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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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采矿多功能烯酸乙酶:遗传学分类,结构特征和深度学习模型.

Liang Guo1, Yuxin Dong2, Deyong Zhang1

  • 1Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren University, Hangzhou, 310015, China.

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概括
此摘要是机器生成的。

研究人员使用酶相似性网络,遗传学分析和深度学习识别了多功能的铁乙酶 (FE). 这扩大了生物催化剂的工具包,用于各种合成应用.

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深度学习是一种深度学习.酶相似性网络是一种酶相似性网络.铁烯 Esterase 是一种铁烯 Esterase.分子动力学模拟的模拟.人类遗传学分析

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科学领域:

  • 生物催化剂是一种生物催化剂.
  • 酶学 是一种酶学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 酸乙酶 (FEs) 是生物过程中的重要酶.
  • 识别具有广泛基质特异性的多功能FE具有挑战性,但对应用至关重要.

研究的目的:

  • 通过综合计算方法识别新的多功能酸乙酶 (FEs).
  • 扩大用于合成应用的生物催化剂的库存.

主要方法:

  • 对来自BRENDA的2085个FE序列的酶相似性网络分析.
  • 植物遗传学分析以将酶分类与基质乱交相关联.
  • 模拟分子动力学以研究结构动力学.
  • 深度学习模型用于预测多功能FE.

主要成果:

  • 确定了FE的三个主要集群,其中集群1和3含有特征化的酶.
  • 遗传学分析揭示了酶分类和基质范围之间的联系.
  • 深度学习模型预测了分别来自集群1和3的38个和75个潜在的多功能FE,具有很高的信心.

结论:

  • 将基因和结构数据与深度学习相结合,可以有效地发现多功能FE.
  • 这项研究扩大了已知FE的多样性,并为合成提供了新的生物催化剂.