@article {8462,
	title = {A curated list of targeted optimized promiscuous ketoreductases (TOP-K). [Bugworks Research Pvt. Ltd., a C-CAMP Startup]},
	journal = {Biochem J},
	volume = {480},
	year = {2023},
	month = {2023 Jul 12},
	pages = {975-997},
	abstract = {<p class="rtejustify">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 {\textquoteright}blind{\textquoteright} to an ever-increasing synthetic substrate library. Industries and laboratories 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 {\textquoteright}Classical{\textquoteright} and longer {\textquoteright}Extended{\textquoteright} 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{\textquoteright}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.</p>
},
	keywords = {ketoreductases, machine learning, medium-chain dehydrogenases, short-chain dehydrogenases},
	issn = {1470-8728},
	doi = {10.1042/BCJ20230051},
	url = {https://pubmed.ncbi.nlm.nih.gov/37335080/},
	author = {Shanbhag, Anirudh P and Rajagopal, Sreenath and Ghatak, Arindam and Katagihallimath, Nainesh and Subramanian, Ramaswamy and Datta, Santanu}
}
