通过残留意识注意力机制和预训练的表示来提高kcat的预测
Yunxiang Cai1, Fengya Ge1, Chuanlei Zhang1
1College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, China.
Communications biology
|January 22, 2026
概括
一个新的深度学习框架,PMAK,通过联合建模酶和反应来改善酶动力学预测. 这种方法提高了准确性,并提供了对催化残留的洞察力,优于现有的方法.
科学领域:
- 生物化学和计算生物学
- 酶动力学和催化作用
- 在生物信息学中的机器学习.
背景情况:
- 酶动力学,特别是循环数 (kcat),对于理解酶的效率和机制至关重要.
- 目前的计算kcat预测方法由于有限的反应背景而缺乏准确性和通用性.
- 像TurNuP这样的现有模型由于其两阶段设计,在预测性能和可解释性方面存在局限性.
研究的目的:
- 开发一种新的深度学习框架,PMAK,用于准确和可解释的kcat预测.
- 将酶序列和反应信息整合到一个统一的模型中.
- 为了捕获酶反应相互作用并识别关键的催化残留物.
主要方法:
- 开发了PMAK,这是一个深度学习框架,使用预训练的酶序列表示和反应SMILES.
- 纳入了残留感知注意机制,以模拟酶反应相互作用.
- 使用五倍交叉验证对PMAK与现有方法的性能进行了评估.
主要成果:
- 在kcat预测方面,PMAK显著优于现有的方法.
- 与TurNuP相比,获得了16.9% (新反应) 和10.0% (新酶) 的平均R2改善.
- 证明PMAK能够提供对催化重要残留物的可解释性见解的能力.
结论:
- PMAK为酶动力学预测提供了一种强大而准确的方法.
- 该框架通过突出重点残留物增强对酶催化物的理解.
- PMAK代表了计算酶动力学研究和相关应用的重大进步.
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