已知酶的排序排序作为重新设计新基质活性的起点,使用卷积神经网络重新设计新基质活性
Vikas Upadhyay1, Veda Sheersh Boorla1, Costas D Maranas1
1Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.
Metabolic engineering
|June 10, 2023
概括
EnzRank是一个卷积神经网络 (CNN),优先考虑蛋白质工程的酶,以创建新的生物合成途径. 该工具有助于设计生物燃料和生物活性分子的新路径,通过预测酶适合所需基质活动.
科学领域:
- 合成生物学 合成生物学
- 计算化学是一种计算化学.
- 酶工程是什么?酶工程是什么?
背景情况:
- 回复生物合成方法正在推动生物燃料等点分子的合成路径的设计.
- 目前的方法仅依赖于目录化的酶活性,这限制了新生产途径的发现.
- 为新型基质特异性重新设计酶是实施设计生物合成途径的关键瓶.
研究的目的:
- 开发一个计算工具EnzRank,该工具根据酶的成功蛋白质工程潜力对特定基质活动进行排名.
- 解决在新的路径设计中识别适合新生物转化酶候选者的瓶.
主要方法:
- 一个卷积神经网络 (CNN) 模型被开发并使用BRENDA数据库中的11800个已知的酶基质对进行训练.
- 阳性样本包括已知的活性酶-基质对,而负样本是通过创建不相似的基质对来生成的.
- 该模型经过训练并使用10倍持久方法进行交叉验证,使用Tanimoto相似性得分来评估基质不相似性.
主要成果:
- EnzRank在测试数据上获得了阳性酶基质对的平均恢复率为80.72%,阴性对的平均恢复率为73.08%.
- 为EnzRank开发了一个基于Web的用户界面,接受SMILES基质字符串和酶序列,用于预测酶基质活性.
- 该工具在蛋白质工程中对酶的适合性进行排名方面表现出有效性,用于新型反应.
结论:
- EnzRank通过优先考虑新反应的酶再工程候选者,为新路径设计提供了有价值的工具.
- 该方法有助于预测细胞代谢中的酶的潜在次要活动.
- 这种计算方法有助于发现生物燃料,生物可再生能源和生物活性分子的新生产途径.
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