一个精心策划的目标优化乱交缩酶 (TOP-K) 清单
Anirudh P Shanbhag1,2, Sreenath Rajagopal2, Arindam Ghatak1,3
1Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, Kolkata 700009, India.
The Biochemical journal
|June 19, 2023
概括
研究人员确定了能够催化多个子的散乱的短链脱酶/减少酶 (SDR). 机器学习确定了24个有针对性的优化缩酶 (TOP-Ks),具有工业应用的潜力,将酶灵活性与基质结合联系起来.
科学领域:
- 生物催化和酶工程 生物催化和酶工程
- 蛋白质结构与功能之间的关系.
- 机器学习在生物化学中的应用
背景情况:
- 酶虽然是特定的,但经常与合成基质库作斗争.
- 短链脱酶/还原酶 (SDR) 对性酒精合成至关重要.
- 目前的酶工程方法昂贵且耗时.
研究的目的:
- 确定一组能够催化各种基质的乱交性SDR.
- 调查SDR中酶灵活性和基质乱交之间的联系.
- 利用机器学习开发一种用于识别乱交SDR的预测模型.
主要方法:
- 在SDR中分析保存的N端罗斯曼和可变的C端基质结合区域.
- 使用特定 (FabG_E) 和非特定 (UcpA,IdnO) SDR进行实验验证.
- 开发一种机器学习模型,使用蛋白质序列的物理化学特性.
主要成果:
- 证实了C端结构,酶灵活性和乱交之间的生物化学-生物物理联系.
- 通过使用机器学习,从超过81,000名成员中识别了24个向优化缩酶 (TOP-Ks).
- 实验验证表明C端盖圈,灵活性和亲药物基质的周转率之间存在相关性.
结论:
- C终端区域是SDR灵活性和基质乱交的关键决定因素.
- 机器学习有效地预测乱交的SDR候选人,加速酶发现.
- TOP-K 证明了在工业应用中高效的多基质生物催化剂的潜力.
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