|A curated list of targeted optimized promiscuous ketoreductases (TOP-K). [Bugworks Research Pvt. Ltd., a C-CAMP Startup]
|Year of Publication
|Shanbhag AP, Rajagopal S, Ghatak A, Katagihallimath N, Subramanian R, Datta S
|2023 Jul 12
|ketoreductases, machine learning, medium-chain dehydrogenases, short-chain dehydrogenases
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 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 '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.