Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production.
Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production.
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DOI：
10.1038/s41467-021-25831-w
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发表时间：
2021-10-05
影响因子：
16.6
通讯作者：
Romero PA
中科院分区：
文献类型：
--
作者：
Greenhalgh JC;Fahlberg SA;Pfleger BF;Romero PA
形成醇的脂肪酸酰还原酶（FARs）催化硫酯还原为醇，是微生物生产脂肪醇的关键酶。许多代谢工程策略利用FARs从细胞内酰基辅酶a和酰基acp池中产生脂肪醇；然而，酶活性，特别是酰基acps的活性，仍然是高通量生产的重大瓶颈。在这里，我们通过实施机器学习（ML）驱动的方法来迭代搜索蛋白质适应度景观，设计了对酰基- acp底物具有增强活性的FARs。经过十轮设计-测试-学习的过程，我们设计的酶产生的脂肪醇比开始的自然序列多两倍以上。我们对顶部序列进行了表征，并表明它对棕榈酰acp具有增强的催化速率。最后，我们分析了序列功能数据，以确定与体内活性相关的特征，如底物结合位点附近的净电荷。这项工作证明了机器学习在导航传统上难以工程的蛋白质的适应性方面的力量。脂肪酸酰基还原酶（FARs）是脂肪醇生物合成中的关键酶，具有直接接近酰基acp底物的能力。在这里，作者将基于机器学习的蛋白质工程框架与基因重组相结合，以优化FAR对酰基acp的活性，并提高脂肪醇的产量。
Alcohol-forming fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for microbial production of fatty alcohols. Many metabolic engineering strategies utilize FARs to produce fatty alcohols from intracellular acyl-CoA and acyl-ACP pools; however, enzyme activity, especially on acyl-ACPs, remains a significant bottleneck to high-flux production. Here, we engineer FARs with enhanced activity on acyl-ACP substrates by implementing a machine learning (ML)-driven approach to iteratively search the protein fitness landscape. Over the course of ten design-test-learn rounds, we engineer enzymes that produce over twofold more fatty alcohols than the starting natural sequences. We characterize the top sequence and show that it has an enhanced catalytic rate on palmitoyl-ACP. Finally, we analyze the sequence-function data to identify features, like the net charge near the substrate-binding site, that correlate with in vivo activity. This work demonstrates the power of ML to navigate the fitness landscape of traditionally difficult-to-engineer proteins. Fatty acyl reductases (FARs) are critical enzymes in the biosynthesis of fatty alcohols and have the ability to directly acces acyl-ACP substrates. Here, authors couple machine learning-based protein engineering framework with gene shuffling to optimize a FAR for the activity on acyl-ACP and improve fatty alcohol production.
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影响因子：
4.3
作者：
Bedbrook CN;Yang KK;Rice AJ;Gradinaru V;Arnold FH
通讯作者：
Arnold FH
影响因子：
4.7
作者：
Hernández Lozada NJ;Lai RY;Simmons TR;Thomas KA;Chowdhury R;Maranas CD;Pfleger BF
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Pfleger BF
使用机器学习和合成基因进行工程蛋白酶K。
影响因子：
3.5
作者：
Liao, Jun;Warmuth, Manfred K.;Govindarajan, Sridhar;Ness, Jon E.;Wang, Rebecca P.;Gustafsson, Claes;Minshull, Jeremy
通讯作者：
Minshull, Jeremy
影响因子：
46.9
作者：
Fox, Richard J.;Davis, S. Christopher;Huisman, Gjalt W.
通讯作者：
Huisman, Gjalt W.
影响因子：
8.4
作者：
Mehrer CR;Incha MR;Politz MC;Pfleger BF
通讯作者：
Pfleger BF