开发基于DeepPQK和DeepQK序列的深度学习模型,以预测蛋白质 - 配体亲和力以及在酸乙酶DLfae4的定向进化中的应用
Siwei Li1, Wenqing Li1, Yuting Shao1
1Key Laboratory of Food Processing and Quality Control, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, PR China.
International journal of biological macromolecules
|March 7, 2025
概括
新的深度学习模型,DeepPQK和DeepQK,仅使用序列数据来预测蛋白质-连接体结合亲和力,克服传统基于结构的方法的局限性并指导酶进化.
科学领域:
- 计算生物学 计算生物学
- 生物化学 生物化学
- 酶工程是什么? 酶工程是什么?
背景情况:
- 蛋白质-配体结合亲和力对于酶催化是至关重要的.
- 传统的方法依赖于蛋白质晶体结构,这些结构往往很难和昂贵地获得.
- 仅从序列数据中预测结合亲和力仍然是一个挑战.
研究的目的:
- 开发新的深度学习模型,仅使用序列信息来预测蛋白质 - 配体结合亲和力.
- 克服结构依赖预测方法的局限性.
- 将这些模型应用于指导酶工程,以改善催化活性.
主要方法:
- 开发了两个基于序列的深度学习模型:DeepPQK和DeepQK.
- DeepPQK集成了使用CNNs的本地 (绑定口袋) 和全球 (蛋白质序列) 功能.
- DeepQK使用单独的蛋白质序列和带有扩张卷积的配体模块来捕捉远程相互作用.
主要成果:
- 在基准数据集上,DeepPQK和DeepQK实现了高预测准确度 (Pearson相关系数分别为0.805和0.804).
- 这些模型成功地从序列数据中学习了基础结构和位置关系.
- 设计并经过实验验证的酶变体具有显著增强的活性和催化效率,包括酶活性增加5.6倍.
结论:
- DeepPQK和DeepQK提供了一种强大的,无结构的方法来预测蛋白质 - 连接体结合亲和力.
- 这些模型可以有效地指导酶的定向进化.
- 开发的模型为酶工程和生物化学研究提供了一个新的计算工具.
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