在机器学习的指导下,从环境元基因组中发现热友碳酸无水酶
Sornpornpun Pairoh1, Wuttichai Mhuantong1, Katewadee Boonyapakron1
1Enzyme Technology Research Team, Biorefinery Technology and Bioproduct Research Group, National Center for Genetic Engineering and Biotechnology, Khlong Luang, 12120, Pathum Thani, Thailand.
Scientific reports
|November 20, 2025
概括
机器学习识别了从温泉中获取热稳定的碳酸无水化物 (CA) 用于碳捕获. 这些酶具有很高的热稳定性,为工业二氧化碳利用和储存应用提供了潜力.
科学领域:
- 生物催化剂是一种生物催化剂.
- 酵素工程是什么意思 酵素工程
- 转基因组学是指转基因组学.
背景情况:
- 热性碳酸无水化物 (CA) 对于碳捕获,利用和储存 (CCUS) 是至关重要的,因为它们的高温稳定性和效率.
- 从极端环境中发现新型热稳定的CA对于推进CCUS技术至关重要.
研究的目的:
- 开发和应用一种以机器学习 (ML) 为指导的方法,从元基因组数据中识别热稳定的玛级碳酸无水酶 (γ-CA).
- 选和验证具有工业二氧化碳捕获应用潜力的新型γ-CA.
主要方法:
- 利用两个蛋白质描述器集 (二组成和AAindex) 来训练14个ML分类模型.
- 评估了ML模型的性能,AdaBoost在DPC和LightGBM在AAindex功能方面表现出色.
- 使用优化模型选了1534个预测的CA,并通过表达,净化和生物化学分析验证了三个高可信度候选者 (TtCA,CrCA,ToCA).
主要成果:
- 确定了三种具有高化温度 (97.0°C109.1°C) 的新型耐热 γ-CA (TtCA,CrCA,ToCA).
- 对于验证的候选物来说,已确认碳酸无水酶活性和三聚体结构.
- 证明了ML模型在区分热友和非热友蛋白质方面的有效性.
结论:
- 以ML为指导的元基因组方法有效地发现了来自CCUS极端环境的强大的酶.
- 鉴定的γ-CA具有适合工业二氧化碳捕获的热强度,尽管与α类CA相比,其水化活性适度较低.
- 这项研究为生物技术应用在极端环境中发现和验证酶提供了一个可扩展的策略.
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