ESM-Ezy:一种深度学习策略,用于开采具有优越性质的新型多铜氧化酶
Hui Qian1,2, Yuxuan Wang1,2, Xibin Zhou1
1School of Engineering, Westlake University, Hangzhou, 310014, Zhejiang, China.
Nature communications
|April 5, 2025
概括
我们开发了ESM-Ezy,这是一种用于发现优异酶的新策略,即使是那些序列相似性较低的酶. 这种方法成功地识别了具有强化性能的新型生物催化剂,用于工业应用.
科学领域:
- 生物技术是生物技术.
- 酶工程是什么? 酶工程是什么?
- 生物信息学是一种生物信息学.
背景情况:
- 从序列数据中预测酶功能具有挑战性,特别是对于序列相似性较低的酶.
- 发现具有卓越的催化性能和工业适用性的酶需要先进的计算工具.
研究的目的:
- 开发和验证ESM-Ezy,一种用于识别高性能生物催化剂的新型酶开采策略.
- 为了利用ESM-1b蛋白质语言模型和语义相似性来发现酶.
主要方法:
- 利用ESM-1b蛋白语言模型生成序列嵌入.
- 在语义空间中使用相似性计算来识别候选酶.
- 验证了已识别的酶的催化特性,包括多铜氧化酶 (MCOs) 和L-asparaginases.
主要成果:
- 在识别多铜氧化酶 (MCOs) 中,ESM-Ezy在至少一个催化性质上表现优于查询酶的成功率为44%.
- 51%的已识别的MCO显示了环境修复应用的潜力.
- 40%的确定的L-asparaginases与查询酶相比表现出更高的特异活性和催化效率.
结论:
- ESM-Ezy是一种有效的策略,用于发现高性能生物催化剂,特别是那些具有较低序列相似性的生物催化剂.
- 该方法加速了用于各种工业应用的酶发现,包括环境修复.
- 确定了具有增强功能性质和独特结构特征的新型MCO和L-asparaginases.
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