DLKcat无法预测突变物和不熟悉的酶的有意义的kcat值
Alexander Kroll1, Martin J Lercher1
1Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany.
Biology methods & protocols
|September 30, 2024
概括
DLKcat模型难以预测新型酶和突变的酶周转数 (kcat). 对于与其训练数据相同的低序列酶,它的准确性显著下降.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 酶动力学 酶动力学
背景情况:
- 酶周转数 (kcat) 是代谢建模中的一个关键参数.
- 预测新型酶和突变的kcat对于代谢工程和药物发现至关重要.
- 最近提出了DLKcat深度学习模型,用于高吞吐量kcat预测.
研究的目的:
- 批判性地评估DLKcat模型的概括性和预测准确性.
- 评估DLKcat在培训数据分发之外对酶的性能.
- 确定DLKcat对预测酶突变中的kcat变化的有用性.
主要方法:
- 对DLKcat预测与实验kcat值进行比较分析.
- 基于对训练数据的序列同一性的DLKcat性能评估.
- 评估DLKcat捕获突变诱导的kcat变异的能力.
主要成果:
- 对于与训练集序列相同性<60%的酶,DLKcat预测的准确性低于恒定的平均kcat.
- 该模型无法预测实验观察到的kcat变异在不包括在训练中的突变体中.
- 在DLKcat对新型酶家族和突变体的概括性存在重大限制.
结论:
- DLKcat在预测新型酶和突变物中的kcat方面具有有限的实用性.
- 对于不同于其训练数据的酶,模型的性能会大幅下降.
- 需要进一步开发,以提高深度学习模型的通用性和可靠性,以进行kcat预测.
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