Shanbhag Anirudh P, Rajagopal Sreenath, Ghatak Arindam, Katagihallimath Nainesh, Subramanian Ramaswamy, Datta Santanu
Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, Kolkata 700009, India.
Bugworks Research India Pvt. Ltd., C-CAMP, UAS GKVK Campus, Bangalore 560065, India.
Biochem J. 2023 Jul 12;480(13):975-997. doi: 10.1042/BCJ20230051.
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.
酶在本质上要么是特异性催化剂,要么是混杂性催化剂。后者由细胞色素P450E、醛酮还原酶和短/中链脱氢酶等蛋白质家族所代表,它们参与解毒或次生代谢产物的产生。然而,酶在进化上对不断增加的合成底物库是“盲目”的。工业界和实验室通过高通量筛选或位点特异性工程来规避这一问题,以合成感兴趣的产物。然而,这种模式需要成本高且耗时的单酶、单底物催化模型。常用于手性醇合成的超家族之一是短链脱氢酶/还原酶(SDR)。我们的目标是确定一组能够催化多种酮的混杂性SDR。它们通常分为较短的“经典”型和较长的“延伸”型酮还原酶。然而,目前对建模的SDR的分析表明,这两类酶都有一个与长度无关的保守N端罗斯曼折叠和一个可变的底物结合C端底物结合区域。后者被认为会影响酶的灵活性和底物混杂性,我们假设这些特性是直接相互关联的。我们通过用必需且特异性的酶FabG_E以及非必需的SDR如UcpA和IdnO催化酮中间体来测试这一点。实验结果证实了这种生化-生物物理关联,使其成为确定混杂性酶的一个有趣筛选指标。因此,我们创建了一个从蛋白质序列衍生的物理化学性质数据集,并使用机器学习算法来检查潜在的候选酶。这从81014个成员中产生了24个靶向优化的酮还原酶(TOP-K)。对选定的TOP-K进行的实验验证表明,C端盖环结构、酶的灵活性和对前药底物的周转率之间存在相关性。