IECata:可解释的双线性注意网络和证据深度学习改善了酶的催化效率预测
Jingjing Wang1, Yanpeng Zhao2,3, Zhijiang Yang1
1State Key Laboratory of NBC Protection for Civilian, No. 37, South Central Street, Changping District, Beijing 102205, China.
Briefings in bioinformatics
|June 23, 2025
概括
IECata通过深度学习与不确定性估计和可解释性来提高酶催化效率 (kcat/Km) 的预测. 这种方法提高了酶选的准确性,并加速了定向的酶进化.
科学领域:
- 生物化学和生物信息学
- 计算生物学 计算生物学
- 酶工程是什么? 酶工程是什么?
背景情况:
- 酶催化效率 (kcat/Km) 对于识别高活性酶至关重要.
- 深度学习模型提供了快速kcat/Km预测的潜力,但面临着有限数据的挑战,缺乏信心估计和糟糕的解释性.
研究的目的:
- 解决现有的深度学习模型对kcat/Km预测的局限性.
- 开发一个模型,提供可靠的不确定性估计和对酶催化反应的可解释的见解.
主要方法:
- 使用BRENDA和SABIO-RK数据库中的11,815 kcat/Km条目数据集开发IECata模型.
- 将证据深度学习纳入预测中的不确定性估计.
- 使用双线性注意力机制来解释关键残留物和基质原子的解释性.
主要成果:
- 与最先进的基准模型相比,IECata表现出优越的预测性能.
- 该模型为kcat/Km预测提供可靠的信心评估.
- 将不确定性纳入酶查显著增加了命中率.
结论:
- IECata为准确和可解释的kcat/Km预测提供了一个有效的解决方案.
- 该模型的不确定性估计提高了实验验证和定向酶进化的效率.
- 一个在线预测平台可用,以促进研究人员访问IECata.
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