用机器和深度学习来预测动力学未表征的酶的周转率数.
Alexander Kroll1, Yvan Rousset1, Xiao-Pan Hu1
1Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany.
Nature communications
|July 12, 2023
概括
我们开发了TurNuP,这是一种新的计算模型,可以准确地预测不同生物体的酶效率 (kcat). 该工具通过提供可靠的酶周转数估计,增强了我们对细胞生理学和资源分配的理解.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 以周转率 (kcat) 衡量的酶效率对于细胞生理学和资源分配至关重要.
- 对大多数酶来说,实验性 kcat 数据很少,因此需要精确的计算预测方法.
- 现有的机器学习模型缺乏生物体的独立性,并与与训练数据不同的酶作斗争.
研究的目的:
- 开发一种通用且独立于生物体的计算模型,用于预测酶周转数 (kcat).
- 为了提高野生类型酶 kcat 预测的准确性和通用性.
- 为更好地了解细胞资源分配和代谢建模提供一个工具.
主要方法:
- 利用差异反应指纹来表示化学反应.
- 采用修改过的变压器网络模型来表示蛋白质序列.
- 在自然酶反应的各种数据集上训练并验证了TurNuP模型.
主要成果:
- 与现有模型相比,TurNuP表现出优越的性能.
- 该模型表现出强大的概括能力,即使对于与训练集相似度较低的酶.
- 将TurNuP预测的kcat值纳入代谢模型可以改善蛋白质分配预测.
结论:
- TurNuP提供了一个强大的和广泛适用的解决方案,用于预测酶周转数.
- 该模型推进了分子生物化学和细胞生理学的研究.
- 已经开发了一个Web服务器,以促进在研究中使用TurNuP.
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