Enzymes are either specific or promiscuous catalysts in nature. The latter is portrayed by protein families like CYP450Es, Aldo-ketoreductases, and Short/Medium-chain dehydrogenases which participate in detoxification or secondary metabolite production. However, enzymes are evolutionarily ‘blind’ to an ever-increasing synthetic substrate library. Industries and labs have circumvented this by high-throughput screening or site-specific engineering to synthesize the product of interest. However, this paradigm entails cost and time-intensive one-enzyme, one-substrate catalysis model. One of the superfamilies regularly used for chiral alcohol synthesis are Short-chain dehydrogenases/reductases (SDRs). Our objective is to determine a superset of promiscuous SDRs that can catalyze multiple ketones. They are typically classified into shorter 'Classical' and longer 'Extended' type ketoreductases. However, current analysis of modelled SDRs reveals a length-independent conserved N-terminus Rossmann-fold and a variable substrate binding C-terminus substrate-binding region for both categories. The latter is recognized to influence the enzyme's flexibility and substrate promiscuity and we hypothesize these properties are directly linked with each other. We tested this by catalyzing ketone-intermediates with the essential and specific enzyme: FabG_E, as well as non-essential SDRs such as UcpA and IdnO. The experimental results confirmed this biochemical-biophysical association, making it an interesting filter for ascertaining promiscuous enzymes. Hence, we created a dataset of physicochemical properties derived from the protein sequences and employed machine-learning algorithms to examine potential candidates. This resulted in 24 targeted optimized ketoreductases (TOP-K) from 81,014 members. The experimental validation of select TOP-Ks demonstrated the correlation between the C-terminal lid-loop structure, enzyme flexibility and turnover rate on pro-pharmaceutical substrates.
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Cites
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Identification and Application of Novel Patulin-Degrading Enzymes from Bacillus subtilis 168
Niu, J., Zhu, H., Shen, J., Ma, B., Chi, H., Lu, Z., Lü, F., & Ping, Z. (2024) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c06999 · Cited by 9
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Self-citationHou, X., Gu, J., Chen, J., Ullah, H., Mamtimin, T., Wang, X., Li, X., Jiang, Y., Fan, J., Han, H., et al. (2025) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2025.138486 · Cited by 3
Metadata
- Authors
- Shanbhag, A. P., Rajagopal, S., Ghatak, A., Katagihallimath, N., Subramanian, R., & Datta, S.
- Affiliations
- Bugworks Research India Pvt. Ltd., Bangalore, India
- Biomoneta Research Pvt. Ltd., Bangalore, India
- Institute for Stem Cell Science and Regenerative Medicine, Bangalore, India
- Corresponding author
- Datta, Santanu
- Venue
- Biochemical Journal
- Publisher
- Portland Press Ltd.
- Published
- 2023-06-19
- Volume
- 480
- Issue
- 13
- Pages
- 975-997
- ISSN
- 0264-6021 (ISSN-L)
- 1470-8728
- OpenAlex
- W4381197802
- Cited by
- 5
- References
- 76
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 1
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Biochemical Journal (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- Department of Biotechnology, Ministry of Science and Technology, India (BT/BIP0475/13/11, BT/PR13429/BRB/10/767/)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biotechnology › Enzyme Production and Characterization
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- short-chain dehydrogenase/reductase, ketoreductase, enzyme promiscuity, substrate-binding region, enzyme flexibility, chiral alcohol synthesis, FabG, physicochemical properties, machine learning prediction, turnover rate
- MeSH terms
- Amino Acid Sequence, Catalysis, High-Throughput Screening Assays
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- Shanbhag, A. P., Rajagopal, S., Ghatak, A., Katagihallimath, N., Subramanian, R., & Datta, S. (2023). A curated list of targeted optimized promiscuous ketoreductases (TOP-K). Biochemical Journal. https://doi.org/10.1042/bcj20230051
